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Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino by Jim McTague
algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, buy and hold, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative ﬁnance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K
., 194 arbitrage, 161-162 arbitrage opportunities, 70 Archipelago, 33 Arnuk, Sal, 11-25, 44-45, 61, 217 Atkins, Paul, 145-146 ATSs (Alternative Trading Systems), 93 regulation of, 139-144 Australia (film), 78 automated liquidity provision, 161 Automatic Trading Systems (ATSs), 93, 139-144 Aykroyd, Dan, 29 B Bachus, Spencer, 187 Baker, Jim, 130 BATS (Better Alternative Trading System) Exchange, 78 Bee, Samantha, 44 Berkeley, Alfred, 173 Biden, Joe, 47-49, 52-53 “Big Picture” blog (Ritholtz), 234 Birk, Roger, 116 Black Monday (October 19, 1987), 125-133, 179 Blair, Bruce, 160 Blankfein, Lloyd, 99 Blodgett, Henry, 189-190 Bloomberg, Michael, 100 Boesky, Ivan, 126 Boggs, Caleb, 48 Bookstaber, Richard, 157 Born, Brooksley, 99 Boston Stock Exchange, 33 BP oil spill (Deepwater Horizon), 67 Brady Commission, 128 Brady, Nicholas, 83, 128 broken trades after Flash Crash, 83, 225 brokerage houses, internal trades, 31-32 Brown, Alistair, 17 Brown, Gordon, 66 Budge, Hamer, 107 Buffett, Warren, 234 Bulgaria Confidential (newspaper), 42 Bush, George H.W., 102 Bush, George W., 50 busted trades after Flash Crash, 88 buttonwood trees, 168 C Cameron, David, 66 Canaday, Ed, 41 capital crisis of 1969-70, 105-111 Casey, William, 120 CBOT (Chicago Board of Trade), 28-30 Cembalest, Michael, 207-208 CFTC (Commodities Futures Trading Commission), 27 Flash Crash report, 213-227 immediate reaction to Flash Crash, 82 investigation of Flash Crash, 183, 187 consolidated tape delays, 202-204 quick fix rules after Flash Crash, 85-87, 90 CFTC-SEC Joint Advisory Committee Accenture testifying before, 85-90 investigation of Flash Crash, 91-95 Chicago Board of Trade (CBOT), 28-30 Chicago Mercantile Exchange (CME), 28-30 Chilton, Bart, 214 Christie, William, 139 circuit breaker rule, 188 circuit breakers, 63-64, 89 Citigroup, 166 Clinton, Bill, 53, 97, 100-102, 143 Clinton, Hillary, 100 Close Encounters of the Third Kind (film), 49 CME (Chicago Mercantile Exchange), 28-30 collocated servers, 17, 22, 34 collocation, origins of, 165-169 commission structure, fixed commissions, 118-120 commodities exchanges correlation with equities exchanges, 94 history in United States, 27-30 unification with equities exchanges, 36, 70 Commodities Futures Trading Commission.
See Ivandjiiski, Dan, 41-42 E E-Mini futures contracts in Flash Crash, 68-69, 215-219 ECNs (electronic communications networks), 94, 142 Edelman, Asher, 136 Engelberg, Jeff, 191 Equinix, 167 equities exchanges correlation with commodities exchanges, 94 Flash Crash, details of, 61-79 Regulation NMS changes to, 21, 31-37 unification with commodities exchanges, 36, 70 erosion of investor confidence, 207-210 ETFs (exchange-traded funds), 185 in Flash Crash, 76-77 mutual funds versus, 232 ethics issues in Flash Crash investigation, 193-198 Eurex, 30 Euronext N.V., 33 European commodities exchanges, modernization of, 29 event trading, 161 exchange-traded funds (ETFs), 185 in Flash Crash, 76-77 mutual funds versus, 232 exchanges collocation, origins of, 165-169 commodities exchanges, history in United States, 27-30 equities exchanges, Regulation NMS changes to, 21, 31-37 Flash Crash, details of, 61-79 individual stock circuit breakers, 89 integration, lack of, 128 intraday moves after Great Recession, 2-3 unification of commodities and equities exchanges, 36, 70 executing brokers, 32 exhaust, 33 F Facciponte, Joseph, 40 failed trades in capital crisis of 1969-70, 106-108 Federal Reserve, regulation of markets through, 129 FINRA (Financial Industry Regulatory Authority), 19, 41 Mary Schapiro’s leadership of, 97 Trillium Brokerage Services LLC, case against, 210-212 fixed commissions, 118-120 Flash Crash Accenture, affect on, 85-87, 90 details of, 6-8, 61-79 immediate Congressional reaction to, 81-84 investigation of, 91-95, 183-185, 189-191 consolidated tape delays, 199-205 SEC ethics issues, 193-198 precursors to, 1, 4-6 SEC and CFTC report on, 213-227 trades busted afterwards, 88 flash orders, 36, 42-43 banning, 47-59 FOIA (Freedom of Information Act), 113-114 Ford, Gerald, 120 Fox, Kevin N., 40 Frank, Barney, 55, 186 Fraud Enforcement and Recovery Act of 2009, 49 Freedom of Information Act (FOIA), 113-114 Freeman, John P., 197 front-running by hedge funds, 140 Futures Industry Association of America, 101 G Galbraith, John Kenneth, 58 Gastineau, Gary, 232 Geithner, Timothy, 81 Gensler, Gary, 86, 97-101, 216 Getco, 157, 196 Gillespie, Ed, 53 Giuliani, Rudolph, 126 Glassman, Cynthia, 146 Goldman Sachs, 39-41, 92, 99 Goldstein, Michael, 68, 186 Gorelick, Richard, 151-152, 155 Graham, Benjamin, 177 Grassley, Charles, 197 Great Britain, elections prior to Flash Crash, 66 Great Depression, volatility after, 179 Great Recession details of, 4 recovery from, 1, 4-6 Greece, debt of, 65-66 Greenspan, Alan, 98 Gulf of Mexico, BP oil spill (Deepwater Horizon), 67 H Hartzell, David, 230-231 Hathaway, Frank, 178 Hawaiian Holdings, Inc., 41 hedge funds, 159-160 front-running by, 140 Hensarling, Jeb, 187 high-frequency trading (HFT) accusation of market manipulation, 39-45 blamed by Congress for Flash Crash, 83-84 collocation, origins of, 165-169 Congressional pressure on SEC to reform, 54-59 eroded market confidence from, 207-210 explanation of, 149-164 Flash Crash consolidated tape delays, 199-205 details of, 61-79 investigation of, 91-92, 183-185, 189-191 in report, 221-227 role in, 7-8 SEC ethics issues, 193-198 origins of, 135-147 public relations efforts of, 150 Quants versus, 157-160 retail trades and, 32 statistics on, 156 strategies employed by, 11-25, 34-37, 161-162 Trillium Brokerage Services LLC, case against, 210-212 volatility reasons for, 175-182 rhythm of, 176-177 history of commodities exchanges in United States, 27-30 Hoffman, Stuart, 234 hot-potato trading, 22 Houtkin, Harvey Ira, 133-139, 161 Hu, Henry, 44, 58, 98 Hunsader, Eric Scott, 199-205, 225-226 Hunt, Ben, 163 hybrid market, 78 I ICE (Intercontinental Exchange), 30 Ichan, Carl, 136 individual stock circuit breakers, 89 initial public offerings (IPOs), 142-144 integration of exchanges, lack of, 128 Intercontinental Exchange (ICE), 30 internal trades, 31-32 internalization during Flash Crash, 73 in Flash Crash report, 222-225 intraday moves after Great Recession, 2-3 investigation of Flash Crash, 91-95, 183-185, 189-191 consolidated tape delays, 199-205 SEC ethics issues, 193-198 investor behavior, overcorrelation of, 177 investor confidence after Flash Crash, 62 erosion of, 207-210 investor recommendations, 229-234 IPOs (initial public offerings), 142-144 Ira Haupt & Co., 108 Island (ECN), 144-145 Ivandjiiski, Krassimir, 42 Ivandjiiski, Dan, 41-42 J–K Johnson, Lyndon, 114 Johnson, Simon, 186 Junger, Sebastian, 67 justifiable trades, 88 Kanjilal, Debases, 189 Kanjorski, Paul, 81-83 Kaufman, Ted, 37, 47-61, 103, 183-187, 193-198, 208-210 Kay, Bradley, 231-232 Kim, Edward, 142 King, Elizabeth, 196 Kirilenko, Andrei, 171, 201 Kotok, David, 234 Kotz, David, 197 L latency, 167 layering, 210-212 Leibowitz, Larry, 42 Lemov, Michael, 114 Levitt, Arthur, 14, 35, 50, 110, 118, 140-144 Lewis, Michael, 191 life-cycle funds, 230 LIFFE (London International Financial Futures and Options Exchange), 30 limit orders, market orders versus, 224 Lincoln, Abraham, 48 liquidity during Flash Crash, 72-73 Liquidity Replenishment Point (LPR), 78 Liquidnet, 173-174 Lo, Andrew W., 160 London International Financial Futures and Options Exchange (LIFFE), 30 Long Term Capital Management, 98, 159 Loomis, Philip A., 121 LPR (Liquidity Replenishment Point), 78 Luddites, 150 Lukken, Walt, 28 M Madoff, Bernie, 56 Malyshev, Misha, 39 market manipulation, HFT (high-frequency traders) accused of, 39-45 market orders, limit orders versus, 224 market volatility.
., 41 hedge funds, 159-160 front-running by, 140 Hensarling, Jeb, 187 high-frequency trading (HFT) accusation of market manipulation, 39-45 blamed by Congress for Flash Crash, 83-84 collocation, origins of, 165-169 Congressional pressure on SEC to reform, 54-59 eroded market confidence from, 207-210 explanation of, 149-164 Flash Crash consolidated tape delays, 199-205 details of, 61-79 investigation of, 91-92, 183-185, 189-191 in report, 221-227 role in, 7-8 SEC ethics issues, 193-198 origins of, 135-147 public relations efforts of, 150 Quants versus, 157-160 retail trades and, 32 statistics on, 156 strategies employed by, 11-25, 34-37, 161-162 Trillium Brokerage Services LLC, case against, 210-212 volatility reasons for, 175-182 rhythm of, 176-177 history of commodities exchanges in United States, 27-30 Hoffman, Stuart, 234 hot-potato trading, 22 Houtkin, Harvey Ira, 133-139, 161 Hu, Henry, 44, 58, 98 Hunsader, Eric Scott, 199-205, 225-226 Hunt, Ben, 163 hybrid market, 78 I ICE (Intercontinental Exchange), 30 Ichan, Carl, 136 individual stock circuit breakers, 89 initial public offerings (IPOs), 142-144 integration of exchanges, lack of, 128 Intercontinental Exchange (ICE), 30 internal trades, 31-32 internalization during Flash Crash, 73 in Flash Crash report, 222-225 intraday moves after Great Recession, 2-3 investigation of Flash Crash, 91-95, 183-185, 189-191 consolidated tape delays, 199-205 SEC ethics issues, 193-198 investor behavior, overcorrelation of, 177 investor confidence after Flash Crash, 62 erosion of, 207-210 investor recommendations, 229-234 IPOs (initial public offerings), 142-144 Ira Haupt & Co., 108 Island (ECN), 144-145 Ivandjiiski, Krassimir, 42 Ivandjiiski, Dan, 41-42 J–K Johnson, Lyndon, 114 Johnson, Simon, 186 Junger, Sebastian, 67 justifiable trades, 88 Kanjilal, Debases, 189 Kanjorski, Paul, 81-83 Kaufman, Ted, 37, 47-61, 103, 183-187, 193-198, 208-210 Kay, Bradley, 231-232 Kim, Edward, 142 King, Elizabeth, 196 Kirilenko, Andrei, 171, 201 Kotok, David, 234 Kotz, David, 197 L latency, 167 layering, 210-212 Leibowitz, Larry, 42 Lemov, Michael, 114 Levitt, Arthur, 14, 35, 50, 110, 118, 140-144 Lewis, Michael, 191 life-cycle funds, 230 LIFFE (London International Financial Futures and Options Exchange), 30 limit orders, market orders versus, 224 Lincoln, Abraham, 48 liquidity during Flash Crash, 72-73 Liquidity Replenishment Point (LPR), 78 Liquidnet, 173-174 Lo, Andrew W., 160 London International Financial Futures and Options Exchange (LIFFE), 30 Long Term Capital Management, 98, 159 Loomis, Philip A., 121 LPR (Liquidity Replenishment Point), 78 Luddites, 150 Lukken, Walt, 28 M Madoff, Bernie, 56 Malyshev, Misha, 39 market manipulation, HFT (high-frequency traders) accused of, 39-45 market orders, limit orders versus, 224 market volatility.
Flash Crash: A Trading Savant, a Global Manhunt, and the Most Mysterious Market Crash in History by Liam Vaughan
algorithmic trading, backtesting, bank run, barriers to entry, Bernie Madoff, Black Swan, Bob Geldof, centre right, collapse of Lehman Brothers, Donald Trump, Elliott wave, eurozone crisis, family office, Flash crash, high net worth, High speed trading, information asymmetry, Jeff Bezos, Kickstarter, margin call, market design, market microstructure, Nick Leeson, offshore financial centre, pattern recognition, Ponzi scheme, Ralph Nelson Elliott, Ronald Reagan, sovereign wealth fund, spectrum auction, Stephen Hawking, the market place, Tobin tax, tulip mania, yield curve, zero-sum game
However, all those layers of nuance were lost: “Futures Trader Charged with Illegally Manipulating Stock Market, Contributing to the May 2010 Market ‘Flash Crash,’ ” Justice Department press release, April 21, 2015. Speaking to the Wall Street Journal: Bradley Hope and Andrew Ackerman, “ ‘Flash Crash Investigators Likely Missed Clues,” Wall Street Journal, April 26, 2015. the trader’s program was switched off: Tim Cave, Juliet Samuel, and Aruna Viswanatha, “U.K. ‘Flash Crash’ Trader Navinder Sarao Fighting Extradition to U.S. Granted Bail,” Wall Street Journal, April 22, 2015. We “should have seen this”: Hope and Ackerman, “ ‘Flash Crash’ Investigators.” “Yes, Sarao’s conduct was dodgy”: Craig Pirrong, “A Matter of Magnitudes: Making Matterhorn Out of a Molehill,” Streetwise Professor, April 24, 2015, www.streetwiseprofessor.com.
By then he would be known around the world as the “Flash Crash Trader” and the “Hound of Hounslow”; the scourge of the markets or a modern-day folk hero, depending on whom you asked. That afternoon, both the DOJ and the CFTC sent out press releases announcing the arrest. They made for dramatic reading. “A futures trader was arrested in the United Kingdom today on U.S. wire fraud and commodities fraud and manipulation charges in connection with his alleged role in the May 2010 ‘Flash Crash,’ ” the DOJ statement said. Sarao “used an automated trading program to manipulate the market,” it went on, earning “significant profits” and contributing to a “major drop in the U.S. stock market.” For a multitude of reporters and finance professionals, the announcement was shocking and bizarre. For one thing, the Flash Crash—that apocalyptic half-hour spell when markets around the world collapsed before bouncing back again, and stocks temporarily changed hands at 0.0001 cents—had occurred almost five years ago.
However, all those layers of nuance were lost after the press office sent out a release with the headline “Futures Trader Charged with Illegally Manipulating Stock Market, Contributing to the May 2010 Market ‘Flash Crash.’ ” Sarao was duly dubbed the “Flash Crash Trader,” and much of the subsequent debate about his innocence or guilt disregarded the hundreds of other days he was trading. The prosecutors, busy preparing for trial, discounted the media’s fixation with the Flash Crash as an unfortunate sideshow, but the truth was, for Nav, it was profoundly important. Historically, spoofers and manipulators were handled civilly, with fines or temporary bans; so the government’s decision to charge Sarao criminally marked an escalation. He was the first alleged market manipulator ever to be extradited, and some of the counts against him carried sentences of up to twenty years in prison.
Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio by Sal Arnuk, Joseph Saluzzi
algorithmic trading, automated trading system, Bernie Madoff, buttonwood tree, buy and hold, commoditize, computerized trading, corporate governance, cuban missile crisis, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, Mark Zuckerberg, market fragmentation, Ponzi scheme, price discovery process, price mechanism, price stability, Sergey Aleynikov, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, stocks for the long run, stocks for the long term, transaction costs, two-sided market, zero-sum game
The first BATS trade was at $15.25 a share, but almost immediately market makers on NASDAQ quoted BATS at $10.98 a share, and then at $8.03, and then $4.17, and then ever lower at blazing speed. It took precisely 1.372 seconds for the price to crash all the way from $15.25 to a fraction of a penny a share, a textbook example of a mini flash crash. While BATS was being brutally flayed during its debut, not one of the SEC’s reforms instituted since the Flash Crash had any effect at all. Never before had a stock market’s own shares flash crashed to oblivion. NASDAQ market makers simply withdrew for “safety,” the same reason why Cummings said Tradebot withdrew in the Flash Crash. The episode made headlines around the world and forced BATS to rescind its IPO. Meaningful stock market reform must reverse at least some of the anarchy of the last 10 years. HFT market maker scalpers should take on material market obligations before they’re handed any market privileges.
Although the headlines of the SEC report seemed to exonerate HFT, many market observers believed there were more problems beneath the surface (see Chapters 10 and 11 for more detail on the Flash Crash). One of the biggest questions was why it took the SEC almost five months to produce a report on less than thirty minutes of trading. The SEC explained that most of the delay was because it did not have a consolidated data trail. The Commission needed to piece together what happened on May 6 from many different sources. It was apparent that the SEC was lacking the tools necessary to police the new equity market that it had created. The Band-Aid Fixes Since the Flash Crash, rather than address the entire fragmented equity market, the SEC proposed and approved a number of short-term band-aid fixes. Single Stock Circuit Breakers In June 2010, single stock circuit breakers were approved by the SEC as an initial line of defense against another flash crash. They initiate a trading halt on moves of more than 10% within a five-minute window on Phase One or Phase Two securities (which include Russell 1000 stocks and some ETFs) and a halt on moves of 30% on all other stocks.
As an example of this kind of statistical artifact, fatalities from a plane crash will not move national mortality statistics a jot, but that is no consolation to the many victims. Academic statistical studies to date are simply not sharp enough to detect most of these episodes. If you look at some of these statistics for 2010, the Flash Crash itself isn’t even noticeable. Nanex, the market data company with its own theories about the crash, did an analysis that looked at stocks using finely tuned statistics.5 Nanex discovered hundreds—thousands—of “mini” flash crashes over the years. The worst year for them so far was 2008, but there were still more than a thousand of them in each of 2009 and 2010. There are several ways to define a mini flash crash. Nanex’s analysis looked at the general case where a stock has an extreme short-term price movement and then an immediate rebound, and counted all these. Some would sort these instances into at least two categories: one in which a large order instantaneously clears out an exchange’s order book down to (or up to) nonsense prices, and the other in which HFT market makers empty a book by withdrawing, or by dumping inventory and withdrawing as on May 6, 2010, but the effect on investors is the same—ricocheting stock prices simply because of structural or regulatory defects in the stock market.
High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara
algorithmic trading, asset allocation, backtesting, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, natural language processing, offshore financial centre, pattern recognition, price discovery process, price discrimination, price stability, quantitative trading / quantitative ﬁnance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve
The transmission of illiquidity from what 209 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 210 — #230 i i HIGH-FREQUENCY TRADING Figure 10.1 Flash crashes (a) 1,180 10,800 1,160 10,600 1,140 1,130 10,400 1,120 10,200 1,100 15h54 15h38 15h06 14h34 14h02 13h30 12h42 12h10 11h38 11h06 10h34 1,060 10h02 1,080 9,800 09h30 10,000 S&P 500 DJIA 11,000 (b) (a) US flash crash, May 6, 2012: black line (top), DJIA; mid-grey line (middle), E-mini S&P 500; dark-grey line (bottom), S&P 500 Index. (b) India flash crash, October 5, 2012. Source: adapted from Bloomberg. is the most liquid equity future contract to the equity market, as well as the speed with which this occurred, was a wake-up call for both markets and regulators. Figure 10.1 illustrates this US flash crash, as well as a flash crash in India.3 Unfortunately, there have now been a variety of similar illiquidity events in settings around the world, raising serious concerns about the stability of the HF market structure. How do we reconcile the apparent evidence of improved market quality with the equally apparent evidence of decreased market stability?
These studies show that high-frequency traders increase the overall market quality, but they fail to zoom in on extreme events, where their impact may be very different. A notable exception is the study by Kirilenko et al (2011) that uses audit-trail data and examines trades in the E-mini S&P 500 stock index futures market during the May 6, 2010, Flash Crash. They conclude that high-frequency traders did not trigger the Flash Crash; HFT behaviour caused a “hot potato” effect and thus exacerbated market volatility. In contrast to these studies, the following sections provide anecdotal evidence of the behaviour of computerised traders in times of severe stress in foreign exchange markets: • the JPY carry trade collapse in August 2007; • the May 6, 2010, Flash Crash; • JPY appreciation following the Fukushima disaster; • the Bank of Japan intervention in August 2011 and Swiss National Bank intervention in September 2011. 76 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 77 — #97 i i HIGH-FREQUENCY TRADING IN FX MARKETS While each of these episodes is unique in terms of the specific details and they occurred at different stages of the evolution of highfrequency traders, these events provide valuable insight into how computerised traders behave in periods of large price moves.
., 2010, Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies. London: 4Myeloma Press. Kirilenko, A. A., A. S. Kyle, M. Samadi and T. Tuzun, 2011, “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market”, Technical Report, May. Masry, S., 2013, “Event Based Microscopic Analysis of the FX Market”, PhD Thesis, University of Essex. 88 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 89 — #109 i i HIGH-FREQUENCY TRADING IN FX MARKETS Menkveld, A., 2012, “High Frequency Trading and the New-Market Makers”, Technical Report, February. Menkveld, A. J., and B. Z. Yueshen, 2013, “Anatomy of the Flash Crash”, SSRN Working Paper, April. Nanex, 2010, “May 6th 2010 Flash Crash Analysis: Final Conclusion”, August, http:// www.nanex.net/FlashCrashFinal/FlashCrashAnalysis_Theory.html. Schmidt, A., 2011, “Ecology of the Modern Institutional Spot FX: The EBS Market in 2011”, Technical Report, Electronic Broking Services.
Army of None: Autonomous Weapons and the Future of War by Paul Scharre
active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day
Navinder Singh Sarao,” February 11, 2015, https://www.justice.gov/sites/default/files/opa/press-releases/attachments/2015/04/21/sarao_criminal_complaint.pdf. 206 pin the blame for the Flash Crash: “Post Flash Crash, Regulators Still Use Bicycles To Catch Ferraris,” Traders Magazine Online News, accessed June 13, 2017, http://www.tradersmagazine.com/news/technology/post-flash-crash-regulators-still-use-bicycles-to-catch-ferraris-113762-1.html. 206 spoofing algorithm was reportedly turned off: “Guy Trading at Home Caused the Flash Crash,” Bloomberg.com, April 21, 2015, https://www.bloomberg.com/view/articles/2015-04-21/guy-trading-at-home -caused-the-flash-crash. 206 exacerbated instability in the E-mini market: Department of Justice, “Futures Trader Charged with Illegally Manipulating Stock Market, Contributing to the May 2010 Market ‘Flash Crash,’ ” April 21, 2015, https://www.justice.gov/opa/pr/futures-trader-charged-illegally-manipulating-stock-market-contributing-may-2010-market-flash. 206 “circuit breakers”: The first tranche of individual stock circuit breakers, implemented in the immediate aftermath of the Flash Crash, initiated a five-minute pause if a stock’s price moved up or down more than 10 percent in the preceding five minutes.
Knight’s runaway algo vividly demonstrated the risk of using an autonomous system in a high-stakes application, especially with no ability for humans to intervene. Despite their experience in high-frequency trading, Knight was taking fatal risks with their automated stock trading system. BEHIND THE FLASH CRASH If the Knightmare on Wall Street was like a runaway gun, the Flash Crash was like a forest fire. The damage from Knight’s trading debacle was largely contained to a single company, but the Flash Crash affected the entire market. A volatile combination of factors meant that during the Flash Crash, one malfunctioning algorithm interacted with an entire marketplace ready to run out of control. And run away it did. The spark that lit the fire was a single bad algorithm. At 2:32 p.m. on May 6, 2010, Kansas-based mutual fund trader Waddell & Reed initiated a sale of 75,000 S&P 500 E-mini futures contracts estimated at $4.1 billion.
If the stock price moves out of that band for more than fifteen seconds, trading is halted on that stock for five minutes. Circuit breakers are an important mechanism for preventing flash crashes from causing too much damage. We know this because they keep getting tripped. An average day sees a handful of circuit breakers tripped due to rapid price moves. One day in August 2015, over 1,200 circuit breakers were tripped across multiple exchanges. Mini-flash crashes have continued to be a regular, even normal event on Wall Street. Sometimes these are caused by simple human error, such as a trader misplacing a zero or using an algorithm intended for a different trade. In other situations, as in the May 2010 flash crash, the causes are more complex. Either way, the underlying conditions for flash crashes remain, making circuit breakers a vital tool for limiting their damage. As Greg Berman, associate director of the SEC’s Office of Analytics and Research, explained, “Circuit breakers don’t prevent the initial problems, but they prevent the consequences from being catastrophic.”
Advances in Financial Machine Learning by Marcos Lopez de Prado
algorithmic trading, Amazon Web Services, asset allocation, backtesting, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, G4S, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative ﬁnance, RAND corporation, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman
(d) LTAP on Passive group. (e) GTB on Active group. (f) LTAP on Active group 22.6.4 The Flash Crash of 2010 The extended time it took for the SEC and CFTC to investigate the Flash Crash of 2010 was the original motivation for CIFT's work. Federal investigators needed to sift through tens of terabytes of data to look for the root cause of the crash. Since CFTC publicly blamed the volume of data to be the source of the long delay, we started our work by looking for HPC tools that could easily handle tens of terabytes. Since HDF5 is the most commonly used I/O library, we started our work by applying HDF5 to organize a large set of stock trading data (Bethel et al. ). Let us quickly review what happened during the 2010 Flash Crash. On May 6, at about 2:45 p.m. (U.S. Eastern Daylight Time), the Dow Jones Industrial Average dropped almost 10%, and many stocks traded at one cent per share, the minimum price for any possible trade.
Class weights are weights that correct for underrepresented labels. This is particularly critical in classification problems where the most important classes have rare occurrences (King and Zeng ). For example, suppose that you wish to predict liquidity crisis, like the flash crash of May 6, 2010. These events are rare relative to the millions of observations that take place in between them. Unless we assign higher weights to the samples associated with those rare labels, the ML algorithm will maximize the accuracy of the most common labels, and flash crashes will be deemed to be outliers rather than rare events. ML libraries typically implement functionality to handle class weights. For example, sklearn penalizes errors in samples of class[j], j=1,…,J, with weighting class_weight[j] rather than 1. Accordingly, higher class weights on label j will force the algorithm to achieve higher accuracy on j.
Mendelson (1987): “Trading mechanisms and stock returns: An empirical investigation.” Journal of Finance, Vol. 42, pp. 533–553. Amihud, Y. (2002): “Illiquidity and stock returns: Cross-section and time-series effects.” Journal of Financial Markets, Vol. 5, pp. 31–56. Andersen, T. and O. Bondarenko (2013): “VPIN and the Flash Crash.” Journal of Financial Markets, Vol. 17, pp.1-46. Beckers, S. (1983): “Variances of security price returns based on high, low, and closing prices.” Journal of Business, Vol. 56, pp. 97–112. Bethel, E. W., Leinweber. D., Rubel, O., and K. Wu (2012): “Federal market information technology in the post–flash crash era: Roles for supercomputing.” Journal of Trading, Vol. 7, No. 2, pp. 9–25. Carlin, B., M. Sousa Lobo, and S. Viswanathan (2005): “Episodic liquidity crises. Cooperative and predatory trading.” Journal of Finance, Vol. 42, No. 5 (October), pp. 2235–2274.
Flash Boys: Not So Fast: An Insider's Perspective on High-Frequency Trading by Peter Kovac
bank run, barriers to entry, bash_history, Bernie Madoff, computerized markets, computerized trading, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, prediction markets, price discovery process, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day
For example, the SEC now legally requires brokers to apply risk controls before an order is placed – preventing orders like the one that started the flash crash from crushing the markets. Further, if there is an abrupt drop in a stock, all trading in that stock is paused. If the broader markets fall abruptly, the SEC and CFTC have mandated a uniform market-wide halt to trading. If you’re still curious, please read at least the executive summary of the SEC-CFTC report – it really answers a lot of questions and has a lot of data behind it. If you’re not still curious, just know that (a) the SEC and CFTC gathered and synthesized mountains of data to produce a comprehensive report, and (b) important fixes were made after the flash crash to prevent a recurrence in similar volatile markets, although more work still needs to be done. Does The Data Exist? “No one could say for sure what caused the flash crash – for the same reason no one could prove that high-frequency traders were front-running the orders of ordinary investors.
The reader is then led to infer that either the fear of Post-Only orders, or a fiery car crash due to the use of Post-Only orders, has scared everyone away from the stock market: “[T]he investing public had lost faith in the U.S. stock market. Since the flash crash back in May 2010, the S&P index had risen 65 percent, and yet trading volume was down 50 percent: For the first time in history, investors’ desire to trade had not risen with market prices. Before the flash crash, 67 percent of U.S. households owned stocks; by the end of 2013, only 52 percent did: the fantastic post-crisis bull market was noteworthy for how many Americans elected not to participate in it.” This is surprising for a number of reasons. Putting aside the numbers – which are actually wrong – the real surprise is why Lewis thinks people are wary of investing in the stock market today. It apparently is due to the twenty minutes of trading in 2010 known as the flash crash, and has nothing to do with losing $19.2 trillion dollars in the Financial Crisis. Really?
The answer is an unequivocal no. So perhaps a better question is: why, if in 200-plus pages, Lewis is going to put forth only three real-world examples purporting a vast market-wide conspiracy of front-running, would he choose this one? If this was one of the most credible and compelling anecdotes, one wonders what less-believable evidence was edited out. The Flash Crash Conspiracy Theory For stock market pundits, the flash crash of May 6, 2010, when the stock market and stock futures markets plunged 6% and recovered only twenty minutes later, is something of a Rorschach test. For data-driven analysts, this was an opportunity to dive deep into the complexity of the derivatives and equities markets, evaluate how they had failed, and fix it. Another group, however, gleefully rubbed their hands together in anticipation of a witch-hunt indulging their favorite conspiracy theory: high-frequency traders gone wild.
The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini
affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative ﬁnance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sam Peltzman, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond
It’s not always easy, however, to regulate these fast-playing traders. The theory is that HFTs may have flooded the system with orders. That created a slowdown in order processing, which led to delayed quotes and a loss in confidence around pricing on the day of the Flash Crash. Jittery Markets On the day of the Flash Crash, world developments made the market nervous. Market makers may have been shy about taking on sell volume on a day that was likely to have many sell orders. What made this jittery day different than other jittery days? The Real Cause of the Flash Crash A detailed investigation into Flash Crash causes showed what might have caused the market price chaos on that day in May.14 The market was already jittery on May 6, reacting to negative developments around the world, including the Greek and European debt crisis.
The Spoils of Having Friends in High Places Chapter 12: The Absurdity of Imbalance The Long-Dated Swap Imbalance The Repo Imbalance The 228 Wasted Resources and the Global Run on Banks Chapter 13: Asleep in Basel Basel I Basel II Basel and the Financial Crisis Chapter 14: Chapter The LTCM Spinoffs JWM Partners LLC Platinum Grove Asset Management The Others The Copycat Funds Chapter 15: The End of LTCM’s Legacy The Bear and the Gorilla Attack November Rain What Went Wrong? Chapter 16: New and Old Lessons from the Financial Crisis Interconnectedness and Crowds Leverage Systemic Risk and Too Big to Fail Derivatives: The Good, the Bad, and the Ugly Conflicts of Interest Policy Lessons Risk Management Counterparty Interaction Hedge Funds The Importance of Arbitrage Part III: The Aftermath Chapter 17: The Flash Crash Background Flash Crash Theories The Real Cause of the Flash Crash The Aftermath Chapter 18: Getting Greeked Members Only The Club’s Early Years Getting Greeked Greek Choices The EU’s Future Chapter 19: The Fairy-Tale Decade I Hate Wall Street The Real Costs of the Financial Crisis An Avatar’s Life Force Economic System Choices The Crisis of Crowds The Wine Arbitrage Appendices Appendix A: The Mathematics of LTCM's Risk-Management Framework A General Framework Measuring Risk Appendix B: The Mechanics of the Swap Spread Trade The Mechanics of the Swap Spread Trade The Long Swap Spread Trade The Short Swap Spread Trade Appendix C: Derivation of Approximate Swap Spread Returns Derivation of Approximate Swap Spread Returns Appendix D: Methodology to Compute Zero-Coupon Daily Returns Methodology to Compute Zero-Coupon Daily Returns Appendix E: Methodology to Compute Swap Spread Returns from Zero-Coupon Returns Appendices Appendix F: The Mechanics of the On-the-Run and Off-the-Run Trade Appendix G: The Correlations between LTCM Strategies Before and During the Crisis Appendix H: The Basics of Creative Mortgage Accounting Appendix I: The Business of an Investment Bank The Business of an Investment Bank Investment Banking Capital Markets Equities Fixed Income Foreign Exchange Global Distribution (Global Sales) Research Client Services Technology Corporate and Risk Management Summary Appendix J: The Calculation of the BIS Capital Adequacy Ratio The Calculation of the BIS Capital Adequacy Ratio The General Calculation An Example Appendix K: The U.S.
Chincarini raises some interesting arguments on crowding and risk models, and practitioners and academics can debate these ideas since risk management is still evolving and the market events of 2008 showed that these models were not fully reliable. There are places where Chincarini applies the crowd idea differently. For example, the author puts “crowd behavior” at the center of the flash crash of May 6, 2010, which is the subject of Chapter 17. The flash crash was, no doubt, a very interesting event. Most accounts blame a large trade in the equity futures market, apparently implemented by a poorly designed algorithm that did not respond appropriately as market impact increased over time. According to this version of events, the trade created a shock that led to a whiplike impact in exchange-traded funds (ETFs) and then even more profound price moves in individual equities as liquidity, today largely provided by high-frequency trading algorithms, dried up.
Flash Boys: A Wall Street Revolt by Michael Lewis
automated trading system, bash_history, Berlin Wall, Bernie Madoff, collateralized debt obligation, computerized markets, drone strike, Fall of the Berlin Wall, financial intermediation, Flash crash, High speed trading, latency arbitrage, pattern recognition, risk tolerance, Rubik’s Cube, Sergey Aleynikov, Small Order Execution System, Spread Networks laid a new fibre optics cable between New York and Chicago, the new new thing, too big to fail, trade route, transaction costs, Vanguard fund
He watched the most sophisticated investors respond after Duncan Niederauer, the CEO of the New York Stock Exchange, embarked on a goodwill tour, the purpose of which seemed to be to explain why the New York Stock Exchange had nothing to do with the flash crash. “That’s when a light went off,” said Danny Moses, of Seawolf Capital, a hedge fund that specialized in stock market investments. He had heard Brad and Ronan’s pitch. “Niederauer was saying, ‘Hey, have confidence in us. It wasn’t us.’ Wait a minute: I never thought it was you. Why should I be concerned that it was you? It was like your kid walks into your house and says to you, ‘Dad, I didn’t dent your car.’ Wait, there’s a dent in my car?” After the flash crash, Brad no longer bothered to call investors to set up meetings. His phone rang off the hook. “What the flash crash did,” said Brad, “was it opened the buy side’s willingness to understand what was going on. Because their bosses started asking questions.
That was just a sampling from a single year of what were usually described as “technical glitches” in the new, automated U.S. stock markets: Collectively, they had experienced twice as many outages in the two years after the flash crash as in the previous ten. The technical glitches were accompanied by equally bewildering irregularities in stock prices. In April 2013, the price of Google’s shares fell from $796 to $775 in three-quarters of a second, for instance, and then rebounded to $793 in the next second. In May the U.S. utilities sector experienced a mini–flash crash, with stocks falling by 50 percent or more for a few seconds before bouncing back to their previous prices. These mini–flash crashes in individual stocks that now occurred routinely went largely unnoticed and unremarked upon.* Zoran liked to argue that there were actually fewer, not more, “technical glitches” in 2012 than there had been in 2006—it was only the financial consequences of system breakdowns that had grown.
The first thing Brad noticed as he read the SEC report on the flash crash was its old-fashioned sense of time. “I did a search of the report for the word ‘minute,’ ” said Brad. “I got eighty-seven hits. I then searched for ‘second’ and got sixty-three hits. I then searched for ‘millisecond’ and got four hits—none of them actually relevant. Finally, I searched for ‘microsecond’ and got zero hits.” He read the report once and then never looked at it again. “Once you get a sense of the speed with which things are happening, you realize that explanations like this—someone hitting a button—are not right,” he said. “You want to see a single time-stamped sheet of every trade. To see what followed from what. Not only does it not exist, it can’t exist, as currently configured.” No one could say for sure what caused the flash crash—for the same reason no one could prove that high-frequency traders were front-running the orders of ordinary investors.
Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson
algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!, zero-sum game
As Mathisson looked out over the audience, he knew Santayana wouldn’t be trolling clubs for bleach-blond babes this year. A freakish stock market crash on May 6, 2010—the so-called Flash Crash—had revealed that the computer-driven market was far more dangerous than anyone had realized. Regulators were angry, fund managers furious. Something had gone dramatically wrong. Senators were banging down Mathisson’s door wanting to know what the hell was going on. A harsh light was shining on an industry that had grown in the shadows. Mathisson was ready to confront the attack. He hit a button on the remote for his PowerPoint presentation. A graph appeared. A jagged line took a cliff-like plunge followed by a sharp vertical leap. It looked like a tilted V, the far right-hand side just lower than the left. “There’s the Flash Crash,” he said. “We all remember that day, of course.” The chart showed the Dow Jones Industrial Average, which took an eight-hundred-point swan dive in a matter of minutes on May 6 due to glitches deep in the plumbing of the nation’s computer-trading systems—the very systems built and run by many of the people sitting in the Glitter Room.
The chart showed the Dow Jones Industrial Average, which took an eight-hundred-point swan dive in a matter of minutes on May 6 due to glitches deep in the plumbing of the nation’s computer-trading systems—the very systems built and run by many of the people sitting in the Glitter Room. The audience stirred. The Flash Crash was a downer, and they were restless. It was going to be a long day full of presentations. Later that night, they’d be treated to a speech by the Right Honorable Gordon Brown, former prime minister of the United Kingdom. Ex–Clinton aide James Carville would address the group the following morning. (It was nothing unusual. Past keynote speakers at the conference had included luminaries such as former Federal Reserve chairman Alan Greenspan, former secretary of state Colin Powell, and the onetime junk-bond king Michael Milken.) Mathisson hit the button, calling up a chart showing that cash had flowed out of mutual funds every single month through 2010, following the Flash Crash. Legions of regular investors had become fed up, convinced the market had become either far too dangerous to entrust with their retirement savings, or just outright rigged to the benefit of an elite technorati.
., headquarters—Niederauer, Greifeld, Joe Ratterman from BATS, Bill O’Brien from Direct Edge—and gave them their marching orders: Implement circuit breakers for individual stocks in order to head off another flash crash. After years of pushing speed like an inner-city drug dealer, the SEC was shifting course. Marketwide circuit breakers, which would trigger a brief stop in trading if the market made a major move in a short period of time, were quickly implemented. They amounted to a reversal of the speed-freak frenzy that had hijacked the financial system in the past decade. It was time to slow things down. Was it enough? No one knew. IN the weeks and months following the events the media dubbed the Flash Crash, the fierce debate over what had become of the U.S. stock market that had erupted after the arrest of Sergey Aleynikov grew even more heated. Angry words were exchanged in the halls of Capitol Hill, on financial television shows, and in the backrooms of giant trading firms in New York and Chicago.
The Payoff by Jeff Connaughton
algorithmic trading, bank run, banking crisis, Bernie Madoff, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cuban missile crisis, desegregation, Flash crash, locking in a profit, London Interbank Offered Rate, London Whale, Long Term Capital Management, naked short selling, Neil Kinnock, plutocrats, Plutocrats, Ponzi scheme, risk tolerance, Robert Bork, short selling, Silicon Valley, too big to fail, two-sided market, young professional
S. exchange. Thanks to Josh’s intrepid research and synthesis, tutorials from our covert industry insiders, and our own exhaustive (and exhausting) reading, Ted and I became extremely knowledgeable about these practices and how they affect market stability. In fact, Ted even predicted the flash crash—when the market dropped one thousand points in just minutes on May 6, 2010—eight months before it happened. In a speech on September 14, 2009, the anniversary of the collapse of Lehman Brothers, Ted warned of a flash crash and how HFT would fuel it: [U]nlike specialists and traditional market-makers that are regulated, some of these new high-frequency traders are unregulated, though they are acting in a market-maker capacity. They have no requirements to “maintain a fair and orderly” market. They trade when it benefits them.
Ted had become the Oracle of Delaware, the man who’d read the algorithmic auguries of high-frequency trading and foreseen the flash crash. Jim Cramer of Mad Money called Ted the “most sophisticated man in Washington” and someone who was looking out for the average investor. Ted’s clairvoyance gave him instant credibility with many of his colleagues, and we intended to use it. I immediately started drafting a letter from Ted and Warner to Chris Dodd. We delivered it the next day, May 7. The letter asked Dodd to add to the Wall Street reform bill then before the Senate the requirement that the SEC and the Commodity Futures Trading Commission (CFTC) conduct a joint study of what had caused the flash crash and how it should be dealt with. “A temporary $1 trillion drop in market value is an unacceptable consequence of a software glitch,” the letter said.
But it still had no real knowledge about how HFT strategies affected the market or the average investor. As CNBC’s Jim Cramer later said of the SEC, “The lifeguard is off duty. And when you go swimming in this market, you’d better remember there’s nobody out there making sure the water is safe.” The flash crash taught at least three lessons, all of which Ted had identified long before May 6, 2010. First, stock prices don’t always reflect the market’s best estimation of the value of the underlying companies; in mini flash crashes, they can result from the breakdown of algorithmic trading strategies. Second, technology has far outpaced regulation. Regulators’ lack of understanding of HFT strategies and the volatility they create left the markets vulnerable to a nausea-inducing plunge. For example, the SEC took for granted that high-frequency traders were the new market makers without taking into account the ways in which they differed from traditional market makers.
Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better by Andrew Palmer
Affordable Care Act / Obamacare, algorithmic trading, Andrei Shleifer, asset-backed security, availability heuristic, bank run, banking crisis, Black-Scholes formula, bonus culture, break the buck, Bretton Woods, call centre, Carmen Reinhart, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Graeber, diversification, diversified portfolio, Edmond Halley, Edward Glaeser, endogenous growth, Eugene Fama: efficient market hypothesis, eurozone crisis, family office, financial deregulation, financial innovation, fixed income, Flash crash, Google Glasses, Gordon Gekko, high net worth, housing crisis, Hyman Minsky, implied volatility, income inequality, index fund, information asymmetry, Innovator's Dilemma, interest rate swap, Kenneth Rogoff, Kickstarter, late fees, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, margin call, Mark Zuckerberg, McMansion, money market fund, mortgage debt, mortgage tax deduction, Myron Scholes, negative equity, Network effects, Northern Rock, obamacare, payday loans, peer-to-peer lending, Peter Thiel, principal–agent problem, profit maximization, quantitative trading / quantitative ﬁnance, railway mania, randomized controlled trial, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, short selling, Silicon Valley, Silicon Valley startup, Skype, South Sea Bubble, sovereign wealth fund, statistical model, Thales of Miletus, transaction costs, Tunguska event, unbanked and underbanked, underbanked, Vanguard fund, web application
If you have a few minutes to spare and want to listen to the sound of chaos, find an audio recording of the futures pits in Chicago during those moments of panic.17 A joint Commodity Futures Trading Commission and Securities and Exchange Commission (SEC) task force that investigated the flash crash found that the primary cause of the volatility was a “fundamental” seller. To be precise, at 2:32 p.m., a mutual fund began to execute an order to sell seventy-five thousand E-Mini S&P 500 futures contracts in order to hedge an existing equity exposure. Whether trying to offload these types of contract or any other asset, a seller does not want the price to go down before its order has gone through. So the sell order uses algorithms that are designed to sell the holding without being spotted, a bit like sneaking out of a party when it’s crowded. Some algorithms slice big orders into a lot of tiny ones; in the flash crash, the computers were programmed to sell more shares when there was more trading going on in the stock.
This can lead to extraordinary numbers of transactions, which is exactly what happened during the flash crash. In a 14-second period between 2:45:13 and 2:45:27, high-frequency traders swapped more than 27,000 E-Mini contracts, which accounted for almost half of the total trading volume, while buying only about two hundred additional contracts.19 Remember, however, that the original algorithm uses the volume of transactions as a prompt to unload shares. The increase in trading brought about by the “hot-potato” effect acted as a signal for the first algorithm to sell more stock, which moved the price down a bit further, sparking more activity by the high-frequency traders. The loop was closed. The flash crash comprised more elements than this volume-driven downward pressure, however. The price declines brought a lot more algorithms, used by both high-frequency traders and institutional investors, into play.
Basildon is already connected to markets in Frankfurt via microwave; a journey that takes around 8 milliseconds on fiber-optic cables takes 4.7 milliseconds or less this way. There is talk of a network of microwave towers stretching across the Atlantic to connect London and New York as high-frequency traders strive for the nirvana of zero latency.16 The changing nature of the financial markets became clear to the wider world in an event that has since become known as the “flash crash.” On May 6, 2010, in a 30-minute period between 2:30 p.m. and 3:00 p.m. EDT, a number of equity markets tumbled and rebounded with extraordinary rapidity. The Dow Jones Industrial Average fell by more than 5 percent in 5 minutes, before then recovering much of its losses. Individual share prices exhibited even more bizarre behavior. Shares in Accenture, a consultancy firm with $30 billion in annual revenues, plunged from more than $40 a share to, at one point, a single cent.
The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber
"Robert Solow", asset allocation, bank run, bitcoin, business cycle, butterfly effect, buy and hold, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial innovation, fixed income, Flash crash, Henri Poincaré, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative ﬁnance, railway mania, Ralph Waldo Emerson, Richard Feynman, risk/return, Saturday Night Live, self-driving car, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve
If the sellers could have waited longer for the liquidity they demanded, the buyers would have had time to react and the market would have cleared at a higher price. Instead, $500 billion in market value was erased in a couple of hours. THE FLASH CRASH OF MAY 6, 2010 On May 6, 2010, the U.S. equity markets suffered what became known as the Flash Crash. The market dropped more than 7 percent in a matter of fifteen minutes. Some stocks dropped to a penny a share; others rose to $100,000 a share. These extreme prices occurred for somewhat arcane reasons, but those really were the prices at which you would have had to sell or buy at that moment. They wouldn’t seem to be related, but the Flash Crash had echoes of the 1987 crash. The Flash Crash was another crisis of liquidity, one in which liquidity demand came in faster than the supply—the same sort of time disintermediation as occurred in 1987.
This stub bid is there because market makers have to post some bid and offer, and if they really are not interested, they will bid at a crazy price, like a penny a share, and offer at some other crazy price, like $100,000 a share. The mechanics of portfolio insurance and the Flash Crash are similar. Portfolio insurance is a hedging program, a dynamic one that adjusts based on the value of the portfolio. It’s like having a preprogrammed stop loss, where a certain amount of your portfolio is sold based on the portfolio value. The Flash Crash was also the result of preprogrammed selling, this time due to old-fashioned market stop loss orders. In 1987, the specialists were not well capitalized and fled in the face of the onslaught of sell orders. In 2010, no market makers were even expected to stand in front of the selling, and because of decimalization, there was a thin order book.
See also Conway’s Game of Life Conway’s Game of Life: as an agent-based model, 32–33, 122–123; and boids, 37; and computational irreducibility, 32; and context, 122–124; in the context of radical uncertainty, 123–124; emergence in, 32; rules of, 30–31; self-replication features of, 32; and Turing’s halting problem, 55 credit default swaps, 163–165 Cruise, Tom, 94 Darwin, Charles, 72–73 Dawkins, Richard, 181 decimalization, 149 deduction, 15, 107, 124, 180–183, 188–189 degenerative research program, 90–91 Demon of Our Own Design, A, 108, 157 Department of Defense, 158 Deutsche Bank, 165 diversification, 15–16 Dodd-Frank Act, 156–157 Dostoyevsky, Fyodor, 116 Duffie, Darrell, 152–153 dynamic stochastic general equilibrium model, 92 efficient market hypothesis, 116 emergence, 12; and boids, 37; and complexity, 38; and crises, 105; flock of birds movement, example of, 37; and Hajj stampede, 35–36; and heuristics, 65; and limits to knowledge, 52; and neoclassical economics, 83 (see also neoclassical economics); school of fish movement, example of, 36; and stability, 39; stampedes, example of 127–128; traffic example, 95, 97–98; and traffic flow, 17, 94 enclosures, 5 equilibrium, crises of, 104–105 ergodicity, 12, 17–18, 41, 196; context of, 40; history of, 40; and limits to knowledge, 52; and MGonz, 44; and neoclassical economics, 84 (see also neoclassical economics); in physical systems, 40; in physical versus social sciences, 85; testing models of, 177 essayism, 178 eternal recurrence, 60 fallibility, 59, 115, 117; and the rational expectations hypothesis, 175 Feynman, Richard, 54, 90 financial crises: fire marshal analogy, 127–129; financial crisis of 1987, 90; financial crisis of 2008, 92 (see also financial crisis of 2008); structure of, 129 financial crisis of 2008, 157; an agent-based view, 160; contagion during, 160; leverage and, 156, 176; liquidity and, 156; market-to-market difficulties and, 159–164; regulation and, 156; role of AIG in, 163–165; role of Bear Stearns Asset Management (BSAM) in, 161–162 (see also Bear Stearns Asset Management); role of Goldman Sachs in, 163–164 financial institutions: agents of, 99, 106; interactions between, 128–131 financial markets: complexity of, 108–109, 157; crisis in, 14–16, 108–109; environment of, 100–101; fire marshal analogy, 127–128; and Flash Crash, 147–151; and liquidity, 206; and reflexivity, 59; structure of, 128; weather analogy 113, 185–186 Financial Stability Oversight Council, 158 financial system: fire marshal analogy, 129; flows of, 131; multilayer schematic of, 131, 134; schematic system of, 129; structure of, 129, 131 fire sale, 107; asset-based, 138; funding-based, 139 Flash Crash, 147; effect of decimalization on, 149–150; effect of high-frequency trading on, 150; The Price is Right analogy, 148–150 Flaubert, Gustave, 116 Freudianism, 58 Frydman, Roman, 175 funding, 131, 134; cash providers of, 136; and collateral, 137; flows within financial system, 137; and hedge funds, 136; securities lenders for, 136 funding runs, 138–139 Funes, the Memorious, 75–78 Game of Life.
Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen
activist fund / activist shareholder / activist investor, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond
The most extreme trades were later canceled, however. The role of HFTs in the flash crash was not so much what they did but what they didn’t do, namely provide unlimited liquidity. However, the failure of market makers to provide liquidity in the face of overwhelming one-sided demand pressure, confusion about market prices, and increasing risk has always been a problem. For example, old-fashioned market makers in NASDAQ stocks and in over-the-counter markets have been known to take their phones off the hook when markets have gone off the cliff, e.g., in the 1987 stock market crash. Also, half a century before the flash crash of 2010, a similar event occurred that came to be known as the “Market Break of May 1962.” This event was also investigated by the SEC and, as in the flash crash, the SEC found that the “lateness of the NYSE tape and the size of the price declines on the NYSE prompted some over-the-counter dealers to withdraw as market makers in certain securities.”10 9.4.
Some HFTs may also try to identify and exploit large orders that are broken up into smaller trades and traded over hours or days. For example, if you are seeking to buy a large stock position, try to submit a limit order to buy the same number of shares each minute, right at the minute, and see what happens to your execution (relative to an execution where you split up the order more finely and more randomly and execute at more random times). The Flash Crash of 2010 On May 6, 2010, dramatic market events occurred in the U.S. stock market that came to be known as the flash crash. From the morning, the market was dropping on large trading volume and volatility due to rising fears about the ongoing European debt crisis. At 2:32 p.m., the Standard & Poor’s 500 (S&P 500) stock market index was down 2.8%. The limit order book was thinning due to the heightened volatility and because some exchanges were experiencing data delays and other data problems.
The last time this mutual fund had executed a similar sized order, it had done so over the course of several hours, but on the day of the flash crash, the selling mutual fund decided to have the order executed with an algorithm over just 20 minutes. Over the next 13 minutes, the market dropped 5.2% in value, an enormous move over such a short time period, as seen in figure 9.10. HFTs initially provided liquidity. They were net buyers as the market was dropping, but, at 2:41 p.m., HFTs turned around and became net sellers, perhaps to reduce their inventory risk, but throughout the event, HFTs were mainly buying and selling to each other as documented by the CFTC and SEC: Figure 9.10. The flash crash of May 6, 2010. Still lacking sufficient demand from fundamental buyers or cross-market arbitrageurs, HFTs began to quickly buy and then resell contracts to each other—generating a “hot-potato” volume effect as the same positions were rapidly passed back and forth.
Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies by Jeremy Siegel
Asian financial crisis, asset allocation, backtesting, banking crisis, Black-Scholes formula, break the buck, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, carried interest, central bank independence, cognitive dissonance, compound rate of return, computer age, computerized trading, corporate governance, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, Deng Xiaoping, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Financial Instability Hypothesis, fixed income, Flash crash, forward guidance, fundamental attribution error, housing crisis, Hyman Minsky, implied volatility, income inequality, index arbitrage, index fund, indoor plumbing, inflation targeting, invention of the printing press, Isaac Newton, joint-stock company, London Interbank Offered Rate, Long Term Capital Management, loss aversion, market bubble, mental accounting, money market fund, mortgage debt, Myron Scholes, new economy, Northern Rock, oil shock, passive investing, Paul Samuelson, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price stability, purchasing power parity, quantitative easing, random walk, Richard Thaler, risk tolerance, risk/return, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, stocks for the long run, survivorship bias, technology bubble, The Great Moderation, the payments system, The Wisdom of Crowds, transaction costs, tulip mania, Tyler Cowen: Great Stagnation, Vanguard fund
For stocks trading over $3 a share (except leveraged ETFs), the limit remains at 10 percent, except for the first and last 15 minutes of trading, when the limit is expanded to 20 percent.11 The flash crash, coming just a year after the deepest bear market in 75 years, eroded the public’s trust in a fair and orderly market for equities. Many cited the SEC indictment of high-frequency traders as evidence that the market is rigged against the small investor. But high-frequency trading declined after the flash crash, and a number of researchers questioned whether the trading played a significant role in that day’s decline. New rules established by the SEC have virtually eliminated the kind of “errant” and extreme trades that took place during the flash crash. But from a broader perspective, individual investors should not fear short-term market volatility. Should you not want to shop in a store where every so often it announces “10 percent to 20 percent off the price of all items for the next 30 minutes?”
The argument against halts is that they increase volatility by discouraging short-term traders from buying when prices fall sharply since they might be prevented from unwinding their position if trading is subsequently halted. This could lead to an acceleration of price declines toward the price limits, thereby increasing short-term volatility, as occurred when prices fell to these limits on October 27, 1997.6 FLASH CRASH—MAY 6, 2010 Monday October 19, 1987, and the following Tuesday stand as the most volatile days in U.S. stock market history. But investors were equally unnerved by the market collapse on May 6, 2010, an event that became known as the “flash crash.” Just after 2:30 p.m. eastern time, the Dow Industrials collapsed by more than 600 points or about 6 percent in a matter of minutes and recovered just as quickly. There was no economic or financial news that could account for the decline. Furthermore, thousands of individual stocks traded at prices that were more than 60 percent below (and a few far above) the prices they sold at just a few minutes earlier; some shares in well-known stocks traded as low as a penny a share.
Uncertainty and the Market Democrats and Republicans Stocks and War Markets During the World Wars Post-1945 Conflicts Conclusion Chapter 17 Stocks, Bonds, and the Flow of Economic Data Economic Data and the Market Principles of Market Reaction Information Content of Data Releases Economic Growth and Stock Prices The Employment Report The Cycle of Announcements Inflation Reports Core Inflation Employment Costs Impact on Financial Markets Central Bank Policy Conclusion PART IV STOCK FLUCTUATIONS IN THE SHORT RUN Chapter 18 Exchange-Traded Funds, Stock Index Futures, and Options Exchange-Traded Funds Stock Index Futures Basics of the Futures Markets Index Arbitrage Predicting the New York Open with Globex Trading Double and Triple Witching Margin and Leverage Tax Advantages of ETFS and Futures Where to Put Your Indexed Investments: ETFS, Futures, or Index Mutual Funds? Index Options Buying Index Options Selling Index Options The Importance of Indexed Products Chapter 19 Market Volatility The Stock Market Crash of October 1987 The Causes of the October 1987 Crash Exchange Rate Policies The Futures Market Circuit Breakers Flash Crash—May 6, 2010 The Nature of Market Volatility Historical Trends of Stock Volatility The Volatility Index The Distribution of Large Daily Changes The Economics of Market Volatility The Significance of Market Volatility Chapter 20 Technical Analysis and Investing with the Trend The Nature of Technical Analysis Charles Dow, Technical Analyst The Randomness of Stock Prices Simulations of Random Stock Prices Trending Markets and Price Reversals Moving Averages Testing the Dow Jones Moving-Average Strategy Back-Testing the 200-Day Moving Average Avoiding Major Bear Markets Distribution of Gains and Losses Momentum Investing Conclusion Chapter 21 Calendar Anomalies Seasonal Anomalies The January Effect Causes of the January Effect The January Effect Weakened in Recent Years Large Stock Monthly Returns The September Effect Other Seasonal Returns Day-of-the-Week Effects What’s an Investor to Do?
Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman
algorithmic trading, Anton Chekhov, Apple II, Benoit Mandelbrot, citation needed, combinatorial explosion, Danny Hillis, David Brooks, digital map, discovery of the americas, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, HyperCard, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Netflix Prize, Nicholas Carr, Parkinson's law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, Therac-25, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K
Homer-Dixon based part of his narrative on James Gleick, “A Bug and a Crash: Sometimes a Bug Is More Than a Nuisance,” 1996, http://www.around.com/ariane.html (which originally appeared in The New York Times Magazine, December 1996). For a similar discussion of proximate causes versus the underlying reasons for such sudden system failures, see Chris Clearfield and James Owen Weatherall, “Why the Flash Crash Really Matters,” Nautilus 023, April 23, 2015, http://nautil.us/issue/23/dominoes/why-the-flash-crash-really-matters. Three Mile Island nuclear disaster: Clearfield and Weatherall, “Why the Flash Crash Really Matters.” the system’s massive complexity: Essentially, the failure in each of these cases was due to endogenous complexity—the complexity that evolves within a large system—rather than just to any specific exogenous shock. popular narrative of the Challenger: It must be recognized that the Challenger accident was more complicated than the streamlined story we are often told about its cause.
the writer Quinn Norton has noted: Quinn Norton, “Everything is Broken,” The Message, May 20, 2014, https://medium.com/message/81e5f33a24e1. Langdon Winner notes in his book: Winner, Autonomous Technology, 290–91. computer scientist Danny Hillis argues: Danny Hillis, “The Age of Digital Entanglement,” Scientific American, September 2010, 93. Take the so-called Flash Crash: Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford, UK: Oxford University Press, 2014), 17. It is still not entirely clear, however, what caused the Flash Crash. Understanding something in a “good enough” way: See also César Hidalgo, Why Information Grows: The Evolution of Order, from Atoms to Economies (New York: Basic Books, 2015). CHAPTER 2: THE ORIGINS OF THE KLUGE the Internet first began to be developed: For more, see Barry M. Leiner et al., “Brief History of the Internet,” Internet Society, October 15, 2012, http://www.internetsociety.org/brief-history-internet.
Today’s markets involve not just humans, but large numbers of computer programs trading on a wide variety of information at rates faster than what people could do manually. These programs interlock in complicated ways, making decisions that can cascade through vast trading networks. But how are the decisions made on how to trade? By pouring huge amounts of data into still other programs, ones that fit vast numbers of parameters in an effort to squeeze meaning from incredible complexity. The result can be extreme. Take the so-called Flash Crash, when, on May 6, 2010, the global financial market experienced a massive but extremely rapid fluctuation in the stock market, as large numbers of companies lost huge amounts of value, only to regain them instants later. This crash seems to have involved a series of algorithms and their specific rules for trading all interacting in unexpected ways, causing a trillion dollars in lost value for a short period of time.
The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling by Adam Kucharski
Ada Lovelace, Albert Einstein, Antoine Gombaud: Chevalier de Méré, beat the dealer, Benoit Mandelbrot, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, correlation does not imply causation, diversification, Edward Lorenz: Chaos theory, Edward Thorp, Everything should be made as simple as possible, Flash crash, Gerolamo Cardano, Henri Poincaré, Hibernia Atlantic: Project Express, if you build it, they will come, invention of the telegraph, Isaac Newton, Johannes Kepler, John Nash: game theory, John von Neumann, locking in a profit, Louis Pasteur, Nash equilibrium, Norbert Wiener, p-value, performance metric, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative ﬁnance, random walk, Richard Feynman, Ronald Reagan, Rubik’s Cube, statistical model, The Design of Experiments, Watson beat the top human players on Jeopardy!, zero-sum game
And there are predator bots watching for these large trades, hoping to spot a big transaction and take advantage of the subsequent shift in the market. During the flash crash on May 6, 2010, there were over fifteen thousand different accounts trading the futures contracts involved in the crisis. In a subsequent report, the Securities and Exchange Commission (SEC) divided the trading accounts into several different categories, depending on their role and strategy. Although there has been much debate about precisely what happened that afternoon, if the crash was indeed triggered by a single event—as the SEC report suggested—the havoc that followed was not the result of one algorithm. Chances are it came from the interaction between lots of different trading programs, with each one reacting to the situation in its own way. Some interactions had particularly damaging effects during the flash crash. In the middle of the crisis, at 2:45 p.m., there was a drought of buyers for futures contracts.
Telegraph, April 23, 2013. http://www.telegraph.co.uk/finance/markets/10013768/Bogus-AP-tweet-about-explosion-at-the-White-House-wipes-billions-off-US-markets.html. 121One of the biggest market shocks: Details of the flash crash come from: US Commodity Futures Trading Commission and US Securities and Exchange Commission. Findings Regarding the Market Events of May 6, 2010. September 30, 2010. https://www.sec.gov/news/studies/2010/marketevents-report.pdf. 122Algorithms sift through the reports: Sonnad, Nikhil. “The AP’s Newest Business Reporter Is an Algorithm.” Quartz, June 30, 2014. http://qz.com/228218/the-aps-newest-business-reporter-is-an-algorithm/. 122To understand the problem: Keynes, John M. The General Theory of Employment, Interest, and Money (London: Palgrave Macmillan, 1936). 123“As soon as you limit what you can do”: Quotes come from author interview with J. Doyne Farmer, October 2013. 124Some traders have reported: Farrell, Maureen. “Mini Flash Crashes: A Dozen a Day.” CNN Money.
Algorithms sift through the reports and produce a couple of hundred words summarizing firms’ performance in the Associated Press’s traditional writing style. The change means that humans are now even more absent from the financial news process. In press offices, algorithms convert reports into prose; on trading floors, their fellow robots turn these words into trading decisions. The 2010 Dow Jones “flash crash” was thought to be the result of a different type of trigger event: a trade rather than an announcement. At 2:32 p.m., a mutual fund had used an automated program to sell seventy-five thousand futures contracts. Instead of spreading the order over a period of time, as a series of small icebergs, the program had apparently dropped the whole thing in pretty much all at once. The previous time the fund had dealt with a trade that big, it had taken five hours to sell seventy-five thousand contracts.
New Dark Age: Technology and the End of the Future by James Bridle
AI winter, Airbnb, Alfred Russel Wallace, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, British Empire, Brownian motion, Buckminster Fuller, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, drone strike, Edward Snowden, fear of failure, Flash crash, Google Earth, Haber-Bosch Process, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, John von Neumann, Julian Assange, Kickstarter, late capitalism, lone genius, mandelbrot fractal, meta analysis, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, WikiLeaks
While these orders may have actually helped the market swing back up again by continually providing liquidity, they might also have overwhelmed the exchanges in the first place. What is certain is that in the confusion they themselves had generated, many orders that were never intended to be executed were actually fulfilled, causing wild volatility in the prices. Flash crashes are now a recognised feature of augmented markets, but are still poorly understood. The next largest, a $6.9 billion flash crash, rocked the Singapore Exchange in October 2013, causing the market to implement limits on the number of orders that could be executed at the same time – essentially, an attempt to block the obfuscation tactics of high-frequency traders.28 The speed with which algorithms can react also makes them difficult to counteract. At 4:30 a.m. on January 15, 2015, the Swiss National Bank unexpectedly announced it was abandoning an upper limit on the Franc’s value against the Euro.
. – in just twenty-five minutes – it recovered almost all of those 600 points – becoming the largest and fastest swing ever. In the chaos of those twenty-five minutes, 2 billion shares, worth $56 billion, changed hands. Even more worryingly, and for reasons still not fully understood, many orders were executed at what the SEC called ‘irrational prices’: as low as a penny, or as high as $100,000.27 The event became known as the ‘flash crash’, and it is still being investigated and argued over years later. Regulators inspecting the records of the crash found that high-frequency traders massively exacerbated the price swings. Among the various high-frequency trading programmes active on the market, many had hard-coded sell points: prices at which they were programmed to sell their stocks immediately. As prices started to fall, groups of programmes were triggered to sell at the same time.
Other AP accounts, as well as journalists, quickly flooded the site with claims that the message was false; others pointed out inconsistencies with the organisation’s house style. The message was the result of a hack, and the action was later claimed by the Syrian Electronic Army, a group of hackers affiliated with Syrian President Bashar al-Assad and responsible for many website attacks as well as celebrity Twitter hacks.31 The algorithms following breaking news stories had no such discernment however. At 1:08 p.m., the Dow Jones, victim of the first flash crash in 2010, went into nosedive. Before most human viewers had even seen the tweet, the index had fallen 150 points in under two minutes, before bouncing back to its earlier value. In that time, it erased $136 billion in equity market value.32 While some commentators dismissed the event as ineffective or even juvenile, others pointed to the potential for new kinds of terrorism, disrupting markets through the manipulation of algorithmic processes.
Automate This: How Algorithms Came to Rule Our World by Christopher Steiner
23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, G4S, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Robert Mercer, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator
Algorithms normally behave as they’re designed, quietly trading stocks or, in the case of Amazon, pricing books according to supply and demand. But left unsupervised, algorithms can and will do strange things. As we put more and more of our world under the control of algorithms, we can lose track of who—or what—is pulling the strings. This is a fact that had sneaked up on the world until the Flash Crash shook us awake. Algorithms entered evening newscasts through the door of the Flash Crash, but they didn’t leave. They soon showed up in stories about dating, shopping, entertainment, medicine—everything imaginable. The Flash Crash had merely been an augur for a bigger trend: algorithms are taking over everything. When a process on the Web or inside a machine happens automatically, a pithy explanation often comes with it: “It’s an algorithm.” The classical definition of an algorithm says the device is a list of instructions that leads its user to a particular answer or output based on the information at hand.
Burnett’s journalistic fiber had her more excited. “But the fact that after all this, that that could have just happened, is an absolutely stupendous story,” she exclaimed. “I think it’s a great story,” Cramer said flatly. “It’s the greatest story never told. You’ll never know what happened here.” Cramer wasn’t wrong. As of this writing, there is still no consensus on the exact root of what became known as the Flash Crash. Some of the blame was directed at a Kansas City money manager whose algorithm sold off $4 billion worth of stock futures too quickly, sparking other algorithms to do the same. Some blame an unknown group of traders who conspired to send things down all at once through the use of coordinated algorithms. Some believe it was simply an old-fashioned panic, not unlike what the world witnessed in 1929.
Wall Street fortunes have been made betting on Gaussian distributions—and just as many have been lost on algorithms that embrace Gaussian outcomes but don’t account for fat tails. It’s easier to write algorithms to fit normal distributions. And despite history showing us repeatedly that human behavior is anything but normal, some hackers choose to account for only normal distribution. Using this assumption can make money 100 out of 100 days. But it’s day 101, the Black Monday of 1987, the Russian debt default of 1998, the Flash Crash of 2010, that can ruin those banking on algorithms designed purely around Gaussian distributions. Even Gauss, more than two hundred years ago, warned that errors of any magnitude are possible within a normal distribution.26 The introduction of normal distributions changed humankind and ushered in the modern field of statistics, which allows for the easy purchase of things like life insurance, the building of better bridges, and even, though not as important, betting on basketball games.
What They Do With Your Money: How the Financial System Fails Us, and How to Fix It by Stephen Davis, Jon Lukomnik, David Pitt-Watson
activist fund / activist shareholder / activist investor, Admiral Zheng, banking crisis, Basel III, Bernie Madoff, Black Swan, buy and hold, centralized clearinghouse, clean water, computerized trading, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crowdsourcing, David Brooks, Dissolution of the Soviet Union, diversification, diversified portfolio, en.wikipedia.org, financial innovation, financial intermediation, fixed income, Flash crash, income inequality, index fund, information asymmetry, invisible hand, Kenneth Arrow, Kickstarter, light touch regulation, London Whale, Long Term Capital Management, moral hazard, Myron Scholes, Northern Rock, passive investing, performance metric, Ponzi scheme, post-work, principal–agent problem, rent-seeking, Ronald Coase, shareholder value, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, Steve Jobs, the market place, The Wealth of Nations by Adam Smith, transaction costs, Upton Sinclair, value at risk, WikiLeaks
Soon the crisis spilled over to individual stocks. According to a joint report from the US Commodity Futures Trading Commission and the SEC, some twenty thousand individual trades on three hundred different securities were made at prices at least 60 percent different from the prices that existed before the flash crash started—even though the market had recovered in twenty minutes and had returned to normal by 3:00. Some stocks were traded at “irrational prices as low as one penny or as high as $100,000.”48 So the flash crash was warning no. 1, the false tweet warning no. 2. Barry Schwartz, a Canadian portfolio manager, draws a simple conclusion: “Don’t let computers rule your investments.”49 But few are listening. High-frequency computer-driven trading is estimated to account for 61 percent of all stock market trades in the United States.50 If such trading adds value, it does so at a cost to other investors.
“No human believed the story,” said Rick Fier, Director of Equity Trading at Conifer Securities. “Only the computers react to something that serious disseminated in such a way. I bought some stock well and did not sell into it. Humans win.”44 Others also blamed the computers but noted how scary that thought is.45 It was not the first time Wall Street had dodged a bullet. Nearly three years before, on May 6, 2010, there was the “flash crash.” At about 2:32 p.m., a mutual fund entered a sell order for a large amount—$4.1 billion’s worth—of a futures contract designed to mimic the performance of the S&P 500 index. The computer program that entered the trade was created to take into account the volume of trading, but not the time frame or the price.46 It turned out there weren’t enough buyers for such a large sale. Within seconds, high-frequency trading computers went into an electronic frenzy, creating what the Wall Street Journal called a “hot potato effect”: computers sold to other computers, which then tried to sell to other computers to hedge their positions.47 In just three minutes, from 2:41 to 2:44, the price of the futures contract and the actual S&P trading basket dropped 3 percent; then, in just fifteen more seconds, it dropped another 1.7 percent.
Lu Wang, Whitney Kisling, and Eric Lam, “Fake Post Erasing $136 Billion Shows Market Needs Humans” (Bloomberg News Service, April 24, 2013). 44. Ibid. 45. Ibid. 46. “Findings Regarding the Market Events of May 6, 2010: Report of the Staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues” (US Commodity Futures Trading Commission and US Securities and Exchange Commission, September 30, 2010). 47. Tom Lauricella, Kara Scannell, and Jenny Strasburg, “How a Trading Algorithm Went Awry: Flash-Crash Report Finds a ‘Hot Potato’ Volume Effect from Same Positions Passed Back and Forth,” Wall Street Journal, October 2, 2010. 48. Ibid. 49. Wang, Kisling, and Lam, “Fake Post Erasing $136 Billion.” 50. Larry Tabb, Tabb Group, “High Frequency Trading: What Is It and Should I Be Worried?,” presentation to the World Federation of Exchanges, Cambridge, MA, November 2009. 51. Chris Sier, interview for this book, April 25, 2015. 52.
Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat
AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day
We have produced designs so complicated: Charles Perrow, Normal Accidents: Living with High-Risk Technologies (Princeton, NJ: Princeton University Press, 1999), 11. The point of HFTs: CBS News, “How Speed Traders Are Changing Wall Street,” 60 Minutes, October 11, 2010, http://www.cbsnews.com/stories/2010/10/07/60minutes/main6936075.shtml (accessed July 3, 2011). After the sale, the price: Cohan, Peter, “The 2010 Flash Crash: What Caused It and How to Prevent the Next One,” Daily Finance, August 18, 2010, http://www.dailyfinance.com/2010/08/18/the-2010-flash-crash-what-caused-it-and-how-to-prevent-the-next/ (accessed July 3, 2011). The lower price automatically: Nanex, “Analysis of the ‘Flash Crash,’” last modified June 18, 2010, http://www.nanex.net/20100506/FlashCrashAnalysis_CompleteText.html. not only unexpected: Perrow, Charles, Normal Accidents, 8. We know that a lot of algorithms: “The Market’s Black Box: Engine for Efficiency or Ever-Growing Monster?”
They’d detect cascading algorithm interactions like the 2010 Flash Crash and unplug the machines. The Large Trader Rule requires detailed registration of AIs, along with full human organization charts. If this sounds like a prelude to large government intervention, it is. Why not? Wall Street has proven again and again that as a culture it cannot behave responsibly without strenuous regulation. Is that also true of AGI developers? Without a doubt. There’s no moral merit badge required for studying AGI. Pre-trade testing of algorithms could simulate algorithms’ behavior in a virtual environment before they were let loose on the market. AI Source Code audits and Centralized AI Activity Recording aim to anticipate errors, and facilitate after-game analysis following an accident, like the 2010 Flash Crash. But look back at the four levels of opacity mentioned earlier, and see if these defenses, even if they were fully implemented, sound anything like foolproof to you
According to Wissner-Gross, market observers have suggested that some seem to be signaling each other across Wall Street with millisecond trades that occur at a pace no human can track (these are HFTs or high-frequency trades, discussed in chapter 6). Wouldn’t the next logical step be to make your hedge fund reflective? That is, perhaps your algorithm shouldn’t automatically trigger sell orders based on another fund’s massive sell-off (which is what happened in the flash crash of May 2010). Instead it would perceive the sell-off and see how it was impacting other funds, and the market as a whole, before making its move. It might make a different, better move. Or maybe it could do one better, and simultaneously run a very large number of hypothetical markets, and be prepared to execute one of many strategies in response to the right conditions. In other words, there are huge financial incentives for your algorithm to be self-aware—to know exactly what it is and model the world around it.
Never Let a Serious Crisis Go to Waste: How Neoliberalism Survived the Financial Meltdown by Philip Mirowski
"Robert Solow", Alvin Roth, Andrei Shleifer, asset-backed security, bank run, barriers to entry, Basel III, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, blue-collar work, Bretton Woods, Brownian motion, business cycle, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, constrained optimization, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, dark matter, David Brooks, David Graeber, debt deflation, deindustrialization, do-ocracy, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, facts on the ground, Fall of the Berlin Wall, financial deregulation, financial innovation, Flash crash, full employment, George Akerlof, Goldman Sachs: Vampire Squid, Hernando de Soto, housing crisis, Hyman Minsky, illegal immigration, income inequality, incomplete markets, information asymmetry, invisible hand, Jean Tirole, joint-stock company, Kenneth Arrow, Kenneth Rogoff, Kickstarter, knowledge economy, l'esprit de l'escalier, labor-force participation, liberal capitalism, liquidity trap, loose coupling, manufacturing employment, market clearing, market design, market fundamentalism, Martin Wolf, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Naomi Klein, Nash equilibrium, night-watchman state, Northern Rock, Occupy movement, offshore financial centre, oil shock, Pareto efficiency, Paul Samuelson, payday loans, Philip Mirowski, Ponzi scheme, precariat, prediction markets, price mechanism, profit motive, quantitative easing, race to the bottom, random walk, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, savings glut, school choice, sealed-bid auction, Silicon Valley, South Sea Bubble, Steven Levy, technoutopianism, The Chicago School, The Great Moderation, the map is not the territory, The Myth of the Rational Market, the scientific method, The Wisdom of Crowds, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Vilfredo Pareto, War on Poverty, Washington Consensus, We are the 99%, working poor
It then broke out in the theoretical area concerning whereof the vast majority of neoclassical economists were most proud: the microeconomics of a fully competitive market.89 One of the most worrying heralds of fresh mortification was the so-called flash crash that occurred in New York share markets on the afternoon of May 6, 2010. For twenty minutes, starting at 2:30 p.m., trading volume spiked dramatically as a wide range of shares fell more than 5 percent in a matter of minutes, only to recover equally sharply (Figure 4.7). The same also happened on a number of exchange-traded indexes. * * * Figure 4.7: The “Flash Crash” of May 6, 2010 * * * * * * Source: Bloomberg Some individual share prices dropped to mere pennies in price, forcing the various exchanges to impose “broken” or canceled trades on something like 27 percent of all transactions.
Perhaps more distressing, a month later, the government investigators and financial economists were no more the wiser as to the real causes of the whipsaw movement.90 What was more disconcerting was that a brawl subsequently broke out among economists over which of the myriad “innovations” in trading may have been the culprit: high-frequency automated trading, the dispersal of trades among numerous for-profit exchanges, robo-trading in general, the practice of “stub quotes,” the role of exchange-traded funds, and so forth. Other, smaller flash crashes have occurred in financial markets since then, among which may be counted the BATS IPO fiasco and the Knight Capital spasm. Distressingly, no consensus interpretation of the flash crashes has taken hold among economists. While this should give market participants pause, you might have thought it would frighten economists even more, since this phenomenon potentially contradicts everything their core models postulate about market behavior. Neoliberals should equally take umbrage, since the wonderful information processing capacities of the market seem impugned by such events.
OK, maybe I can let microeconomists off the hook” (Krugman, “The Profession and the Crisis,” p. 307). 90 The preliminary SEC report can be consulted at www.sec.gov/sec-cftc-prelimreport.pdf. The final attempt at imposing consensus came out five months later as U.S. SEC, Findings Regarding the Market Events of May 6, 2010, but the dispute nevertheless continues. See Kirilenko et al., “The Flash Crash”; Easley et al., “The Microstructure of the Flash Crash”. 91 This attitude is almost too ubiquitous to document properly. For selected examples, see Coyle, “The Public Responsibilities of the Economist”; Krugman, End This Depression Now!; and Quiggin, Zombie Economics. 92 One rather unapologetic example is Eric Maskin at http://thebrowser.com/interviews/eric-maskin-on-economic-theory-and-financial-crisis. Maskin is domiciled at the Institute for Advanced Study, and is a specialist in game theory, which may go some distance in explaining the intellectual bubble he inhabits.
Bad Data Handbook by Q. Ethan McCallum
Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative ﬁnance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application
Those trades officially never happened, and in the end no money changed hands. Most stock data sources have already had the canceled trades removed, so it is difficult to even see the evidence of the flash crash in the historic record. In that sense, the flash crash contained bad data, not bad reality. Yet, the only difference between the flash crash and the United Airlines example is that human intervention undid the flash crash after it happened; it was “too big to fail.” To further cloud the issue, people believed that those trades were real when they happened. Stock trading is state-dependent; how you behave depends on the previous trades that happened. To properly model trading during the flash crash, you would have to simulate making trades, updating your portfolio, and then having them canceled afterwards. In reality, you would just shrug your shoulders and hope that it is okay to ignore this sort of crash.
United Airlines (UAUA) Stock Price on 2008-09-08, Volume-Weighted Average over One-Minute Intervals Many data-cleaning algorithms would throw out these few minutes of obviously wrong prices. Yet, these prices did happen; real money was exchanged. Within ten minutes, 15% of UAUA’s shares and $156 million had changed hands. This is correct data that should be kept for any honest analysis. Other times, the answer is not so clear cut. In the May 6th, 2010, “flash crash,” many stocks suddenly dropped in price by more than half for no readily apparent reason before going back again when sanity prevailed. There is still no consensus on exactly what happened. After the crash had been resolved, the various stock exchanges retroactively canceled all trades that were more than 60% different from the pre-crash price. This caused an odd situation where people who got a really good bargain buying shares for half the correct price got to keep their gains, while those who had an even better deal at 60% off had to give the profits back.
Situations like the United Airlines example are more or less just outlier detection; a bad price that persists for one second should probably be thrown out while one that keeps going for a few minutes probably should not. It is a matter of judgment to decide what kind of thresholds to set and what error rates are acceptable, but that is a tractable data problem. But there is little that can be done to deal with situations like the flash crash, except to know that they happened and work around them. There is no systematic way to model extraordinary events which have been shaped by human judgment and politics. Conclusion The theme of these examples is that clean-looking data often has additional complexity lurking under the surface. If you do not understand the data, where it comes from, and what it represents, then your conclusions may carry a bias.
Rigged Money: Beating Wall Street at Its Own Game by Lee Munson
affirmative action, asset allocation, backtesting, barriers to entry, Bernie Madoff, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, call centre, Credit Default Swap, diversification, diversified portfolio, estate planning, fiat currency, financial innovation, fixed income, Flash crash, follow your passion, German hyperinflation, High speed trading, housing crisis, index fund, joint-stock company, money market fund, moral hazard, Myron Scholes, passive investing, Ponzi scheme, price discovery process, random walk, risk tolerance, risk-adjusted returns, risk/return, stocks for the long run, stocks for the long term, too big to fail, trade route, Vanguard fund, walking around money
Short term, if you only care about generating commissions, HFTs will work until routers stop hitting your ECN. Thus, it is the ECN that wants to be careful of the bad HFTs, since they are the only people that can root them out. Figure 6.1 100,000? A Lie! When the Lights Go Out It seems that after the Flash Crash we learned that HFTs provided a false sense of security. Sure, under normal circumstances HFTs are the new market makers, buying and selling all day long to bring liquidity to the market. However, when things go south, the operators just shut off the machines and the liquidity disappears. According to the SEC’s report on the Flash Crash, “Still lacking sufficient demand from fundamental buyers or cross-market arbitrageurs, HFTs began to quickly buy and then resell contracts to each other—generating a ‘hot-potato’ volume effect as the same positions were rapidly passed back and forth.
The knee-jerk reaction to the economic impact was like slow motion compared to the crash of 1929, which had the benefit of radio to broadcast the panic on a daily basis. You can understand how things have changed since then. Back then you couldn’t use your smart phone to take a picture of your house burning down or tweet about the destruction of San Francisco to your friends. Today, the world knew immediately about the 20-minute long Flash Crash of May 6, 2010, and was able to watch the market recover in real time. Is it progress that we are now at the point of 20-minute long stock market crashes? Technology has created a false veneer of total information awareness. I’m here to expose how a few specific events occurred that contributed to the delinquency of the long-term investor. Many market prognosticators like to spit out factoids of how the average holding period of stocks back in the 1950s was around eight years.
Say you want to sell your house today for $100 (yes, you live in Detroit). If only one person walks by and offers $50, that’s the price you can sell for. If two people will pay $100, the price might rise until one backs down. In each case, the transaction price is based on what the other person is willing to pay; either a limit they set, or the market price you accept. When the market declines quickly, as we saw in the Flash Crash of May 6, 2010, many orders were placed to sell at the market price. Because there were not enough orders to buy at the market price, stocks fell until the market found a price that buyers were willing to pay. That is why the market didn’t go to zero that day. At some point orders came in, or limits were hit to buy shares at the specified prices. So, the next time someone tells you there were more sellers than buyers, you can tell him there were equal amount of both, but more liquidity takers on the buy side.
Wait: The Art and Science of Delay by Frank Partnoy
algorithmic trading, Atul Gawande, Bernie Madoff, Black Swan, blood diamonds, Cass Sunstein, Checklist Manifesto, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, Daniel Kahneman / Amos Tversky, delayed gratification, Flash crash, Frederick Winslow Taylor, George Akerlof, Google Earth, Hernando de Soto, High speed trading, impulse control, income inequality, information asymmetry, Isaac Newton, Long Term Capital Management, Menlo Park, mental accounting, meta analysis, meta-analysis, MITM: man-in-the-middle, Nick Leeson, paper trading, Paul Graham, payday loans, Ralph Nader, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, six sigma, Spread Networks laid a new fibre optics cable between New York and Chicago, Stanford marshmallow experiment, statistical model, Steve Jobs, The Market for Lemons, the scientific method, The Wealth of Nations by Adam Smith, upwardly mobile, Walter Mischel
Although some politicians have argued for regulators to police high-frequency trading, it is unlikely that regulators would have much of a chance against computer trading algorithms, any more than they would be able to beat a computer at chess or Jeopardy. By the time the federal government’s report on the flash crash was published on September 30, 2010, market participants already had switched to new strategies. No one would use Waddell & Reed’s trading program today. Although regulators won’t have much of a chance battling high-frequency traders directly, there is one policy they might implement to help protect against future flash crashes: instead of trying to keep up with the markets, regulators could help slow them down by introducing explicit pauses. Stock exchanges already use circuit breakers to force markets to shut down when they have declined by certain amounts.11 So here is one concrete proposal to help traders slow down: force them to take a lunch break.
The details of the “flash crash” were reported in “Findings Regarding the Market Events of May 6, 2010,” report of the staffs of the Commodities Futures Trading Commission (CFTC) and the Securities and Exchange Commission (SEC) to the Joint CFTC-SEC Advisory Committee on Emerging Regulatory Issues, September 30, 2010. 7. Joel Hasbrouck and Gideon Saar, “Low-Latency Trading,” Johnson School Research Paper Series 35-2010, September 1, 2011, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1695460; see also Jonathan A. Brogaard, “High-Frequency Trading and Market Quality,” Working Paper Series, July 17, 2010, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1970072; Andrei Kirilenko, Albert S. Kyle, Mehrdad Samadi, and Tugkan Tuzun, “The Flash Crash: The Impact of High-Frequency Trading on an Electronic Market,” Working Paper Series, May 26, 2011, available at: http://papers.ssrn.com/sol3/papers.cfm?
The E-Mini contract and all of these stocks recovered. By 3:08 PM, the market had settled and prices were about the same as they were before Waddell & Reed had started its trading program. When the firm’s computers finished selling 75,000 E-Mini contracts, they ran out of instructions and shut down. The entire ride, the bust and the boom, had taken just thirty-six minutes. It became known as the “flash crash.”6 Numerous studies show that under normal conditions high-frequency traders are a powerful positive force in the markets. They improve liquidity, making it easier and cheaper for us to buy and sell stocks. They reduce volatility, particularly in the short term, so that stock prices remain relatively stable.7 However, other studies show that during periods of high uncertainty, such as May 6, 2010, high-frequency trading is associated with increased volatility and sudden, abrupt swings in the prices of stocks.8 Overall, the evidence is mixed.
The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards
"Robert Solow", Affordable Care Act / Obamacare, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Swan, blockchain, Bonfire of the Vanities, Bretton Woods, British Empire, business cycle, butterfly effect, buy and hold, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, cellular automata, cognitive bias, cognitive dissonance, complexity theory, Corn Laws, corporate governance, creative destruction, Credit Default Swap, cuban missile crisis, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, Edward Lorenz: Chaos theory, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, fiat currency, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, Fractional reserve banking, G4S, George Akerlof, global reserve currency, high net worth, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Isaac Newton, jitney, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, Paul Samuelson, Peace of Westphalia, Pierre-Simon Laplace, plutocrats, Plutocrats, prediction markets, price anchoring, price stability, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk-adjusted returns, Ronald Reagan, Silicon Valley, sovereign wealth fund, special drawing rights, stocks for the long run, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transfer pricing, value at risk, Washington Consensus, Westphalian system
The October 15, 2014, flash crash stands alone as an earthquake that emerged unannounced from an unobservable shift of deep tectonic plates. Other foreshocks of comparable magnitude were soon to come. On Thursday, January 15, 2015, three months to the day after the Treasury yield flash crash, the Swiss franc surged 20 percent against the euro, and a comparable amount against the dollar, in a twenty-minute event window from 9:30 a.m. to 9:50 a.m. Central European Time. In effect, there was a flash crash in the depreciating currencies, the euro and the dollar. Before the event, one euro was pegged at 1.20 francs. Within minutes, one euro was worth only one franc. Collateral damage was extensive—Swiss stocks plunged 10 percent the day the franc was revalued. Unlike the Treasury flash crash, the Swiss franc shock was triggered by a specific event.
The rarity of the intraday 37-basis-point flash crash in yields is troubling enough. More troubling is the observation of a 16-basis-point fall in six minutes. That move is completely unprecedented. The other three comparable events took place over the course of an entire trading day, not an event window measured in minutes. But the most disquieting aspect is that the October 15, 2014, yield crash occurred on a day when nothing else happened. There was no news that day. The crash just happened. An official joint staff report from the Treasury, Fed, SEC, and CFTC summarized the events of October 15, 2014: “For such significant volatility and a large round-trip in prices to occur in so short a time with no obvious catalyst is unprecedented in the recent history of the Treasury market.” Flash crashes are comprehensible on days when the world is panicked (October 8, 2008), the Federal Reserve rides to the rescue (March 18, 2009), or the United States suffers a credit downgrade (August 9, 2011).
The Alchemists: Three Central Bankers and a World on Fire by Neil Irwin
"Robert Solow", Ayatollah Khomeini, bank run, banking crisis, Berlin Wall, Bernie Sanders, break the buck, Bretton Woods, business climate, business cycle, capital controls, central bank independence, centre right, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, currency peg, eurozone crisis, financial innovation, Flash crash, George Akerlof, German hyperinflation, Google Earth, hiring and firing, inflation targeting, Isaac Newton, Julian Assange, low cost airline, market bubble, market design, money market fund, moral hazard, mortgage debt, new economy, Northern Rock, Paul Samuelson, price stability, quantitative easing, rent control, reserve currency, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, savings glut, Socratic dialogue, sovereign wealth fund, The Great Moderation, too big to fail, union organizing, WikiLeaks, yield curve, Yom Kippur War
., fell from double digits to a single penny in a matter of moments. By shortly after 3 p.m., the market was climbing back to its normal level, and it closed the day down 3.2 percent, not far from where it had been before what would soon be known around the world as the Flash Crash. The episode had more to do with frailties in the U.S. stock market in a world in which trillions of dollars gush around through automated trades than anything that the ECB had done. But it wasn’t wholly unrelated to the crisis in Europe. The market had been falling all week as investors fretted about the Greek debt crisis. The Flash Crash was merely one particularly jarring piece of evidence of just how on-edge global investors had become over whether Trichet and his colleagues would intervene to keep Europe together. And given the uncertainty in those early hours (and days) about why it had happened, among the ECB officials themselves, it prompted a particular unhappy reaction: Did we do that?
(Recall that this was during the crucial final days of negotiations over what would become the Dodd-Frank financial reform act.) Geithner’s message to Trichet and other European officials that day and in the days that followed—and, for that matter, in the years that followed—was that the time for half-measures was over. The Flash Crash had only heightened the sense of urgency among the Americans, adding to pressure from around the world—it also came from the British and from Dominique Strauss-Kahn at the IMF—for the Europeans to move more boldly than they had to that point. About the same time as the Flash Crash, Trichet and his colleagues on the ECB Governing Council gathered to eat at the Palácio da Bacalhoa, a fifteenth-century estate south of Lisbon. At the moment of the crash, a bit before 8 p.m. Lisbon time, many of the central bankers’ BlackBerrys started vibrating simultaneously.
See European financial crisis (2007–2012); U.S. financial crisis (2007–2012) Financial industry home mortgages, risky products, 99–100 mortgage-backed securities as creation of, 101–2 risk taking and rewards, 107 Financial markets central bankers’ comments, impact on, 9 decline (2001), 99 Flash Crash, 218–19, 243 global stock market decline (2009), 165 stock market drop (2008), 145 Financial reform. See U.S. financial reform Financial Services Authority, 122, 234, 238 Financial Stability Board, 300 Fine, Camden, 178, 190, 196 Finland, anti-EU position, 297 First Name Club meeting (1910), 35–36, 43–44 Fischer, Stanley, 116 Fisher, Paul, 241 Fisher, Richard, 164, 187, 193, 196, 264, 275, 330, 332 Fixed-rate tender with full allotment, 3–4 Flaherty, Jim, 209 Flash Crash, 218–19, 243 Ford, Gerald, inflation, approach to, 67 France central bank. See Banque de France -England adversarial relationship, 55, 57 and European unity negotiations, 76–77, 81–82 Franco-German Declaration, 289–92 gold hoarding (1927–), 55, 58, 59 Great Depression, stability during, 55, 58, 59 inflation rate (1980s), 75 presidents.
Fed Up: An Insider's Take on Why the Federal Reserve Is Bad for America by Danielle Dimartino Booth
Affordable Care Act / Obamacare, asset-backed security, bank run, barriers to entry, Basel III, Bernie Sanders, break the buck, Bretton Woods, business cycle, central bank independence, collateralized debt obligation, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, financial deregulation, financial innovation, fixed income, Flash crash, forward guidance, full employment, George Akerlof, greed is good, high net worth, housing crisis, income inequality, index fund, inflation targeting, interest rate swap, invisible hand, John Meriwether, Joseph Schumpeter, liquidity trap, London Whale, Long Term Capital Management, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, moral hazard, Myron Scholes, natural language processing, negative equity, new economy, Northern Rock, obamacare, price stability, pushing on a string, quantitative easing, regulatory arbitrage, Robert Shiller, Robert Shiller, Ronald Reagan, selection bias, short selling, side project, Silicon Valley, The Great Moderation, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, yield curve
I spent time over the next few months consulting with my peers at the Markets Desk as they zeroed in on high-frequency trading (HFT) as the cause of the “flash crash,” which wiped out $1 trillion of investors’ equity. I was surprised at how little proprietary data the Desk had on HFT and that there had not been more coordination between the Desk and the SEC. Lehman had failed eighteen months ago. What had happened to promises of regulatory coordination? The crash’s trigger was ultimately traced to a young British trader named Navinder Singh Sarao, who was accused of “spoofing” futures markets on the Chicago Mercantile Exchange (CME) by placing thousands of trades that were later canceled. Arrested in April 2015, Sarao was dubbed “The Hound of Hounslow” by the British tabloids. At the time of the flash crash, Sarao was thirty-one years old and living with his parents at their home near Heathrow Airport.
Over the subsequent four years he had made $40 million using the same techniques. Sarao was charged with twenty-two counts of wire fraud and market manipulation. His ability to manipulate the CME laid bare how vulnerable the markets had become to one rogue trader—and how important it was for the Fed to understand the new technologies that had overtaken the financial system. On May 9, a few days after the flash crash, the FOMC held an unscheduled meeting and announced it would reopen swap lines with the ECB and other central banks to stem the chaos in the eurozone. That summer, the Senate version of the proposed Dodd-Frank financial regulation overhaul bill finally lurched toward a vote. In late 2009, the House had passed its version with zero Republican support. Two years of fierce lobbying by Wall Street had delayed Senate action.
“This guy, for want”: Suzi Ring, John Detrixhe, and Liam Vaughan, “The Alleged Flash-Trading Mastermind Lived with His Parents and Couldn’t Drive,” Bloomberg.com, June 9, 2015, www.bloomberg.com/news/articles/2015-06-09/the-alleged-flash-trading-mastermind-lived-with-his-parents-and-couldn-t-drive. The DOJ maintained: Jane Croft and Philip Stafford, “Sarao Loses First Round of US Extradition Fight,” Financial Times, March 23, 2016, www.ft.com/fastft/2016/03/23/flash-crash-trader-sarao-to-be-extradited-to-us/. That summer, the Senate version: Signed by President Barack Obama, July 21, 2010, www.whitehouse.gov/blog/2010/07/21/president-obama-signs-wall-street-reform-no-easy-task. In July 2010: FRBNY: Zoltan Pozsar, Adrian Tobias, Adam Ashcraft, and Hayley Boesky, “Shadow Banking,” Federal Reserve Bank of New York Staff Reports No. 458, July 2010; revised February 2012.
Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan
Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, information asymmetry, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration
“Hedge Funder Spends $75M on Eastchester Manse,” Real Deal, August 1, 2012, http://therealdeal.com/blog/2012/08/01/hedge-funder-spend-75m-on-westchester-manse/. 12. http://www.deshawresearch.com, accessed November 26, 2014. 4. THE GODS ARE ANGRY 1. “Automated Trading: What Percent of Trades Are Automated?” Too Big Has Failed: Let’s Reform Wall Street for Good, April 3, 2013, http://www.toobighasfailed.org/2013/03/04/automated-trading/. 2. Marcy Gordon and Daniel Wagner, “‘Flash Crash’ Report: Waddell & Reed’s $4.1 Billion Trade Blamed for Market Plunge,” Huffington Post, December 1, 2010, http://www.huffingtonpost.com/2010/10/01/flash-crash-report-one-41_n_747215.html. 3. http://rocketfuel.com. 4. Steve Omohundro, “Autonomous Technology and the Greater Human Good,” Journal of Experimental and Theoretical Artificial Intelligence 26, no. 3 (2014): 303–15. 5. CAPTCHA stands for “Completely Automated Public Turing Test to tell Computers and Humans Apart.”
But the root cause is much more sinister—the emergence of invisible electronic agents empowered to take actions on behalf of the narrow self-interests of their owners, without regard to the consequences for the rest of the world. Because these agents are stealthy and incorporeal, we can’t perceive their presence or comprehend their capabilities. We’d be better off with robotic muggers —at least we could see them coming and run away. The “Flash Crash” of 2010 may have caught regulators’ attention, but it did nothing to slow the application of similar techniques to a wide variety of other domains. Any time you buy something, visit a website, or post a comment online, a hidden army of electronic agents, working for someone else, is watching you. Whole industries have sprung up that do nothing but sell weapons in the form of programs and data to companies brave enough to wade into these never-ending melees.
See government specific departments and agencies Federal Housing Administration (FHA), insurance program, 223n10 Federal Reserve Board, 171–72, 174 financial system, 45, 51–53, 57–58, 102 government programs, 168–69, 178 reinvestment vs. spending and, 117. See also assets ownership; stock markets fines, 88, 90 firefighters, 44, 46 First Amendment, 90 Fitch Ratings, 178 Fitzgerald, F. Scott, “The Rich Boy,” 109 “Flash Crash of 2010” (stock market plunge), 8–9, 61–63 Forbes magazine, 109, 113 forged laborers (robots), 3, 5, 35–48 advanced technologies and, 38–48 anthropomorphic bias and, 11, 24, 36–37, 87 capabilities of, 6, 38–48, 84–86, 90, 143 corporate investments in, 177 criminal acts and, 40, 85–86, 90 flexible systems of, 46–48 future uses of, 47–48 as independent agents, 91–92 labor market effects from, 10–12, 13, 134, 143–45 legal responsibility for actions of, 84–85 moral discretion and, 81 need for controls for, 203–4 paired with synthetic intellects, 6, 135 potential ownership of other robots by, 200 primitive precursors to, 39 sensor network for, 42–43, 44 slave status parallel with, 86 401K plans, 182 free market.
Present Shock: When Everything Happens Now by Douglas Rushkoff
algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, business cycle, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global pandemic, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, lateral thinking, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, negative equity, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K, zero-sum game
Algorithms determine the songs playing on Clear Channel stations, the ideal partners on dating websites, the best driving routes, and even the plot twists for Hollywood screenplays—all by compressing the data of experience along with the permutations of possibility. But the results aren’t always smooth and predictable. A stock market driven by algorithms is all fine and well until the market inexplicably loses 1,000 points in a minute thanks to what is now called a flash crash. The algorithms all feeding back to and off one another get caught in a loop, and all of a sudden Accenture is trading at $100,000 a share or Proctor & Gamble goes down to a penny.30 Ironically, and in a perfect expression of present shock, the leading high-frequency trading exchange had a high-profile flash crash on the same day it was attempting to conduct its own initial public offering—and on the same day I was finishing this section of the book. The company, BATS Global Markets, runs a stock exchange called Better Alternative Trading System, which was built specifically to accommodate high-frequency trading and handles over 11 percent of US equities transactions.
BATS issued 6 million shares for about $17 each, and then something went terribly wrong: their system suddenly began executing trades of BATS stock at three and four cents per share.31 Then shares of Apple trading on the BATS exchange suddenly dropped 10 percent, at which point the company halted trading of both ticker symbols. Embarrassed, and incapable of figuring out quite what happened, BATS took the extremely unusual step of canceling its own IPO and giving everyone back their money. What will be the equivalent of a flash crash in other highly compressed arenas where algorithms rule? What does a flash crash in online dating or Facebook friendships look like? What about in criminal enforcement and deterrence, particularly when no one knows how the algorithms have chosen to accomplish their tasks? Algorithmic present shock is instantaneous. Its results impact us before it is even noticed. At least on the stock market, participation in this pressure cooker is optional.
See gaming and Internet analyst Kevin Slavin’s terrific presentation on this history to the Lift11 Conference at www.livestream.com/liftconference/video?clipId=pla_08a3016b-47e9-4e4f-8ef7-ce71c168a5a8. 29. Kevin Slavin, “How Algorithms Shape Our World,” TedTalks, July 2011, www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world.html. 30. Nina Mehta, “Automatic Futures Trade Drove May Stock Crash, Report Says,” Bloomberg Businessweek, October 4, 2010. See also Graham Bowley. “Lone $4.1 Billion Sale Led to ‘Flash Crash’ in May,” New York Times, October 1, 2010. 31. Brian Bremner, “The Bats Affair: When Machines Humiliate their Masters,” Bloomberg Businessweek, March 23, 1012, www.businessweek.com/articles/2012-03-23/the-bats-affair-when-machines-humiliate-their-masters. 32. For the basics, see Alexandra Zendrian, “Don’t Be Afraid of the Dark Pools,” Forbes, May 18, 2009. 33. John Henley, “Greece on the Breadline: Cashless Currency Takes Off,” Guardian, March 16, 2012. 34.
Why I Left Goldman Sachs: A Wall Street Story by Greg Smith
always be closing, asset allocation, Black Swan, bonus culture, break the buck, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, delayed gratification, East Village, fixed income, Flash crash, glass ceiling, Goldman Sachs: Vampire Squid, high net worth, information asymmetry, London Interbank Offered Rate, mega-rich, money market fund, new economy, Nick Leeson, quantitative hedge fund, Renaissance Technologies, short selling, Silicon Valley, Skype, sovereign wealth fund, Stanford marshmallow experiment, statistical model, technology bubble, too big to fail
What was so disturbing to me—and to millions of people around the country who invest in the stock market—was the utter fragility the flash crash revealed in a market that had become insanely complicated. There were interlocking technologies and backup systems, none of them necessarily able to communicate with the others when things went wrong. High-frequency trading—computers making millions of trades per second—had become a massive proportion of the daily trading volume. Eventually, the SEC and the media settled on a trade made by the mutual fund company Waddell and Reed as the catalyst. No one will ever convince me that a mutual fund manager selling $2 billion in E-mini futures was responsible for what happened that May afternoon. When I was on Corey’s desk, I would routinely trade $3 billion of them myself. I never caused a flash crash. To an outsider, the mini-disaster may have looked reasonable: a big sale triggering a sell-off.
Everyone was gawking at their screens. What the hell was going on? Another thing people noticed is that stocks such as Accenture, CenterPoint Energy, and Exelon had, for a brief moment, lost the entirety of their value, and had traded as low as one cent per share. This wasn’t possible. How could a stock instantaneously lose its market cap in less than a second? This was unprecedented, to say the least. This was the flash crash. Between 2:42 and 2:47 P.M., the Dow Jones dropped 600 points beyond the 300 it had fallen earlier, for a loss of almost 1,000 points on the day. By 3:07 P.M., the market made back most of the 600 points. Whenever there is a very big and precipitous drop in the market that cannot be explained by any one news headline, investors almost always speculate: “Oh it must be a fat finger in the E-mini S&P futures”—meaning some clumsy trader accidentally sold off a massive amount of volume, far more than he intended, wreaking havoc in the process.
Whenever there is a very big and precipitous drop in the market that cannot be explained by any one news headline, investors almost always speculate: “Oh it must be a fat finger in the E-mini S&P futures”—meaning some clumsy trader accidentally sold off a massive amount of volume, far more than he intended, wreaking havoc in the process. In the early 2000s, there were a few famous fat-finger issues as the E-mini was taking over the big futures contract that had been traded in the pit. We used to joke on the desk that the guy who kept “fat-fingering” was a mysterious character known as the “E-Mini Bandit.” But was the flash crash the E-Mini Bandit’s work? What actually happened was never clear to me—nor, I think, to anyone else. Various theories were offered, but the one thing a number of people started saying was that the crash had been triggered by a large sale of E-mini S&P 500 futures, the very product I had traded years ago on the Futures desk with Corey, the most liquid futures contract in the world. In the midst of this bizarre twenty-five minutes, a number of senior people, remembering my experience with these futures, came up to me for an analysis of what was going on.
The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl
3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, lifelogging, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator
If this is the case, then how many shipwrecks do we need before we stop building ships? Those looking for stories of algorithms run amok can certainly find them with relative ease. On May 6, 2010, the Dow Jones Industrial Average plunged 1,000 points in just 300 seconds—effectively wiping out close to $1 trillion of wealth in a stock market debacle that became known as the Flash Crash. Unexplained to this day, the Flash Crash has been pinned on everything from the impact of high-speed trading to a technical glitch.15 Yet few people would seriously put forward the view that algorithms are, in themselves, bad. Indeed, it’s not simply a matter of algorithms doing the jobs that were once carried out manually; in many cases algorithms perform tasks that would be impossible for a human to perform.
As technologies are invented and prove not to be the end of humanity, they recede into background noise, where they become fodder for further generations of disruptive technology, just as the strongest lions eventually weaken and are overtaken by younger, fitter ones. Confusing the matter further is the complex relationship we enjoy with technology on a daily basis. Like David Ecker, the Columbia Spectator journalist I quoted in the last chapter, most of us hold concerns over “bad” uses of technology, while enjoying everything good technology makes possible. To put it another way, how did I find out the exact details of the Flash Crash I mentioned above? I Googled it. Objectivity in the Post-mechanical Age One topic I continued to butt up against during the writing of this book (and in my other tech writing for publications like Fast Company and Wired) is the subject of objectivity. In each chapter of this book, the subject of objectivity never strayed far from either my own mind or the various conversations I enjoyed with the technologists I had the opportunity to interview.
(Winner) 134 Dodds, Peter 172–76 Dominguez, Jade 25 Dostoyevsky, Fyodor 118 Dourish, Paul 231 Dow Jones 219 drunk driving 142–44 Eagle, Nathan 85 Ecker, David 206–7, 219 eHarmony 71, 74–77, 88 see also Internet; love and sex; Warren, Neil Clark Eisenstein, Sergei 178 Electric Dreams 103 Ellul, Jacques 5, 56 EMD Serono 58 emotion sniffing 51–52 Emotional Optimisation 200–201 Enchanted Loom, The (Jastrow) 96 entertainment, see art and entertainment Epagogix 165–68, 170–72, 176, 179, 191, 203, 205 Eric Berne Memorial Scientific Award 23 Essay on the Moral Statistics of France (Guerry) 117 “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market” 173 Facebook 232, 241 and Facedeals 20 and facial recognition 215 how algorithms work with 2 jobs at 27 profiles, and people’s success 30–31 profiles, traits inferred from 37–38 Timeline feature on 38–39 and YouAreWhatYouLike 37 Facedeals 20 facial recognition and analysis 20, 33, 91, 146, 151, 193, 215 and Internet dating 78 Failing Law Schools (Tamanha) 216 Family Guy 196 Farewell to the Working Class (Gorz) 217–18 Fast Company 3, 35, 128, 220 on Amazon 44–5 Faster Than Thought (Bowden) 184 Faulkner, William 187 Feldman, Konrad 18–19 films, see art and entertainment Filter Bubble, The (Pariser) 47 Fincher, David 189 Find the Love of Your Life (Warren) 73 FindYourFaceMate 78 Fitbit 13 FitnessSingles 78 Flash Crash 219 flexitime 43 Food Stamp Act (US) 154–55 Ford, Henry 44 Foucault, Michel 101 Fourastie, Jean 219 Freud, Sigmund 11 Friedman, Milton 218 Galbraith, Robert 187 Gale, David 62–63, 66 Galton, Francis 31–32 gaming technology 32–33 Gass, John 148 Gates, Bill 182 Geek Logik (Sundem) 67–68 gender reassignment 26 GenePartner 77–78 Generation X (Coupland) 16 Gibson, William 194n Gild 25–26, 29–30 Gillespie, Tarleton 233 Gladwell, Malcolm 211 Goldman, William 161, 173 Good Morning America 67 Google 201–2 and auto-complete 225–27 claimed objectivity of 220–21 differentiated results from 46–48 dynamic-pricing patent granted to 50; see also differential pricing employment practices of 41–42 and facial recognition 215 Flu Trends algorithm of 238–39 how algorithms work with 2 and inadvertent racism 151 and Lake Wobegone Strategy 27–29 Levy’s study of 41 and news-outlet decline 225–27 People Analytics Group within 41–42; see also web analytics and self-driving cars 143, 213 Slate article on 41 and UAL 229 Google Earth 135 Google Glass 14, 26 Google Maps 16, 134–35 Google Street View 227 Google Translate 215, 221 Gorz, André 217 Gottschall, Jonathan 186 Gould, Stephen Jay 33–34 Graf, Daniel 135 graph theory 182 Grindr 89, 152 Guardian 84 Guattari, Félix 48, 54 Guerry, André-Michel 114–18 Gusfield, Joseph 142–43 Halfteck, Guy 32–34 Hansen, Mark 53 Hanson, Curtis 167 Heaven’s Gate 167 Henry VI (Shakespeare) 125–26 Her 103 Hitchcock, Alfred 17 Hogge, Becky 44 Holmes, Katie 68–69 Holmes, Oliver Wendell Jr. 158 Horkheimer, Max 179, 205 House of Cards 188–89 House of Commons, rebuilding of 45 How the Mind Works (Pinker) 80 Human Dynamics (at MIT) 85 Hume, David 199–200 Hunch 37, 234 Hunger Games, The 169 Hutcheson, Joseph C.
The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order by Paul Vigna, Michael J. Casey
Airbnb, altcoin, bank run, banking crisis, bitcoin, blockchain, Bretton Woods, buy and hold, California gold rush, capital controls, carbon footprint, clean water, collaborative economy, collapse of Lehman Brothers, Columbine, Credit Default Swap, cryptocurrency, David Graeber, disintermediation, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, fiat currency, financial innovation, Firefox, Flash crash, Fractional reserve banking, hacker house, Hernando de Soto, high net worth, informal economy, intangible asset, Internet of things, inventory management, Joi Ito, Julian Assange, Kickstarter, Kuwabatake Sanjuro: assassination market, litecoin, Long Term Capital Management, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, McMansion, means of production, Menlo Park, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, Network effects, new economy, new new economy, Nixon shock, offshore financial centre, payday loans, Pearl River Delta, peer-to-peer, peer-to-peer lending, pets.com, Ponzi scheme, prediction markets, price stability, profit motive, QR code, RAND corporation, regulatory arbitrage, rent-seeking, reserve currency, Robert Shiller, Robert Shiller, Ross Ulbricht, Satoshi Nakamoto, seigniorage, shareholder value, sharing economy, short selling, Silicon Valley, Silicon Valley startup, Skype, smart contracts, special drawing rights, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, supply-chain management, Ted Nelson, The Great Moderation, the market place, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, Turing complete, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, underbanked, WikiLeaks, Y Combinator, Y2K, zero-sum game, Zimmermann PGP
-wide gasoline prices from the Energy Information Administration, http://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_nus_w.htm; and bitcoin prices from the CoinDesk Bitcoin Price Index, http://www.coindesk.com/price. New York University professor David Yermack concluded that bitcoin: David Yermack, “Is Bitcoin a Real Currency?,” NBER Working Paper No. 19747, December 2013. You need look no further: CoinDesk Bitcoin Price Index. This included a harrowing “flash crash”: Paul Vigna, “BitBeat: A Bitcoin ‘Flash Crash’ as Volume Spike Briefly Takes Price to $309,” Wall Street Journal, MoneyBeat blog, August 18, 2014, http://blogs.wsj.com/moneybeat/2014/08/18/bitbeat-a-bitcoin-flash-crash-as-volume-spike-briefly-takes-price-to-309/. In a scathing presentation to the New York: Mark T. Williams, “Testimony of Mark T. Williams,” New York State Department of Financial Services, January 28–29, 2014, http://www.dfs.ny.gov/about/hearings/vc_01282014/williams.pdf. “I wouldn’t say hoarding is a bad thing”: Bobby Lee, interviewed by Michael J.
reports put the amount at anywhere from two thousand to half a million coins: For the low-end two thousand: Marc Bevand, “Major Attack on the World’s Largest Bitcoin Exchange,” Zorinaq, June 19, 2011, http://blog.zorinaq.com/?e=55; for the high-end half a million: Jason Mick, “Inside the Mega-Hack of Bitcoin: The Full Story,” DailyTech, June 19, 2011, http://www.dailytech.com/Inside+the+MegaHack+of+Bitcoin+the+Full+Story/article21942.htm. Bitcoin’s prices plunged to meet it: Jack Hough, “Bitcoin’s Flash Crash,” MarketWatch, June 22, 2011, http://blogs.marketwatch.com/paydirt/2011/06/22/bitcoin%E2%80%99s-flash-crash/; also, Tyler Cowan, “The Bitcoin Crash,” Marginal Revolution, http://marginalrevolution.com/marginalrevolution/2011/06/the-bitcoin-crash.html. The fraudulent trades would later be unwound and do not show up in historical price charts, although a chart at Bitcoin Charts, http://bitcoincharts.com/charts/mtgoxUSD#tgCzm1g10zm2g25zv, does show a “double float” of 1.7e+308 in the price columns, for six days after the nineteenth, the time the trades were being unwound.
In any case, a little more than four months after that November peak, the price was plumbing the depths of $344.24 following the collapse of Mt. Gox and amid news in early April of a crackdown by Chinese authorities. Things stabilized somewhat over the summer, but with frequent bouts of what would still be regarded as extreme volatility in any other currency market. This included a harrowing “flash crash” that occurred in mid-August solely on the Bulgaria-based exchange, BTC-e, where the price plunged from $500 to $309 in three minutes before bouncing most of the way back. CoinDesk’s Bitcoin Price Index (Courtesy of CoinDesk) A case can be made that bitcoin’s volatility is unavoidable for the time being. Earning respect and widespread adoption as a currency is a process; it can’t be achieved overnight.
This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman
23andMe, Albert Einstein, Alfred Russel Wallace, banking crisis, Barry Marshall: ulcers, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, butterfly effect, Cass Sunstein, cloud computing, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Exxon Valdez, Flash crash, Flynn Effect, hive mind, impulse control, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jaron Lanier, Johannes Kepler, John von Neumann, Kevin Kelly, lifelogging, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, Pierre-Simon Laplace, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Satyajit Das, Schrödinger's Cat, security theater, selection bias, Silicon Valley, Stanford marshmallow experiment, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, twin studies, Vilfredo Pareto, Walter Mischel, Whole Earth Catalog, WikiLeaks, zero-sum game
Between 2:42 P.M. and 2:50 P.M. on May 6, 2010, the Dow-Jones Industrial Average experienced a rapid decline and subsequent rebound of nearly six hundred points, an event of unprecedented magnitude and brevity. This disruption occurred as part of a tumultuous event on that day now known as the Flash Crash, which affected numerous market indices and individual stocks, even causing some stocks to be priced at unbelievable levels (e.g., Accenture was at one point priced at $.01). With tick-by-tick data available for every trade, we can watch the crash unfold in slow motion, a film of a financial calamity. But the cause of the crash itself remains a mystery. The U.S. Securities & Exchange Commission report on the Flash Crash was able to identify the trigger event (a $4 billion sale by a mutual fund) but could provide no detailed understanding of why this event caused the crash. The conditions that precipitated the crash were already embedded in the market’s web of causation, a self-organized, rapidly evolving structure created by the interplay of high-frequency trading algorithms.
The conditions that precipitated the crash were already embedded in the market’s web of causation, a self-organized, rapidly evolving structure created by the interplay of high-frequency trading algorithms. The Flash Crash was the birth cry of a network coming to life, eerily reminiscent of Arthur C. Clarke’s science fiction story “Dial F for Frankenstein,” which begins “At 0150 GMT on December 1, 1975, every telephone in the world started to ring.” I’m excited by the scientific challenge of understanding all this in detail, because . . . well, never mind. I guess I don’t really know. The Name Game Stuart Firestein Neuroscientist, chair of the Department of Biological Sciences, Columbia University Too often in science we operate under the principle that “to name it is to tame it,” or so we think.
., 268–69 cycles and, 171, 172–73 intelligent design and, 59–60, 89 of microbes, 16 mutation in, 99 selection in, see natural selection time and, 1–2, 223 toward intelligence, 4 expanding in-group, 194–95 experiments, 23–24, 34 controlled, 25–27, 274 double-blind control, 17–18, 44 failure in, 79–80 replicability of, 373–75 thought, 28–29 experts and authority figures, 18, 20, 34 explanation, levels of, 276 externalities, 124–26 extinction, 175, 362 extroversion, 232–33 eye, 130, 139, 141, 147–48, 163, 188–90, 359 facial attractiveness, 136, 137 failure, 79–80 fantasizing, 235–36 fear of the unknown, 55–57 Feynman, Richard, 20, 236 financial analysis, 186 financial crisis, 259, 261, 307, 309, 322, 386 financial instruments, 178, 179 financial risk, 259 Finn, Christine, 282–84 Firestein, Stuart, 62–64 fish, 90 Fisher, Helen, 229–31 Fiske, Susan, 267 Fitch, W. Tecumseh, 154–56 fixed-action patterns, 160–61 Flash Crash, 60–61 flavor, 141 Flock of Dodos, A, 268–69 flu, 351 vaccinations for, 56 Flynn, James, xxx, 372 Flynn effect, 89, 195 focusing illusion, 49–50 food chain, 312 Ford, Henry, 335 Foreman, Richard, 225 Foucault, Michel, 118 Fowler, James, 306 framing, 201–2, 203 free jazz, 254–56 free trade, 100 free will, 35, 48, 217 Freud, Sigmund, 37–38, 146, 147, 148 Friedman, Milton, 84 functional modularity, 131 future, 1–2 Galbraith, John Kenneth, 307 Galileo, 9, 28–29, 110, 162, 335 Galton, Francis, 242 Game of Life, 275–77 game theory, 94–95, 96, 318 Gandhi, Mohandas K., 335 gangs, 345 garbage, mental, 395–97 Gaussian distribution, 199, 200 gedankenexperiment, 28–29 Gefter, Amanda, 299–300 Gelernter, David, 246–49 Gell-Mann, Murray, 190, 388 General Motors, 204 general relativity, 25, 64, 72, 234, 297 generators, 277 genes, 10, 15, 32, 88, 97, 98, 99, 157, 165–66, 395 altruism and, 196 horizontal transfer of, 16 Huntington’s disease and, 59 hybrid vigor and, 194–95 McClintock’s work with, 240–41 pangenome, 16 personality and, 229, 233 see also DNA gene therapy, 56 genetically modified (GM) crops, 16, 56 genetic vulnerability, 278–79 geometry, hyperbolic, 109 Gershenfeld, Neil, 72–73 Gibbon, Edward, 128 Gibbs, J.
In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis
3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, animal electricity, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, Kickstarter, liberal capitalism, lifelogging, millennium bug, Moravec's paradox, natural language processing, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K
Early in the afternoon of Thursday, 6 May 2010 the Dow Jones Industrial Average fell by 6 per cent in a matter of minutes. Not only that, but all kinds of crazy things started happening with stock prices: some fell as low as one cent and others shot through the roof at US$100,000 apiece with no obvious cause. In fifteen nail-biting minutes almost US$1 trillion of market capitalisation was wiped out. Yet five minutes later the Dow was back to normal, as if nothing had happened. The incident became known as the ‘Flash Crash’.28 The causes of it are still highly contested. The official explanation by the Securities and Exchange Commission blames a single badly timed and overly large stock sale. But this is disputed by many experts, who instead point at a set of financial computer technologies called ‘high-frequency trading’ as the true culprit. Basically, what these technologies do is exploit tiny, nanosecond-scale intervals between the placement of an order to buy or sell stock and the actual transaction.
Our only other option is therefore adding yet another layer of complexity, of a non-human kind. What if we had super machines that could watch over us? Machines that monitored other machines and ensured no one spied on our data, machines that defended our vital computer systems and corrected the instabilities whenever they might occur, that guaranteed there could be no more ‘millennium bugs’ or ‘flash crashes’ or a digital apocalypse? What if we had machines that were truly intelligent and that would be our guardians? 15 MACHINES THAT THINK We have come a long way since Aristotle had the insight that logic follows rules. We saw how Boole and Frege pushed this insight further by codifying logic, thus enabling the development of computer languages that code logical rules. In the fullness of time, a torrent of inventions and innovations – such as the electric bulb, electromechanical relays, the transistor and miniaturisation – facilitated the development of advanced electronics.
In this chapter I shall explore what all this means. How close are we to truly intelligent machines – complete with self-awareness? What will the repercussions be for our economy and society as thinking machines begin to replace us in the workplace? Are we in danger of extinction from thinking machines that will one day become self-aware and take over the world – making the millennium bug and the flash crash incidents seem like child’s play? How close are we to the notorious ‘AI Singularity’? The wise men of Dartmouth Artificial Intelligence, as a distinct scientific discipline, was born in the summer of 1956 during a conference on the campus of Dartmouth College in New Hampshire. It was a truly historical event, and those who attended would go on to contribute major innovations in the field of AI in the years to come.
Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff
activist fund / activist shareholder / activist investor, Airbnb, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business process, buy and hold, buy low sell high, California gold rush, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, centralized clearinghouse, citizen journalism, clean water, cloud computing, collaborative economy, collective bargaining, colonial exploitation, Community Supported Agriculture, corporate personhood, corporate raider, creative destruction, crowdsourcing, cryptocurrency, disintermediation, diversified portfolio, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, market fundamentalism, Marshall McLuhan, means of production, medical bankruptcy, minimum viable product, Mitch Kapor, Naomi Klein, Network effects, new economy, Norbert Wiener, Oculus Rift, passive investing, payday loans, peer-to-peer lending, Peter Thiel, post-industrial society, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, TaskRabbit, The Future of Employment, trade route, transportation-network company, Turing test, Uber and Lyft, Uber for X, uber lyft, unpaid internship, Y Combinator, young professional, zero-sum game, Zipcar
For instance, one typical HFT strategy is to provide liquidity in a particular stock until the market shows signs of instability. The algorithm then suddenly withdraws all its bids and offers, leading to an immediate dearth of demand and a precipitous price drop. The smart algorithm, knowing it can make this happen, has already bet against the stock with derivative options. When the other algorithms realize what’s happening, they freeze up, too, leading to a “flash crash.” The stock goes down, but for no real-world reason. It’s just collateral damage from the game itself.31 Another common algorithm strategy is to flood the quote and order systems with fake trades—orders of intent but not full executions—to convince human traders (or other algorithms) that the market is moving in a particular direction. More than 90 percent of all quotes are fake gestures of this sort, generated by computers.32 Algorithms run on ultrafast computers connected as physically close to the stock exchange computers as possible.
It’s an iterative process, in which the algorithms adjust themselves and their activity on every loop, responding less to the news on the ground than to one another. Such systems go out of control because the feedback of their own activity has become louder than the original signal. It’s like when a performer puts a microphone too close to an amplified speaker. It picks up its own feedback, sends it to the speaker, picks it up again, and sends it through again, ad infinitum. The resulting screech is equivalent to the sudden market spike or flash crash created by algorithms iterating their own feedback. Traditional market players scratch their heads at these outlier events because they can’t be explained in terms of trading activity between humans. What made that bubble burst? Was it market sentiment, a piece of news, or something being overbought? None of the usual suspects indicated trouble. That’s why it has become popular to label these gaps in rationality “black swans”—as if they are utterly unpredictable anomalies.
., 229 Circuit City, 90 Citizens United case, 72 Claritas, 32 click workers, 50 climate change, 135, 227–28, 237 coin of the realm, 128–29 collaboration as corporate strategy, 106–7 colonialism, 71–72 commons, 215–23 co-owned networks and, 220–23 history of, 215–16 projects inspired by, 217–18 successful, elements of, 216–17 tragedy of, 215–16 worker-owned collectives and, 219–20 competencies, of corporations, 79–80 Connect+Develop, 107 Consumer Electronics Show, 19 Consumer Reports,33 contracting with small and medium-sized enterprises, 112 cooperative currencies, 160–65 favor banks, 161 LETS (Local Exchange Trading System), 163–65 time dollar systems, 161–63 co-owned networks, 220–23 corporations, 68–82 acquisition of startups, growth through, 78 amplifying effect of, 70, 73 Big Shift and, 76 cash holdings of, 76, 77–78 competency of, 79–80 cost reduction, growth through, 79–80 decentralized autonomous corporations (DACs), 149–50 Deloitte’s study of return on assets (ROA) of, 76–77 distributive alternative to platform monopolies, 93–97 evaluation of, 69–74 extractive nature of, 71–72, 73, 74, 75, 80–82 growth targets, meeting, 68–69 income inequality and, 81–82 limits to corporate model, 75–76, 80–82 managerial and financial methods to deliver growth by, 77–79 monopolies (See monopolies) obsolescence created by, 70–71, 73 offshoring and, 78–79 personhood of, 72, 73–74, 90, 91 recoding of, 93–97, 125–26 repatriation and, 80 retrieval of values of empire and, 71–72, 73 as steady-state enterprises, 97–123 Costco, 74 cost reduction, and corporate growth, 79–80 Couchsurfing.com, 46 crashes of 1929, 99 of 2007, 133–34 biotech crash, of 1987, 6 flash crash, 180 Creative Commons, 215 creative destruction, 83–87 credit, 132–33 credit-card companies, 143–44 crowdfunding, 38–39, 198–201 crowdsharing apps, 45–49 crowdsourcing platforms, 49–50 Crusades, 16 Cumbrian Pounds, 156 Curitiba, Brazil modified LETS program, 164–65 Daly, Herman, 184 data big, 39–44 getting paid for our own, 44–45 “likes” economy and, 32, 34–36 in pre-digital era, 40 Datalogix, 32 da Vinci, Leonardo, 236 debt, 152–54 decentralized autonomous corporations (DACs), 149–50 deflation, 169 Dell, 115–16 Dell, Michael, 115–16 Deloitte Center for the Edge, 76–77 destructive destruction, 100 Detroit Dollars, 156 digital distributism, 224–39 artisanal era mechanisms and values retrieved by, 233–34 developing distributive businesses, 237–38 digital industrialism compared, 226 digital technology and, 230–31 historical ideals of distributism, 228–30 leftism, distinguished, 231 Pope Francis’s encyclical espousing distributed approach to land, labor and capital, 227–28 Renaissance era values, rebirth of, 235–37 subsidiarity and, 231–32 sustainable prosperity as goal of, 226–27 digital economy, 7–11 big data and, 39–44 destabilizing form of digitally accelerated capitalism, creation of, 9–10 digital marketplace, development of, 24–30 digital transaction networks and, 140–51 disproportionate relationship between capital and value in, 9 distributism and, 224–39 externalizing cost of replacing employees in, 14–15 industrialism and, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 industrial society, distinguished, 11 “likes” and similar metrics, economy of, 30–39 platform monopolies and, 82–93, 101 digital industrialism, 13–16, 23–24, 101–2, 201 digital distributism compared, 226 diminishing returns of, 93 externalizing costs and, 14–15 growth agenda and, 14–15, 23–24 human data as commodity under, 44 income disparity and, 53–54 labor and land pushed to unbound extremes by, 214 “likes” economy and, 33 reducing bottom line as means of creating illusion of growth and, 14 digital marketplace, 24–30 early stages of e-commerce, 25–26 highly centralized sales platforms of, 29 initial treatment of Internet as commons, 25 “long tail” of widespread digital access and, 26 positive reinforcement feedback loop and, 28 power-law dynamics and, 26–29 removal of humans from selection process in, 28 digital transaction networks, 140–51 Bitcoin, 143–49, 150–51, 152 blockchains and, 144–51 central authorities, dependence on, 142 decentralized autonomous corporations (DACs) and, 149–50 PayPal, 140–41 theft and, 142 direct public offerings (DPOs), 205–6 discount brokerages, 176–78 diversification, 208, 211 dividends, 113–14, 208–10 dividend traps, 113 Dorsey, Jack, 191–92 Draw Something, 192, 193 Drexler, Mickey, 116 dual transformation, 108–9 dumbwaiter effect, 19 Dutch East India Company, 71, 89, 131 eBay, 16, 26, 29, 45, 140 education industry, 95–97 Eisenhower administration, 52–53, 63, 75 Elberse, Anita, 28 employee-owned companies, 116–18 Enron, 133, 171n Eroski, 220 eSignal, 178 EthicalBay, 221 E*Trade, 176, 177 Etsy, 16, 26, 30 expense reduction, and corporate growth, 78–79 Facebook, 4, 31, 83, 93, 96, 201 data gathering and sales by, 41, 44 innovation by acquisition of startups, 78 IPO of, 192–93, 195 psychological experiments conducted on users by, 32–33 factors of production, 212–14 Fairmondo, 221 Family Assistance Plan, 63 family businesses, 103–4, 231–32 FarmVille, 192 favor banks, 161 Febreze Set & Refresh, 108 Federal Reserve, 137–38 feedback loop, and positive reinforcement, 28 Ferriss, Tim, 201 feudalism, 17 financial services industry, 131–33, 171–73, 175 Fisher, Irving, 158 flash crash, 180 flexible purpose corporations, 119–20 flow, investing in, 208–10 Forbes,88, 173, 174 40-hour workweek, reduction of, 58–60 401(k) plans, 171–74 Francis, Pope, 227, 228, 234 Free, Libre, Open Knowledge (FLOK) program, 217–18 Free (Anderson), 33 free money theory, local currencies based on, 156–59 barter exchanges, 159 during Great Depression, 158–59 self-help cooperatives, 159 stamp scrip, 158–59 tax anticipation scrip, 159 Wörgls, 157–58 frenzy, 98–99 Fried, Jason, 59 Friedman, Milton, 64 Friendster, 31 Frito-Lay, 80 front running, 180–81 Fulfillment by Amazon, 89 Fureai Kippu (Caring Relationship Tickets), 162 Future of Work initiative, 56n Gallo, Riso, 103–4 Gap, 116 Gates, Bill, 186 General Electric, 132 General Public License (GPL) for software, 216 Gesell, Silvio, 157 GI Bill, 99 Gimein, Mark, 147 Gini coefficient of income inequality, 81–82, 92 global warming, 135, 227–28, 237 GM, 80 Goldman Sachs, 133, 195 gold standard, 139 Google, 8, 48, 78, 83, 90–91, 93, 141, 218 acquisitions by, 191 business model of, 37 data sales by, 37, 44 innovation by acquisition of startups, 78 IPO of, 194–95 protests against, 1–3, 5, 98–99 grain receipts, 128 great decoupling, 53 Great Depression, 137, 158–59 Great Exhibition, 1851, 19 Greenspan, Alan, 132–33 growth, 1–11 bazaars, and economic expansion in late Middle Ages, 16–18 central currency and, 126, 129–31, 133–36 digital industrialism, growth agenda of, 14–15, 23–24 highly centralized e-commerce platforms and, 29 startups, hypergrowth expected of, 187–91 as trap (See growth trap) growth trap, 4–5, 68–123 central currency as core mechanism of, 133–34 corporations as program and, 68–82 platform monopolies and, 82–93, 101 recoding corporate model and, 93–97 steady-state enterprises and, 98–123 guaranteed minimum income programs, 62–65 guaranteed minimum wage public jobs, 65–66 guilds, 17 Hagel, John, 76–77 Hardin, Garrett, 215–16 Harvard Business Review,108–9 Heiferman, Scott, 196–97 Henry VIII, King, 215, 229 Hewlett-Packard UK, 112 high-frequency trading (HFT), 179–80 Hilton, 115 Hobby Lobby case, 72 Hoffman, Reid, 61 Holland, Addie Rose, 205–6 holograms, 235 Homeport New Orleans, 121 housing industry, 135 Huffington, Arianna, 34, 35, 201 Huffington Post, 34, 201 human role in economy, 13–67 aristocracy’s efforts to control peasant economy, 17–18 bazaars and, 16–18 big data and, 39–44 chartered monopolies and, 18 decreasing employment and, 30–39 digital marketplace, impact of, 24–30 industrialism and, 13–16, 18–24, 44 “likes” economy and, 30–39 reevaluation of employment and adopting policies to decrease it and, 54–67 sharing economy and, 44–54 Hurwitz, Charles, 117 IBM, 90–91, 112 inclusive capitalism, 111–12 income disparity corporate model and, 81–82 digital technology as accelerating, 53–54 Gini coefficient of, 81–82, 92 growth trap and, 4 power-law dynamics and, 27–28, 30 public service options for reducing, 65–66 IndieGogo, 30, 199 individual retirement accounts (IRAs), 171 industrial farming, 134–35 industrialism, 18–24 branding and, 20 digital, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 disempowerment of workers and, 18–19 human connection between producer and consumer, loss of, 19–20 isolation of human consumers from one another and, 20–21 mass marketing and, 19–20 mass media and, 20–21 purpose of, 18–19, 22 value system of, 18–19 inflation, 169 Instagram, 31 Intercontinental Exchange, 182 interest, 129–31 investors/investing, 70, 72, 168–223 algorithmic trading and, 179–84 bounded, 210–15 commons model for running businesses and, 215–23 crowdfunding and, 198–201 derivative finance, volume of, 182 digital technology and, 169–70, 175–84 direct public offerings (DPOs) and, 205–6 discount brokerages and, 176–78 diversification and, 208, 211 dividends and, 208–10 flow, investing in, 208–10 high-frequency trading (HFT) and, 179–80 in low-interest rate environment, 169–70 microfinancing platforms and, 202–4 platform cooperatives and, 220–23 poor performance of do-it-yourself traders and, 177–78 retirement savings and, 170–75 startups and, 184–205 ventureless capital and, 196–205 irruption, 98 i-traffic, 196 iTunes, 27, 29, 34, 89 J.
Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World by Bruce Schneier
23andMe, 3D printing, autonomous vehicles, barriers to entry, bitcoin, blockchain, Brian Krebs, business process, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, Donald Trump, drone strike, Edward Snowden, Elon Musk, fault tolerance, Firefox, Flash crash, George Akerlof, industrial robot, information asymmetry, Internet of things, invention of radio, job automation, job satisfaction, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, move fast and break things, national security letter, Network effects, pattern recognition, profit maximization, Ralph Nader, RAND corporation, ransomware, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, Stanislav Petrov, Stephen Hawking, Stuxnet, The Market for Lemons, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, web application, WikiLeaks, zero day
Dudley (17 May 2016), “Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records,” Scientific Reports 6, no. 26094, https://www.nature.com/articles/srep26094. 83But although the system works: Will Knight (11 Apr 2017), “The dark secret at the heart of AI,” MIT Technology Review, https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai. 83A 2014 book, Autonomous Technologies: William Messner, ed. (2014), Autonomous Technologies: Applications That Matter, SAE International, http://books.sae.org/jpf-auv-004. 84One research project focused on: Anh Nguyen, Jason Yosinski, and Jeff Clune (2 Apr 2015), “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” in Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’15), https://arxiv.org/abs/1412.1897. 84A related research project was able: Christian Szegedy et al. (19 Feb 2014), “Intriguing properties of neural networks,” in Conference Proceedings: International Conference on Learning Representations (ICLR) 2014, https://arxiv.org/abs/1312.6199. 84Yet another project tricked an algorithm: Andrew Ilyas et al. (20 Dec 2017), “Partial information attacks on real-world AI,” LabSix, http://www.labsix.org/partial-information-adversarial-examples. 85Like the Microsoft chatbot Tay: James Vincent (24 Mar 2016), “Twitter taught Microsoft’s AI chatbot to be a racist asshole in less than a day,” Verge, https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist. 85In 2017, Dow Jones accidentally: Timothy B. Lee (10 Oct 2017), “Dow Jones posts fake story claiming Google was buying Apple,” Ars Technica, https://arstechnica.com/tech-policy/2017/10/dow-jones-posts-fake-story-claiming-google-was-buying-apple. 85Within minutes, a trillion dollars: Bob Pisani (21 Apr 2015), “What caused the flash crash? DFTC, DOJ weigh in,” CNBC, https://www.cnbc.com/2015/04/21/what-caused-the-flash-crash-cftc-doj-weigh-in.html. 85in 2013, hackers broke into the Associated Press’s: Edmund Lee (24 Apr 2013), “AP Twitter account hacked in market-moving attack,” Bloomberg, https://www.bloomberg.com/news/articles/2013-04-23/dow-jones-drops-recovers-after-false-report-on-ap-twitter-page. 85We should also expect autonomous: George Dvorsky (11 Sep 2017), “Hackers have already started to weaponize artificial intelligence,” Gizmodo, https://gizmodo.com/hackers-have-already-started-to-weaponize-artificial-in-1797688425. 85The Cyber Grand Challenge was similar: Cade Metz (6 Jul 2016), “DARPA goes full Tron with its grand battle of the hack bots,” Wired, https://www.wired.com/2016/07/__trashed-19. 85One program found: Matthew Braga (16 Jun 2016), “In the future, we’ll leave software bug hunting to the machines,” Vice Motherboard, https://motherboard.vice.com/en_us/article/mg73a8/cyber-grand-challenge.
In 2017, Dow Jones accidentally published a story about Google buying Apple. It was obviously a hoax, and any human reading it would have immediately realized it, but automated stock-trading bots were fooled—and stock prices were affected for two minutes until the story was retracted. That was just a minor problem. In 2010, autonomous high-speed financial trading systems unexpectedly caused a “flash crash.” Within minutes, a trillion dollars of stock market value was wiped out by unintended machine interactions, and the incident ended up bankrupting the company that caused the problem. And in 2013, hackers broke into the Associated Press’s Twitter account and falsely reported an attack on the White House. This sent the stock markets down 1% within seconds. We should also expect autonomous machine-learning systems to be used by attackers: to invent new attack techniques, to mine personal data for purposes of fraud, to create more believable phishing e-mails.
, 136 Electronic Privacy Information Center, 223 e-mail, 153 encryption, 109, 114, 169, 170–72 bypassing, 171, 193 by default, 197 end-to-end, 167, 170–71, 175 limiting, 197–99 as munition, 197 ubiquitous, 171–72, 199 warrant-proof, 194–95 end-to-end principle, 119 end users, 23, 130 Enron, 127, 128 EPA, formation of, 183 Equifax, 37, 79, 106, 124, 125, 128, 130, 180, 187 espionage: cyberespionage and cyberattack, 72, 81 international, 66–68, 71, 171–72 Estonia, national ID card of, 31, 48 ETERNALBLUE, 164–65 EU, regulations in, 184–88 European Safety and Security Engineering Agency, 149 Evans, Lord John, 196 FAA: database of near misses, 177 jurisdiction of, 145–46 Facebook, 190 censorship by, 60 controls exerted by, 61, 62 and EU regulation, 185, 186 identification systems in, 199 surveillance via, 57, 58, 169, 196 Fair Credit Billing Act (1974), 100 Fancy Bear (Russian intelligence unit), 46 Farook, Syed Rizwan, 174 FBI: backdoors demanded by, 172, 174, 193–97, 198, 220 and hacking back, 204 IMSI-catchers used by, 168–70 and law enforcement, 173–76 Microsoft vs., 190 wiretapping by, 168 FDA, 137, 145, 151 Federal Communications Commission (FCC), 149 FedRAMP, 123 Felten, Ed, 223 financial crisis (2008), 125–26 FinFisher, 64–65 FireEye, 42 flash crash, 85 Ford Foundation, 224 Fort Hood shooting (2009), 202 Freeh, Louis, 193 FTC, 148, 154 Gamma Group, 30, 65 Gartner tech analyst firm, 101 GDPR (General Data Protection Regulation) [EU], 151, 184–88 Geer, Dan, 163, 217 George, Richard, 170 Gerasimov Doctrine, 71 Germany, BSI and BND in, 173 GGE (Group of Governmental Experts), UN, 158 Gmail, 153 Goldsmith, Jack, 163 Google: Advanced Protection Program, 47 censorship by, 60 controls exerted by, 61, 62 and EU regulations, 185 identification systems in, 199 lobbying by, 154 state investigation of, 187 surveillance via, 58–59, 169, 196 governments, 144–59 asymmetry between, 91–92 censorship by, 60 and defense over offense, 160–79 functions of, 10 and industry, 176–79 information sharing by, 176 and infrastructure, 117 insecurity favored by, 57 international cooperation, 156–59 international espionage, 171–72 jurisdictional arbitrage, 156 and liability law, 128–33 lobbying of, 154–55 mistrust of, 208, 220 policy challenges in, 99, 100–101, 192–206 regulatory bodies, 121, 144, 150–52, 156–59, 192 and security standards, 167 supply-chain attacks on, 87–89 surveillance by, 64–68, 172, 195, 208 vulnerability disclosure by, 163 Greer, John, 126 GTT Communications, 115 Gutenberg, Johannes, 24 hacking: catastrophic, 9, 16, 217 class breaks, 33, 95 contests in, 85 costs of, 102–3 cyberweapons in, 73 increasing threat of, 79 international havens of, 156 through fish tank, 29 hacking back, 203–4 HackingTeam, 30, 45, 65 HAMAS, 93 Hancock Health, 74 harm, legal definition of, 130 Harris Corporation, 168 Hathaway, Melissa, 114 Hayden, Michael, 170 Healey, Jason, 158, 160 Heartbleed, 21, 114–15 Hello Barbie (doll), 106 Hilton Hotels, 185 Hizballah, 93 Honan, Mat, 29 Hotmail, 153 HP printers, 62 Huawai (Chinese company), 87 Human Rights Watch, 223 humans, as system component, 7 IBM, 33 iCloud, 7 hacking of, 78 and privacy, 190 quality standards for, 111, 123, 135 Idaho National Laboratory, 79, 90 identification, 51–55, 199–200 attribution, 52–55 breeder documents for, 51 impersonation of, 51, 75 identity, 44 identity theft, 50–51, 74–76, 106, 171 Ilves, Toomas Hendrik, 221 iMessage, 170 impersonation, 51, 75 IMSI (international mobile subscriber identity), 168–70 industry lobbying groups, 183 information asymmetries, 133–38 information security, 78 infrastructure: critical, use of term, 116 security of, 116–18 Inglis, Chris, 28 innovation, 155 insecurity, 56–77 cost of, 126 criminals’ benefit from, 74–77 and cyberwar, 68–74 insurance industry, 132–33 integrity, attacks on, 78–82 intellectual property theft, 66, 72–73, 75 interconnections, vulnerabilities in, 28–30, 90 International Organization for Standardization (ISO), 140 Internet: advertising model of, 57, 60 changing concepts of, 5, 218 connectivity of, 5, 91, 105–6 demilitarization of, 212–15 dependence on, 89–90 development phase of, 22–23, 157 explosive growth of, 5, 146 global, 7, 16, 161 governance model of, 157 government regulation of, 152–55 horizontal growth of, 146 industry standards for, 23, 122–23 lack of encryption on, 170–72 maintenance and upkeep of, 143 nonlinear system of, 211 private ownership of infrastructure, 126 resilience of, 210–12 as social equalizer, 214, 217 surveillance and control via, 64–68 viral dissident content on, 158 Internet+: authentication in, 49–51 coining of term, 8 cybersecurity safety board for, 177 risks and dangers of, 217–18 simultaneous vulnerabilities in, 94 Internet+ security: closing the skills gap, 141–42 correcting information asymmetries in, 133–38 correcting misaligned incentives in, 124–28 current state of, 9 defense in, see attack vs. defense enforcement of, 121 funding maintenance and upkeep in, 143 incentives and policy solutions for, 100–103, 120–43 increasing research in, 142–43 liabilities clarified for, 128–33 litigation for, 121 meanings of, 15–17 and privacy, 9 public education about, 138–41 public policies for, 120–21 standards for, 122–23, 140–41, 157–59 as wicked problem, 11, 99 Internet Engineering Task Force (IETF), 23, 167 Internet of Things (IoT), 5 as computerization of everything, 7 Cybersecurity Improvement Act, 180 in developmental stage, 8 patching of, 37–38 smartphone as controller hub for, 48 Internet Policy Research Initiative, MIT, 224 Investigatory Powers Act (UK), 195 iPhones, 3–4 encryption on, 174, 197 new versions of, 42–43 IPsec, 167 Iran: cyberattack by, 71, 116, 178 hackers in, 45 Stuxnet attack on, 79 Iraq, 212 ISIS, 69, 93 ISPs: connections via, 113–14 Tier 1 type, 115 ISS World (“Wiretappers’ Ball”), 65 jobs, in cybersecurity, 141–42 John Deere, 59–60, 62, 63 Joyce, Rob, 45, 53, 54, 164, 166 Kaplan, Fred, 73 Kaspersky Lab, 29, 74, 87 Kello, Lucas, 71 Kelly, John, 66 Keurig coffee makers, 62 key escrow, 194 KICTANet, Kenya, 214 labeling requirements, 134–35 LabMD, unfair practices of, 130–31 Landau, Susan, 175, 176, 223 Las Vegas shooting (2017), 202 Ledgett, Rick, 163–64, 166 lemons market, 134 Lenovo, 187 letters of marque, 204 Level 3 ISP, 115 liability law, 125, 128–33 Liars and Outliers (Schneier), 101, 209 Library of Congress, 42 license plate scanners, 201 linear systems, 210 Lloyd’s of London, 90 Lynn, William, 198 machine learning, 7, 82–87 adversarial, 84 algorithms beyond human comprehension, 111–12 autonomous, 82–83, 85 Maersk, 71, 94 malware, 26, 30, 196 man-in-the-middle attacks, 49, 169 market economics, and competition, 6 mass shootings, 202 May, Theresa, 197 McConnell, Mike, 198 McVeigh, Timothy, 202 medical devices: bugs in, 41 and government regulations, 151 hacking, 16 and privacy, 151 Meltdown vulnerability, 21 Merkel, Angela, 66 metadata, 174 Microsoft, 57, 190 Microsoft Office, new versions of, 42, 43 military systems, autonomous, 86 Minecraft video game, 94 miniaturization, 7 Mirai botnet, 29, 37, 77, 94, 130 money laundering, 183 monocultures, vulnerabilities in, 31 Moonlight Maze, 66 “movie-plot threats,” 96 Mozilla, 163 Munich Security Conference, 70 My Friend Cayla (doll), 106 Nader, Ralph, Unsafe at Any Speed, 182 National Cyber Office (NCO), 146–50 National Cyber Security Centre (UK), 173 National Cybersecurity Safety Board (proposed), 177 National Institute of Standards and Technology (NIST), Cybersecurity Framework of, 123, 147 National Intelligence Council, 211–12 National Science Foundation (NSF), 147 National Security Council, 163 National Security Strategy, 117 National Transportation Safety Board, 177 Netflix, 148 net neutrality, 61, 119 network effect, 60 networks: “air gapped,” 118 collective action required of, 23–24 end-to-end model of, 23 firewalls for, 102 iCloud, 111 secure connections in, 113–14, 125 and spam, 100 telephone, 119 New America, 223 New York Cyber Task Force, 213 NOBUS (nobody but us), 164–65, 169, 170 norms, 157–59 North Korea: cyberattack by, 71 cybercrimes by, 76, 157 hacking by, 54, 71, 78 threats by, 70, 72 Norwegian Consumer Council, 105–6 NotPetya malware, 71, 77, 89, 94 NSA: attribution in, 53–55 BULLRUN program, 167–68 credential stealing by, 45 cyberattack tools of, 165–67 on cybersecurity, 86 cyberweapons stolen from, 73 disclosing and fixing vulnerabilities, 162–67 encryption circumvented by, 171, 193 intelligence-gathering hacks by, 116, 118 missions of, 160–61, 172 mistrust of, 208 reorganization (2016) in, 173 and security standards, 167–70 splitting into three organizations, 172–73 supply-chain attacks by, 87 surveillance by, 65, 66–67, 190, 202 NSO Group, 65 Nye, Joseph, 157 Obama, Barack, 66, 69, 92, 117, 163, 180, 208 Ochoa, Higinio O.
Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, longitudinal study, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game
Other systems specialize in finding arbitrage opportunities within or between markets, or in high-frequency trading that seeks to profit from minute price movements that occur over the course of milliseconds (a timescale at which communication latencies even for speed-of-light signals in optical fiber cable become significant, making it advantageous to locate computers near the exchange). Algorithmic high-frequency traders account for more than half of equity shares traded on US markets.69 Algorithmic trading has been implicated in the 2010 Flash Crash (see Box 2). * * * Box 2 The 2010 Flash Crash By the afternoon of May, 6, 2010, US equity markets were already down 4% on worries about the European debt crisis. At 2:32 p.m., a large seller (a mutual fund complex) initiated a sell algorithm to dispose of a large number of the E-Mini S&P 500 futures contracts to be sold off at a sell rate linked to a measure of minute-to-minute liquidity on the exchange. These contracts were bought by algorithmic high-frequency traders, which were programmed to quickly eliminate their temporary long positions by selling the contracts on to other traders.
Possible impacts from genetic selection in different scenarios 7. Some strategically significant technology races 8. Superpowers: some strategically relevant tasks and corresponding skill sets 9. Different kinds of tripwires 10. Control methods 11. Features of different system castes 12. Summary of value-loading techniques 13. Component list List of Boxes 1. An optimal Bayesian agent 2. The 2010 Flash Crash 3. What would it take to recapitulate evolution? 4. On the kinetics of an intelligence explosion 5. Technology races: some historical examples 6. The mail-ordered DNA scenario 7. How big is the cosmic endowment? 8. Anthropic capture 9. Strange solutions from blind search 10. Formalizing value learning 11. An AI that wants to be friendly 12. Two recent (half-baked) ideas 13. A risk-race to the bottom CHAPTER 1 Past developments and present capabilities We begin by looking back.
After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to post facto cancellation under existing trade rules).70 The retelling here of this episode is a digression because the computer programs involved in the Flash Crash were not particularly intelligent or sophisticated, and the kind of threat they created is fundamentally different from the concerns we shall raise later in this book in relation to the prospect of machine superintelligence. Nevertheless, these events illustrate several useful lessons. One is the reminder that interactions between individually simple components (such as the sell algorithm and the high-frequency algorithmic trading programs) can produce complicated and unexpected effects.
The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman
affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, blockchain, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, obamacare, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, quantitative hedge fund, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine
Bradley Hope, “Five Ways Quants Are Predicting the Future,” Wall Street Journal, April 1, 2015, https://blogs.wsj.com/briefly/2015/04/01/5-ways-quants-are-predicting-the-future. 10. Richard Dewey, “Computer Models Won’t Beat the Stock Market Any Time Soon,” Bloomberg, May 21, 2019, https://www.bloomberg.com/news/articles/2019-05-21/computer-models-won-t-beat-the-stock-market-any-time-soon. 11. Aruna Viswanatha, Bradley Hope, and Jenny Strasburg, “‘Flash Crash’ Charges Filed,” Wall Street Journal, April 21, 2015, https://www.wsj.com/articles/u-k-man-arrested-on-charges-tied-to-may-2010-flash-crash-1429636758. 12. Robin Wigglesworth, “Goldman Sachs’ Lessons from the ‘Quant Quake,’” Financial Times, September 3, 2017, https://www.ft.com/content/fdfd5e78-0283-11e7-aa5b-6bb07f5c8e12.s 13. “Seed Interview: James Simons.” 14. Marcus Baram, “The Millionaire Critic Who Scared Facebook Now Wants to Help ‘Fix the Internet,’” Fast Company, December 11, 2018, https://www.fastcompany.com/90279134/the-millionaire-critic-who-scared-facebook-wants-to-help-fix-the-internet. 15.
For those reasons, there likely will remain pockets of the market where savvy traditional investors prosper, especially those focused on longer-term investing that algorithmic, computer-driven investors tend to shy away from. * * * = The rise of Renaissance and other computer-programmed traders has bred concern about their impact on the market and the potential for a sudden sell-off, perhaps sparked by computers acting autonomously. On May 6, 2010, the Dow Jones Industrial Average plummeted one thousand points in what came to be known as the “flash crash,” a harrowing few minutes in which hundreds of stocks momentarily lost nearly all their value. Investors pointed the finger at computer-programmed trading firms and said the collapse highlighted the destabilizing role computerized trading can play, but the market quickly rebounded. Prosecutors later charged a trader operating out of his West London home for manipulating a stock-market-index futures contract, laying the groundwork for the decline.11 To some, the sudden downturn, which was accompanied by little news to explain the move, suggested the rise of the machine had ushered in a new era of risk and volatility.
Hutton, 64 efficient market hypothesis, 111, 152, 179 Einhorn, David, 264, 309 Einstein, Albert, 27, 128 Elias, Peter, 90–91 email spam, 174 embeddings, 141 endowment effect, 152 Englander, Israel, 238, 252–54, 310 English, Chris, 298, 299 Enron, 226 Esquenazi, Edmundo, 17, 21, 38–39, 50 Euclidean Capital, 308 European Exchange Rate Mechanism, 165 European Union, 280–81 Evans, Robert, 128 Everything Must Go (movie), 270 Exxon, 132, 173 Facebook, 303–4, 318 facial dysplasia, 147 factor investing, 30, 132–33, 315 Farage, Nigel, 280–81 Farkas, Hershel, 34–35 Federalist Society, 290 Federal Reserve, 56–57, 59, 65, 151, 211 Fermat conjecture, 69–70 Ferrell, Will, 270 Fidelity Investments, 161–63 Fields Medal, 28 financial crisis of 2007–2008, 255–62, 263–64 financial engineering, 126 Financial Times, 229 First Amendment, 277 Fischbach, Gerald, 268 flash crash of 2010, 314 Food and Drug Administration, 206, 311 Fortran, 170 Fort Thomas Highlands High School, 88–89 fractals, 127 Franklin Electronic Publishers, 61 freediving, 239 Freedom Partners Action Fund, 278 Freifeld, Charlie, 38–39, 44, 67 Frey, Robert, 200, 240 at Kepler, 133, 157, 166–67, 180 Mercer and election of 2016, 302–3 at Morgan Stanley, 131, 132–33 statistical-arbitrage trading system, 131, 132–33, 157, 166–67, 186–90 Fried, Michael, 72 fundamental investing, 127–28, 161–63, 247, 310 game theory, 2, 88, 93 GAM Investments, 153–54 Gann, William D., 122–23 Gasthalter, Jonathan, 263 gender discrimination, 168, 168n, 176–77, 207 German deutsche marks, 52, 57–58, 110–11, 164–65 Geron Corporation, 310 ghosts, 111 gold, 3, 40, 57, 63–64, 116, 207 Goldman Sachs, 126, 133–34, 256 Goldsmith, Meredith, 176–77 Gone With the Wind (Mitchell), 88 Goodman, George, 124–25 Google, 48, 272–73 Gore, Al, 212 Graham, Benjamin, 127 Granade, Matthew, 312 Greenspan, Alan, 59 Griffin, Ken, 256, 310–11 Gross, Bill, 3, 163–64, 309 Grumman Aerospace Corporation, 56, 78 Gulfstream G450, 257, 267, 325 Hamburg, Margaret, 206 Hanes, 162 Harpel, Jim, 13–14, 283 Harrington, Dan, 297 Harvard University, 15, 17, 21–22, 23, 46–48, 173, 176, 185, 272 head and shoulders pattern, 123–24 Heritage at Trump Place, 278 Heritage Foundation, 278 Hewitt, Jennifer Love, 270 high-frequency trading, 107, 222–23, 271 Hitler, Adolph, 165, 282 holonomy, 20 Homma, Munehisa, 122 housing market, 224–25, 255, 261, 309 Hullender, Greg, 53–59, 74 human longevity, 276 IBM, 33, 37, 169, 171–79, 311 Icahn, Carl, 282 illegal immigrants, 290–91 information advantage, 105–6 information theory, 90–91 insider trading, 310 Institute for Defense Analyses (IDA), 23–26, 28–29, 30–32, 35, 46–49, 93–94 Institutional Investor, 218, 223 interest rates, 163–64, 224–25, 272–73 Internal Revenue Service (IRS), 227 Iraq, invasion of Kuwait, 116, 117 Israel, 184–85, 262 iStar, 26 Japanese yen, 49–50, 52–53, 54–55, 65 Jean-Jacques, J.
Planet Ponzi by Mitch Feierstein
Affordable Care Act / Obamacare, Albert Einstein, Asian financial crisis, asset-backed security, bank run, banking crisis, barriers to entry, Bernie Madoff, break the buck, centre right, collapse of Lehman Brothers, collateralized debt obligation, commoditize, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, disintermediation, diversification, Donald Trump, energy security, eurozone crisis, financial innovation, financial intermediation, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, high net worth, High speed trading, illegal immigration, income inequality, interest rate swap, invention of agriculture, light touch regulation, Long Term Capital Management, low earth orbit, mega-rich, money market fund, moral hazard, mortgage debt, negative equity, Northern Rock, obamacare, offshore financial centre, oil shock, pensions crisis, plutocrats, Plutocrats, Ponzi scheme, price anchoring, price stability, purchasing power parity, quantitative easing, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, too big to fail, trickle-down economics, value at risk, yield curve
Our weak-willed regulators know these things but aren’t taking the forceful steps that would be needed to control them.28 In short, if that ‘flash crash’ could happen in 2010, it could happen again now: the markets of 2011 and 2012 remain highly vulnerable. As a matter of fact, the same thing already has happened again, and worse. In November 2010, the sugar market saw a 20% collapse in prices over two days. Cotton prices are also exceptionally volatile.29 The same has been true of cocoa futures.30 By good fortune, none of these flash crashes have yet caused much damage, but poorly maintained levees didn’t do much harm to New Orleans until 2005. The mortgage market looked to be working fine, until it came close to destroying the international financial system. In 2010 we were fortunate that the flash crash happened when the markets were still being buoyed up by ultra-low interest rates, by quantitative easing, by massive fiscal stimulus, and by a broad sense of returning security in the financial markets.
In 2010 we were fortunate that the flash crash happened when the markets were still being buoyed up by ultra-low interest rates, by quantitative easing, by massive fiscal stimulus, and by a broad sense of returning security in the financial markets. Those props (disastrous as they’re proving in the longer run) were enough to stop the meltdown. But just suppose the next crash happens when another major financial institution is on the brink. When nerves are shredded. When panic is only half a rumor away. Under these circumstances, a flash crash could easily precipitate failure on a Lehman-like scale. And, with some minor exceptions, all the circumstances which allowed the market to fail in 2010 are still in place today, some of them to an even greater extent. Disaster lies just round the corner. It’s extraordinary that regulators do not pursue these matters more aggressively. Take the relatively simple matter of the $128 billion worth of failed trades each day.
AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott
Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional
Joshua Klein, “When Big Data Goes Bad,” Fortune, November 5, 2013, http://fortune.com/2013/11/05/when-big-data-goes-bad/. 17. Catherine Talbi, “‘Keep Calm and Rape’ T-Shirt Maker Shutters After Harsh Backlash,” Huffington Post, June 25, 2013, https://www.huffingtonpost.com/2013/06/25/keep-calm-and-rape-shirt_n_3492411.html. 18. Silla Brush, Tom Schoenberg, and Suzi Ring, “How a Mystery Trader with an Algorithm May Have Caused the Flash Crash,” Bloomberg News, April 21, 2015, https://www.bloomberg.com/news/articles/2015-04-22/mystery-trader-armed-with-algorithms-rewrites-flash-crash-story. 19. J. Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature 457 (February 19, 2009): 1012–14. 20. D. Lazer et al., “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343 (March 14, 2014): 1203–5. 21. D. R. Olson etal., “Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales,” PLOS Computational Biology 9, no. 10 (2013), https://doi.org/10.1371/journal.pcbi.1003256. 22.
It turned out that two algorithms, run by two different book sellers, had gotten into an inverse bidding war, under poor assumptions about the behavior of other sellers.16 • An online clothing retailer called Solid Gold Bomb created an algorithm that automatically made new designs for print-on-demand T-shirts, based on inserting random phrases into popular slogans, such as “Keep Calm and Carry On.” Because of poor oversight, the company ended up accidentally advertising T-shirts emblazoned with terrible misogynistic phrases, including ones about sexual assault. It was a traumatic experience for many who encountered the designs online, and the company went out of business because of the backlash.17 • On May 6, 2010, U.S. stocks experienced a “Flash Crash,” in which the market lost a trillion dollars of value in a matter of minutes—all because of algorithms gone wrong. According to the U.S. Department of Justice, a rogue trader based in London had submitted $200 million worth of “spoof” transactions that were modified 19,000 times over a very short period, before ultimately being withdrawn. This created a feigned sense of market pessimism about certain stocks.
Humble Pi: A Comedy of Maths Errors by Matt Parker
8-hour work day, Affordable Care Act / Obamacare, bitcoin, British Empire, Brownian motion, Chuck Templeton: OpenTable:, collateralized debt obligation, computer age, correlation does not imply causation, crowdsourcing, Donald Trump, Flash crash, forensic accounting, game design, High speed trading, Julian Assange, millennium bug, Minecraft, obamacare, orbital mechanics / astrodynamics, publication bias, Richard Feynman, Richard Feynman: Challenger O-ring, selection bias, Tacoma Narrows Bridge, Therac-25, value at risk, WikiLeaks, Y2K
And let’s be honest: finance is not the only situation where poorly written code can cause problems. Bad code can cause problems almost anywhere. Automatic-trading algorithms get extra interesting in a financial setting when they start to interact. Allegedly, the complex web of algorithms all trading between themselves should keep the market stable. Until they get caught in an unfortunate feedback loop and a new financial disaster is produced: the ‘flash crash’. On 6 May 2010 the Dow Jones Index plummeted by 9 per cent. Had it stayed there, it would have been the biggest one-day percentage drop in the Dow Jones since the crashes of 1929 and 1987. But it didn’t stay there. Within minutes, prices bounced back to normal and the Dow Jones finished the day only 3 per cent down. After a bumpy start to the day, the crash itself happened between 2.40 p.m. and 3 p.m. local time in New York.
Over twenty thousand trades were at prices more than 60 per cent away from what the stock was worth at 2.40 p.m. And many of these trades were at ‘irrational prices’ as low as $0.01 or as high as $100,000 per share. The market had suddenly gone mad. But then, almost as quickly, it got a hold of itself and returned to normal. A burst of extreme excitement which ended as fast as it started, it was the Harlem Shake of financial crashes. People are still arguing about what caused the flash crash of 2010. There were accusations of a ‘fat finger’ error, but no evidence of this has come to light. The best explanation I can find is the official joint report put out by the US Commodity Futures Trading Commission and the US Securities and Exchange Commission on 30 September 2010. Their explanation has not been universally accepted but I think it’s the best we’ve got. It seems that a trader decided to sell a lot of ‘futures’ on a Chicago financial exchange.
Advertising Standards Authority: 198.64179–199.41791 Air Canada: 85.20896–85.23881, 87.41791–88.20896 Air Force: 69.00000–73.94216, 286.62687–287.50746 air traffic control: 302.95522–304.00000 Ariane rocket: 20.41791, 26.56530–27.26866, 29.80597–30.59515 attractiveness scores: 68.35821–69.53731 average Australian: 74.62687–75.95522, 77.56716–77.82090 Avery Blank: 258.02985–258.17910 Benford’s Law: 36.80597–40.95709 Big data: 259.62687–259.74627 big enough: 27.17910–27.47761, 40.50746, 73.14925, 171.20896, 196.02799, 303.08955–304.44776, 313.86567 big number: 47.74627, 203.55224, 253.77612, 289.05970, 310.00000–311.72948, 313.80597 Bill Gates: 141.11940, 160.05970 billion seconds: 290.23881–291.44776, 310.23881–310.41791 binary number: 34.29851, 179.38806–180.50746, 182.35821, 185.35821, 189.41791, 191.08955–191.53731, 246.23881, 250.05970–251.92537, 290.35821, 292.65672–292.80597, 301.00000 brewing beer: 147.89552, 149.59701 Brian Test: 258.02985–258.86567 Casio fx: 11.74627, 175.00000 cause problems: 81.23881, 122.29851–122.56716, 144.20896, 249.56716 CEO pay: 131.53731–131.62687, 133.29851–133.41791 cheese slices: 4.74627, 102.95709 classic football: 235.80597, 238.44776 clay tablets: 149.50746–150.89552 clocks going: 112.08955–114.71642 clockwise anticlockwise: 216.26866 Cluster mission: 29.68657–30.86381 computer code: 29.89552, 123.59701, 187.57836, 189.17910, 191.23881–192.68657, 250.14925, 256.56716, 261.80597, 289.53731, 302.38806–303.02985 constant width: 221.59701–222.86567 cot death: 167.56716–167.68657 crescent moon: 229.11940–231.92537 Datasaurus Dozen: 56.53731, 67.95709–68.92537 Date Line: 286.02985–286.56716 daylight saving: 64.11940–65.92537, 112.05970–114.83582 deliberately vague: somewhere in 7 to 10 and maybe 74 Dice O Matic: 52.20896–52.44776 diehard package: 34.00000–35.86567 Dow Jones: 125.29851, 143.00000–144.47761 drug trial: 62.47761–63.53731 electron beam: 184.05970, 186.53731–186.83582 expensive book: 141.14925–142.86567 explain why: 16.68657, 208.11940, 312.86567–312.89552 false positive: 64.62687, 154.74627, 247.08955, 252.50746, 276.86567, 301.20896, 308.26866–309.86567 fat fingers error: 143.44776, 150.11940 feedback loop: 144.35821, 268.08955–269.86567, 274.26866, 276.02985–277.80597 fence post problem: 208.83582–209.47761 Fenchurch Street: 280.20896, 282.35821–283.38806 fibre optic cable: 136.32836–136.89552, 138.08955 flash crash: 143.41791–144.38806 foot doughnut: 235.50933–235.71642 frigorific mixture: 92.44776–92.68657 fuel gauges: 85.05970–87.92537 full body workout: 213.71642–213.95522 functional sausage: 123.44776–123.47761 gene names: 247.00000–248.47761 Gimli Glider: 82.11940–83.68657 GOOD LUCK: 25.74627, 83.47761–84.83582, 288.74440 Gregorian calendar: 288.14925–288.41791, 293.08955–296.92537 Grime Dice: 160.56716–160.90672, 162.14925 Harrier Jet: 311.00000–313.65672 heart attacks: 64.11940–65.89552, 112.11940–114.92537 high frequency trading: 142.53731, 145.44776–146.71642 Hot Cheese: 1.92537, 4.59701 human brains: 149.05970, 159.00000, 266.29851, 308.02985–309.44776 Hyatt Regency: 264.80597, 267.95522 International Date Line: 286.02985–286.56716 JPMorgan Chase: 240.80597–241.68657 Julian calendar: 295.23881–297.59701 Julius Caesar: 206.32836–206.89552, 293.80597, 297.17910–298.56716 Kansas City: 264.80597–264.86567, 267.95522 lava lamps: 31.27612–32.82090 leap years: 206.29851, 288.47761, 295.26866, 297.00000–298.86567 Lego bricks: 201.05970–202.95522 long enough: 55.77425–56.83582, 99.20896–99.26866, 171.08955, 194.50746, 246.86567, 303.44776 Los Angeles: 255.17910–255.65672, 302.20896, 305.53731 magic squares: 6.11940–7.89552 Mars Climate Orbiter: 96.80410–97.87873 maths error: 9.91418, 11.29851, 28.56716, 79.62687, 97.75933, 147.50746, 175.00000, 181.56716, 245.68657, 304.29851–305.86567 maths mistake: 7.35821–8.11940, 89.65672, 149.80597, 174.11940–175.65672, 181.59701, 206.65672–206.71642, 210.14925, 259.71642, 264.41791, 300.08955, 305.47761, 308.74627–308.95522 McChoice Menu: 197.02985, 199.44776–200.00000 Millennium Bridge: 269.68657–269.83582, 274.26866–277.41791, 280.00000–281.68657 mobile phone masts: 57.19403–59.92537 most important: 6.00000, 190.62687 Mr Average: 74.14925–75.53731 NBA players: 166.17910–166.44776 non transitive dice: 141.17910, 160.00000–162.14925 non zero: 157.35821, 183.20896, 185.17910, 192.74627 Null Island: 254.50746–255.95522 null meal: 197.74627, 199.26866–200.50746 oddly specific: 117.2089552238806, 139.1194029850746, 190.5074626865672, 245.7462686567164 off-by-one errors: 206.53731, 209.65672–211.17910 Olympic Games: 293.26866, 300.14925 Parker Square: 5.05970–6.89552 Penney Ante: 163.32836–163.38806 Pepsi Points: 311.69963–313.71642 phone masts: 57.19403–59.92537 plug seal: 100.90672 Pseudorandom number generators: 43.74627–44.44776, 47.56716–48.86567 punch cards: 73.74627–73.77612, 75.08955–76.74627 real world: 38.34328–40.23881, 121.38806, 123.80597, 145.95522, 208.02985, 241.26866, 244.53731–245.95522 resonant frequencies: 269.68657–269.74627, 274.23881–280.95522 Richard Feynman: 154.95522, 222.00000–223.29851 rm -rf: 23.08955–23.47761 Rock Paper Scissors: 163.50746–163.71642 roll over errors: 25.38806, 180.32836, 184.53731, 190.26866–191.20896 Royal Mint: 215.92537–216.85075 salami slicing: 121.35821–123.80597 salt mines: 232.00000–233.80597 scientific notation: 249.32836–250.77612, 252.00000–253.83582 Scud missile: 179.00000–181.65672 sea level: 89.17910–89.83582 seemingly arbitrary: 53.20896, 190.38806, 296.86567, 302.23881 Sesame Street: 230.00000–230.26866 should open: 225.53731–225.62687, 227.41791–227.44776 skin tight garments: 73.23881–73.26866 something else: 57.29851, 64.65672, 247.02985, 270.11940, 301.14925 Space Invaders: 19.59701–21.83396 space shuttle: 4.10448–4.34328, 152.26866–153.50746, 223.14925–223.35821, 230.80597–230.89552 SQL injection: 250.11940, 256.02985 standard deviation: 34.35821–34.53731, 66.93097–68.83582, 73.65672, 132.86381 Steve Null: 258.00000–259.80597, 261.47761 Stock Exchange: 123.71642, 125.08955–125.65672, 145.29851–145.62687, 150.26866–151.92351 stock options: 131.14925–133.92537 street signs: 233.29851–234.38806, 238.35634–238.50746 survivor bias: 13.89552, 21.32836, 65.53731–66.77612 Swiss Cheese model: 4.56716–4.83582, 13.23881, 103.80597 synchronous lateral excitation: 276.38806–277.38806, 280.62687 T shirt: 5.00000–5.02985, 313.41791–313.62687 tabulating machines: 73.74627, 75.08955–76.65672 Tacoma Narrows Bridge: 268.47761–270.29851 tallest mountain: 115.11940–116.89552 tax return: 36.71642–36.80597, 38.61194, 41.05970–42.92537 Therac machine: 13.00000, 185.05970–185.80597 three cogs: 214.00000–215.74813, 219.02985–220.35821 Tokyo Stock Exchange: 125.38806, 150.26866–151.92351 torsional instability: 268.17910–271.89552 trading algorithms: 138.77612, 142.53731–142.74627, 144.23881–146.68657 Traffic Control: 302.20896–305.68657 Trump administration: 128.17910–129.89552 UK government: 52.47761, 233.23881–234.83582, 238.86567, 256.44776 UK lottery: 155.38806–155.86567, 159.41791–159.92537, 308.08955–308.14925 UK street signs: 238.35634–238.50746 US army: 179.77612, 181.68657–181.71642 USS Yorktown: 175.20896–175.77425 Vancouver Stock Exchange: 123.71642, 125.08955–125.65672 waka waka: 189.95709 went wrong: 27.62687, 29.80597, 84.41791, 134.80597–134.92537, 145.77612, 265.20896, 267.50746, 280.62687, 286.65672, 305.89552 Wobbly Bridge: 97.66978, 280.11940–280.41791 Woolworths locations: 60.00000–60.62687 world record: 119.11940–121.77612, 135.35261, 298.77612 wrong bolts: 99.02985–99.74627, 101.17910–102.70149, 104.77612 X rays: 185.86567–186.80597 THE BEGINNING Let the conversation begin … Follow the Penguin twitter.com/penguinukbooks Keep up-to-date with all our stories youtube.com/penguinbooks Pin ‘Penguin Books’ to your pinterest.com/penguinukbooks Like ‘Penguin Books’ on facebook.com/penguinbooks Listen to Penguin at soundcloud.com/penguin-books Find out more about the author and discover more stories like this at penguin.co.uk ALLEN LANE UK | USA | Canada | Ireland | Australia India | New Zealand | South Africa Allen Lane is part of the Penguin Random House group of companies whose addresses can be found at global.penguinrandomhouse.com.
Who Gets What — and Why: The New Economics of Matchmaking and Market Design by Alvin E. Roth
Affordable Care Act / Obamacare, Airbnb, algorithmic trading, barriers to entry, Berlin Wall, bitcoin, Build a better mousetrap, centralized clearinghouse, Chuck Templeton: OpenTable:, commoditize, computer age, computerized markets, crowdsourcing, deferred acceptance, desegregation, experimental economics, first-price auction, Flash crash, High speed trading, income inequality, Internet of things, invention of agriculture, invisible hand, Jean Tirole, law of one price, Lyft, market clearing, market design, medical residency, obamacare, proxy bid, road to serfdom, school choice, sealed-bid auction, second-price auction, second-price sealed-bid, Silicon Valley, spectrum auction, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, The Wealth of Nations by Adam Smith, two-sided market, uber lyft, undersea cable
A famous example, in which high-speed trading of ES futures and SPY exchange-traded funds was implicated, is the “flash crash” of 2010. In just four minutes, the prices of futures and of the related SPY exchange-traded funds (as well as many of the stocks in the index) were driven down by several percentage points—a very big move, in the absence of earth-shattering news—and then recovered almost as fast. A subsequent investigation by the Securities and Exchange Commission and the Commodity Futures Trading Commission suggested that this brief distortion resulted from high-speed computer algorithms trading with one another, at a speed that eluded human supervision, and briefly spun out of control before anyone could react. In the aftermath of this flash crash, there was added confusion involving order backlogs and incorrect time stamps that made it difficult to determine which trades had actually gone through, since even some of the market computers had been left behind by the high-speed traders.
See also college admissions; residency programs for doctors; school matching in democracy, 166 early admissions in, 73–74 exploding offers in, 98–99 marriage age and, 72 Ph.D. offers in, 77–78 public value of, 125 Edwards, Valerie, 130 electronic order books, 83–84 Elias, Julio, 245 email, 169, 175–77 E-mini S&P 500 futures (ES), 82–89 equilibrium, 77 Ethiopia Commodity Exchange, 17–18 experimental economics, 77, 127–28, 176, 209, 244 experiments, 209, 213, 241 expert guides, 147–48 exploding offers, 9–10, 67, 98–99 empowerment of candidates and, 76–80 in gastroenterology fellowships, 76–78 for judicial clerkships, 91–99 in law firm recruiting, 67, 68 to medical residents, 136 for orthopedic surgeon fellows, 78–80 to Ph.D. candidates, 77–78 in school admissions, 73–74 exploitation, 203 failures, market abandonment of, 167 causes vs. symptoms of, 90–98 child marriage and, 70–74 from congestion, 92–93 cultural change and, 78–80 difficulty of limiting, 67–68, 74, 79–80, 90–98 from early transactions, 57–80 exploding offers and, 67–68 finding solutions for, 133–34 in gastroenterology fellowships, 75–78 in judicial clerkships, 69–70, 79, 90–98 in law firm recruiting, 65–68 in orthopedic surgeon hiring, 78–80 prevalence of, 73 safety, trust, and simplicity and, 113–30 self-control and, 67–68, 74–78 from speed, 81–99 fairness, 25 Falke, Roberta, 38–39 Farmer City, Illinois, 115 farmers’ markets, 20, 74 farming, 198 Federal Communications Commission, 186–89 Federal Law Clerk Hiring Plan, 93–98 fellowships, gastroenterology, 75–78 fertility tourism, 201–2 Fiesta Bowl, 61 financial markets, 82–89 flash crash of 2010, 84–85 Fleming, Alexander, 133–34 Florey, Howard, 134 Food Facility Inspection Report, 220–21 Football Bowl Association, 62–63 football bowl games, 59–65 Franklin, Benjamin, 200–201 Fréchette, Guillaume, 64, 237 free markets, 7, 12–13, 217, 226–28. See also markets and marketplaces FreeMarkets, 121–22 futures markets, 16–17, 82–89 Gale, David, 141–43, 158 game theory, 10–11 thought experiments in, 32–33 on trading cycles, 32–41 gaming the system, 10–11 banning markets and, 213–14 in Boston school choice, 126–30 in early transactions, 57–80 in New York City school system, 109–10, 153–55 in the Oklahoma Land Rush, 58–60 gastroenterology fellowships, 75–78 Google, 190–91 Android, 21–22 Great Recession (2008), 66 Green, Jerry, 3–4, 8 Green, Pamela, 3–4 Greiner, Ben, 118 gun ownership, 198 Hamlet (Shakespeare), 200 Hayek, Friedrich, 226–27 health care reimbursement, 206–7, 223–24 for kidney transplants, 51, 206–7, 208–10 health codes, 220–21 Hendren, Hardy, 138, 141 Hil, Garet, 45–46, 49 Hopwood, Shon, 97, 239 horsemeat, 195–97 Hoxby, Caroline, 126 human dignity, 207 IBM, 19 identity theft, 116 immune systems, 133–34 indentured servitude, 199–200 India, 201–2 industry standards, 22 information early transactions and missing, 60 importance of sharing all, 153–61 privacy and, 119–22 on qualifications and interest (See signals and signaling) reliable, 118–19 safety of sharing in Boston Public Schools, 122–28 in clearinghouses, 112 for kidney exchanges, 34, 36, 37, 47–49 market efficiency and, 119–21 for medical residencies, 137–43, 150–51 in New York City school system, 109–10, 112, 153–61 speed of, cotton market and, 89–90 in-kind exchanges, 202–5 Inquiry into the Nature and Causes of the Wealth of Nations, An (Smith), 206–7 insider trading, 48, 85 Institute for Innovation in Public School Choice, 165 interest charges, 200–201, 202, 205 Internet marketplaces, 7, 20–26 Airbnb, 99–103 congestion in, 99–106 dating sites, 72, 169, 175–77 eBay, 104–5, 116–21 payment systems in, 23–26 privacy and, 119–22 real estate, 224–25 reputation in, 115–16, 117–19 safety of, 105 signaling in, 169 targeted ads in, 189–92 thickness of, 105 trust in, 105 Uber, 103–4 Internet of Things, 101 iPhone, 21–22, 24 Iran, Islamic Republic of, 205–6 Iron Law of Marriage, 145 Islam, 200, 201, 205 iStopOver, 102 Japan college applications in, 171 exploding job offers in, 98–99 Jevons, William Stanley, 32 job markets.
Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky
algorithmic trading, asset allocation, automated trading system, backtesting, barriers to entry, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial intermediation, Flash crash, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, popular capitalism, prediction markets, price discovery process, profit motive, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve
As alpha quality is highly dependent on the magnitude of drawdowns, the ability to predict and avoid at least the largest negative shocks is essential. The predictive nature of the market microstructure Intraday Data in Alpha Research215 dynamics often can be useful for this purpose. According to Easley et al. (2011), the “flash crash” of May 6, 2010, was a good example of such a drawdown: in their results, they use the volume-synchronized probability of informed trading (VPIN) and measure the ramp-up of informed trading that caused liquidity providers to leave the market; this was already noticeable at least a week before the flash crash and had reached its highest level in the history of the E-mini S&P 500 contract just before the collapse. Similarly, Yan and Zhang (2012) document the largest spike in PIN in a decade during the first quarter of 2000, when the dot-com bubble peaked.
., and Subrahmanyam, A. (1998) “A Theory of Overconfidence, Self-Attribution, and Security Market Under- and Over-Reactions.” Journal of Finance 53: 1839–1885. Daniel, K. and Titman, S. (1999) “Market Efficiency in an Irrational World.” Financial Analysts Journal 55, no. 6: 28–40. Easley, D., Hvidkjaer, S., and O’Hara, M. (2002) “Is Information Risk a Determinant of Asset Returns?” Journal of Finance 57, no. 5: 2185–2221. Easley, D., Lopez de Prado, M., and O’Hara, M. (2011) “The Microstructure of the ‘Flash Crash’: Flow Toxicity, Liquidity Crashes, and the Probability of Informed Trading.” Journal of Portfolio Management 37, no. 2: 118–128. Fama, E. and French, K. (1992) “The Cross-Section of Expected Stock Returns.” Journal of Finance 47, no. 2: 427–466. Fama, E. and French, K. (1993) “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33, no. 1: 3–56. 276References Fama, E. and French, K. (2015) “A Five-Factor Asset Pricing Model.”
The Death of Money: The Coming Collapse of the International Monetary System by James Rickards
Affordable Care Act / Obamacare, Asian financial crisis, asset allocation, Ayatollah Khomeini, bank run, banking crisis, Ben Bernanke: helicopter money, bitcoin, Black Swan, Bretton Woods, BRICs, business climate, business cycle, buy and hold, capital controls, Carmen Reinhart, central bank independence, centre right, collateralized debt obligation, collective bargaining, complexity theory, computer age, credit crunch, currency peg, David Graeber, debt deflation, Deng Xiaoping, diversification, Edward Snowden, eurozone crisis, fiat currency, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, G4S, George Akerlof, global reserve currency, global supply chain, Growth in a Time of Debt, income inequality, inflation targeting, information asymmetry, invisible hand, jitney, John Meriwether, Kenneth Rogoff, labor-force participation, Lao Tzu, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market design, money market fund, money: store of value / unit of account / medium of exchange, mutually assured destruction, obamacare, offshore financial centre, oil shale / tar sands, open economy, plutocrats, Plutocrats, Ponzi scheme, price stability, quantitative easing, RAND corporation, reserve currency, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Satoshi Nakamoto, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, special drawing rights, Stuxnet, The Market for Lemons, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, trade route, undersea cable, uranium enrichment, Washington Consensus, working-age population, yield curve
Strangelove, dealt with nuclear-war-fighting scenarios between the United States and the Soviet Union. As portrayed in these films, neither side wanted war, but it was launched nonetheless due to computer glitches and actions of rogue officers. Capital markets today are anything but fail-safe. In fact, they are increasingly failure-prone, as the Knight Capital incident and the curious May 6, 2010, flash crash demonstrate. A financial attack may be launched by accident during a routine software upgrade or drill. Capital markets almost collapsed in 1998 and 2008 without help from malicious actors, and the risk of a similar collapse in coming years, accidental or malicious, is distressingly high. In 2011 the National Journal published an article called “The Day After” that described in detail the highly classified plans for continuity of U.S. government operations in the face of invasion, infrastructure collapse, or extreme natural disaster.
They morph in the same way a caterpillar turns into a butterfly, a process physicists call “self-organized criticality.” Social systems including capital markets are characterized by such self-organized criticality. One day the stock market behaves well, and the next day it unexpectedly collapses. The 22.6 percent one-day stock market crash on Black Monday, October 19, 1987, and the 7 percent fifteen-minute “flash crash” on May 6, 2010, are both examples of the financial system self-organizing into the critical state; at that point, it takes one snowflake or one sell order to start the collapse. Of course, it is possible to go back after the fact and find a particular sell order that, supposedly, started the market crash (an example of hunting for snowflakes). But the sell order is irrelevant. What matters is the system state
The result will be another systemic and unanticipated failure, larger than the Fed’s capacity to contain it. The panic’s immediate impact will be highly deflationary as assets, including gold, are dumped wholesale to raise cash. This deflationary bout will be followed quickly by inflation, as the IMF pumps out SDRs to reliquefy the system. System crashes. A fifth sign will be more frequent episodes like the May 6, 2010, flash crash in which the Dow Jones Index fell 1,000 points in minutes; the August 1, 2012, Knight Trading computer debacle, which wiped out Knight’s capital; and the August 22, 2013, closure of the NASDAQ Stock Market. From a systems analysis perspective, these events are best understood as emergent properties of complex systems. These debacles are not the direct result of banker greed, but they are the maligned ghost in the machine of high-speed, highly automated, high-volume trading.
Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu
algorithmic trading, asset allocation, automated trading system, backtesting, Black-Scholes formula, Brownian motion, business process, buy and hold, continuous integration, corporate governance, discrete time, distributed generation, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, principal–agent problem, random walk, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process
Recipe for disaster? Albuquerque J 2010. Demsetz H. The cost of transacting. Q J Econ 1968;82:33–53. Easley D, Lopez de Prado MM, O’Hara M. The microstructure of the ‘ﬂash crash:’ ﬂow toxicity, liquidity crashes and the probability of informed trading. J Portfolio Management 2011;37:118–128. Engle RE. The econometrics of ultra-high frequency data. Econometrica 2000;1:1–22. Garman M. Market microstructure. J Financ Econ 1976;3:257–275. Iati R. High frequency trading technology. TABB Group; 2009. Johnson J. Probability and statistics for computer science. John Wiley & Sons Inc.; Hoboken, NJ; 2003. Kirilenko A, Kyle A, Samadi M, Tuzun T. The ﬂash crash: the impact of high frequency trading on an electronic market. Working Paper 2011. Available at http://ssrn.com/abstract=1686004. Mandelbrot B, Hudson RL. The (mis) behavior of markets.
In either case, the high UHFT volume is an economic consequence of trader-agents attempting to exploit arbitrage opportunities in Handbook of Modeling High-Frequency Data in Finance, First Edition. Edited by Frederi G. Viens, Maria C. Mariani, and Ionuţ Florescu. © 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 235 236 CHAPTER 9 A Market Microstructure Model milliseconds—akin to gathering pennies at the rate of some 1000 times per second (1000 ms) or more. UHFT market activities have received critical attention in the analysis of the ‘‘ﬂash-crash’’ of May 6th, 2010, when the Dow Jones fell 573 points in several minutes only to recover 543 points in an even shorter time interval. Market regulators conﬁrmed the event initiated from the automated execution of a single large sell order at the Chicago Mercantile Exchange (CME): the sale of 75,000 E-mini S&P 500 futures contracts.1 The price drop gained momentum as a result of a colossal imbalance in buy/sell orders.
., xiii, 3 Finance, volatility and covolatility measurement/forecasting in, 243 Finance problems, methods used for, 68 Financial Accounting Standards Board (FASB), 53 Financial analysis, using boosting for, 47–74 Financial asset returns, computing covariance of, 263–264 Financial data, 176 behavior of, 202 GH distributions for describing, 165 Financial databases, 62 Financial events observations centered on, 107 probability curves for, 108 Financial market behavior, correlations in, 120 Financial mathematics model, 348 Financial mathematics, Black–Scholes model in, 352 Financial models, with transaction costs and stochastic volatility, 383–419 Financial perspective, 51, 55 Financial returns, 164, 216 Financial sector estimates, 150 Financial time series, 176 long-term memory effects in, 119 Finite-sample performance, via simulations, 14–17 Finite value function, 315, 322 Finite variance, 123 Fitted Gaussian distributions, 22 Fixed frequency, vs. high-low frequency, 208–212 Fixed-frequency approach, drawback of, 183–185 Fixed-frequency density, 210 Index Fixed-frequency method, 200 Fixed-point theorem, 391 applying, 398–399 existence based on, 397 Fixed portfolio/consumption processes, 308 Fixed rare event, favorable price movement for, 32 Fixed stopping time, 307, 308 Fixed time interval, 9 Fixed timescale, risk forecasts on, 176–185 ‘‘Flash-crash’’ of 2010, 236 Flat-top realized kernels, 261 Florescu, Ionu, xiii, 27, 97 Fluctuating memory effect, 145 Forecast horizon, monthly, 196–199 Forecasting of covariance, 280–285 of Fourier estimator properties, 272–285 of volatility, 273–275 Forecast pdfs, 209–210 Forecasts, conﬁdence intervals for, 187–188 Foreign stocks index, 128 Forward index level, calculating, 111–112 Fourier coefﬁcients, 247, 251–252 Fourier covariance estimator, ﬁnite sample properties of, 264 Fourier cutting frequency, 274 Fourier estimator(s), 244–245 asymptotic properties of, 248–250 cutting frequency and, 259–260 forecasting performance of, 245 forecasting properties of, 272–285 gains offered by, 245, 286 of integrated covariance, 263–272 of integrated volatility, 254, 252–263 microstructure noise and, 260–261, 274 of multivariate spot volatility, 246–252 of multivariate volatility, 266 performance of, 273 results of, 276–279 robustness of, 252–253 of volatility of variance and leverage, 250–252 427 Fourier estimator MSE (MSEF ), microstructure noise and, 256.
Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman
23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, commoditize, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, drone strike, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, lifelogging, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta analysis, meta-analysis, Minecraft, move fast and break things, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar
The AP tweet was revealed to be the result of a hack (a pro-Assad group called the Syrian Electronic Army claimed responsibility), and the news organization soon regained control over its account. The incident though showed how closely linked systems—Twitter and stock markets, realtime sentiment analysis and automated trading—can be easily gamed, especially when someone in control of a heavily followed Twitter account clicks on a suspicious link, giving control to an unscrupulous hacker. It wasn’t the first flash crash linked to automatic trading—that honor goes to the May 2010 Flash Crash, in which the Dow lost 1,000 points and swung back to equilibrium a few minutes later—but it was the first in which social media has played such an obvious role. Both Twitter and the AP were criticized for their lax security, and a few months later, Twitter introduced two-factor authentication, a security measure that should make such incidents less likely in the future.
See also hacks and hacking social factories, 264 social graph overview, 10–12 advertisers’ use of, 157 and algorithmic experimentation, 204–6 ambient awareness of others in, 50 and personal scrapbooking, 46 and reputation services, 194 social listening, 35–36, 216–17 social media overview, viii–x, 22, 160–61 and advertising, 23–24, 31–35, 148 and bots, 38–39 as community of choice, 257–58 and context collapse, 290–92 and flash crashes, 39–40 interrelating sites, 161, 246 intolerance of fakery and obfuscation, 74 and journalists, 97, 108, 148 labor markets compared to, 227 and oppressed classes, 5 and prosumption, 270–73 redesigning, 274–77 secondary orality, 63 surveillance on, 129, 133–34, 145–46 utopia predictions, 4–6, 7 See also digital serfdom; metrics; opting out; reputation; sharing economy social media outrage, 108 social media rebellion overview, 345–47, 361–62 devaluing your data, 349–55 digital bill of rights, 296, 365 by F.A.T., 359–62 links with limits on number of views, 358 OCR-proof typeface, 358 regulations allowing the right to be forgotten, 364 removing numbers from Facebook, 358–59 ScareMail browser extension, 359 taking time off, 336–41, 343–44, 347 willful acts of sabotage, 347–49 See also Crockford, Kade; identity obfuscation social media users behavior suggestions from virtual assistant, 42–43 commoditizing ourselves, 60–61 control of your content, 256–57, 258–59, 339 hierarchies in social networks, 53–55 media choices for consumption, 202 narcissism, 61–62 and photography, 55–60 politicians, 149 rating people, 190–92 recognizing when you’re done, 258 and reputation damage, 173 self-awareness, 136 stalkers, 78–82, 142 transparency of, 310 See also sharing social news overview, 101–2 advertorials, 116–17 churnalism, 103–7 curiosity gap headlines, 123–24 hyperbolic headlines, 127 and journalists, 127–28 and narcissism, 119–20 Upworthy, 102, 121–22, 125 See also BuzzFeed Social Roulette (F.A.T.), 360 Social Security number, 283 Social Sentiment Index, IBM, 38–39 social surveillance, 129, 133–34, 145–46 social value of privacy, 286 Soghoian, Christopher, 369 Solove, Daniel J., 286 Sontag, Susan, 55 Sony Entertainment Network, 221 sousveillance, 136–37 speech analytics, 40–43 sponsored content, 28, 31–32, 116–18.
The Permanent Portfolio by Craig Rowland, J. M. Lawson
Andrei Shleifer, asset allocation, automated trading system, backtesting, bank run, banking crisis, Bernie Madoff, buy and hold, capital controls, correlation does not imply causation, Credit Default Swap, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, high net worth, High speed trading, index fund, inflation targeting, margin call, market bubble, money market fund, new economy, passive investing, Ponzi scheme, prediction markets, risk tolerance, stocks for the long run, survivorship bias, technology bubble, transaction costs, Vanguard fund
The financial networks of the world are interconnected in a relatively fragile way. A problem in the trading systems of one firm can cause a cascade effect in the world markets. In spring of 2010 the U.S. stock markets experienced a “Flash Crash” during which the Dow Jones stock index sunk by 1,000 points within five minutes before quickly recovering. During this time, automated trading systems piled on sell orders, making the problem escalate quickly before finally coming back under control (some investors who had set stop losses in their accounts took large losses as they were automatically traded out of positions that recovered almost immediately and other bad trades were later backed out and canceled). The Flash Crash was reportedly caused by a combination of automated and high-frequency trading systems gone awry. However, if such an event can occur by accident, it could certainly occur again as a part of a deliberate attack.
See Banks and financial institutions Financial safety: bonds as source of budgeting for pleasure and career/profession providing income for cash as source of (see also Emergencies, cash or gold for) conservative investment approach for diversification for (see Diversification) future market prediction unreliability impacting gold as source of (see also Emergencies, cash or gold for) Golden Rules of international investments for investing vs. speculating for levels of protection for Permanent Portfolio implementation leverage avoidance for market timing challenges impacting past performance warnings for Permanent Portfolio providing personal vs. third-party decision making for portfolio creation for rebalancing creating risks vs. (see Risks) successful investing through tax-avoidance strategy warnings for understanding investments for Variable Portfolio speculation affordability and wealth protection as Firewall maintenance Fisch Coin Balance Fitch Group Flash Crash Flexibility Folio Investing Ford administration Foreign investments. See International investments 401(k) plans: bond investments in brokerage windows in cash investments in gold investments in stock investments in tax considerations with France, economy and investments in Fund manager risks: bond-related commercial Permanent Portfolio incurring gold-related institutional diversification to avoid Gabelli U.S.
The Euro and the Battle of Ideas by Markus K. Brunnermeier, Harold James, Jean-Pierre Landau
Affordable Care Act / Obamacare, asset-backed security, bank run, banking crisis, battle of ideas, Ben Bernanke: helicopter money, Berlin Wall, Bretton Woods, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, collective bargaining, credit crunch, Credit Default Swap, currency peg, debt deflation, Deng Xiaoping, different worldview, diversification, Donald Trump, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, financial deregulation, financial repression, fixed income, Flash crash, floating exchange rates, full employment, German hyperinflation, global reserve currency, income inequality, inflation targeting, information asymmetry, Irish property bubble, Jean Tirole, Kenneth Rogoff, Martin Wolf, mittelstand, money market fund, Mont Pelerin Society, moral hazard, negative equity, Neil Kinnock, new economy, Northern Rock, obamacare, offshore financial centre, open economy, paradox of thrift, pension reform, price stability, principal–agent problem, quantitative easing, race to the bottom, random walk, regulatory arbitrage, rent-seeking, reserve currency, road to serfdom, secular stagnation, short selling, Silicon Valley, South China Sea, special drawing rights, the payments system, too big to fail, union organizing, unorthodox policies, Washington Consensus, WikiLeaks, yield curve
Both institutions are headed by Klaus Regling. In early May 2010, it became apparent that financial support might be needed despite the fact that the treaty did not allow, and in fact prohibited, any balance of payments support from the EU budget to a euro-area member. There was a sense of urgency, and the drama of the new stage unfolded over several days. The nervousness was compounded by the “flash crash” on May 6, when high-frequency traders produced extraordinary volatility on US stock markets, with the Dow Jones index falling 300 points that day. That external event highlighted the possibility that a Greek crisis could produce a new Lehman-like meltdown. A leaders’ summit of euro-area countries took place on May 7, immediately followed by an exceptional Ecofin meeting on May 9–10. On May 7, Sarkozy had proposed that the leaders should simply instruct the ECB to buy sovereign bonds, declaring that “this is the moment of truth.”7 An additional element of drama in the Ecofin meeting came from a health scare for German finance minister Wolfgang Schäuble, who had been partly paralyzed after an assassination attempt twenty years before.
Securities Markets Programme The initiation of the Securities Markets Programme (SMP) in May 2010 triggered both publicity and dissent. On May 6, Trichet stated that the ECB was not considering buying bonds, but just four days later, on May 10, in the aftermath of the May 9 Ecofin meeting, he announced the Securities Purchase Program. That announcement was seen as an unprecedented reversal and a major surprise. The policy environment had suddenly changed with the “flash crash” on the New York Stock Exchange that took place on May 6, introducing a new element of market uncertainty. The 1,000 point fall in the Dow Jones Industrial Average was a chaotic computer-generated response to a single large trade in an atmosphere of nervousness created by the Greek crisis.38 The ECB wanted to act promptly, before the Eurogroup started to debate the issue, so as not to give the impression they were engaged in any sort of negotiation or trade-off with governments.
See United Kingdom English (language), 377 Erdogan, Recip, 261 Erhard, Ludwig, 62, 63, 258 ESBies (European safe bonds), 113–14, 224–26, 389 Eucken, Walter, 61, 62, 66 Eurasia, 286 Euribor interest rate, 169 euro (currency): different conceptions of, 3; ECB’s commitment to, 313; German objections to, 64–65; national debts in, 121; US critique of, 252–54 Eurobills, 113 Eurobonds, 111–14, 224–26 Eurogroup of Finance Ministers (Ecofin), 200 Euronomics group, 113–14, 224–25 European Banking Authority (EBA), 217, 254, 372 European banking charter, 389 European Central Bank (ECB), 12–13, 313–15; asset quality reviews by, 202; on bank bail-ins, 201; bank supervision by, 219–20; collateral policy of, 192–93; conditionality and, 331–43; countries exiting from, 228–29; current state of, 372–74; debts purchased by, 192; EFSM/ESM and, 127, 128, 131; before euro crisis, 315–25; during euro crisis, 17, 325–31; government bonds bought by, 216; Greece and, 233; as independent central bank, 93–94; intervention in Italy by, 116–17; lending and asset purchasing programs of, 343–46, 367; limited powers of, 157; Outright Monetary Transactions created by, 5, 123–25, 352–59; QE measure by, 114; Quantitative Easing by, 359–66; represented on IMF board, 296; Securities Markets Program of, 346–49; supervision of European banks by, 368–72; in troika, 25, 300–304; VLTROs of, 349–52 European Coal and Steel Community (ECSC), 71 European Commission, 17; Eurobonds proposal of, 113; during euro crisis, 18–19, 373; European Parliament election and, 36–37; powers of, 19–20; Stability and Growth Pact and, 148–49; in troika, 25, 300 European Council (of heads of states), 17; Brussels summit meeting of (2012), 217–19; ECB board appointed by, 316; on EFSF, 128; establishment of, 20; European Stability Mechanism created by, 27; Juncker as head of, 37 European Court of Justice (ECJ), 358–59 European Economic Community, 268 European Exchange Rate Mechanism (ERM), 79–82, 89–90, 106 European Financial Stability Facility (EFSF), 24, 26–27, 328, 348; Chinese purchase of bonds from, 280–81; Geithner on, 263; IMF and, 309 European Financial Stabilization Mechanism (EFSM), 26, 124, 328; ESM and, 130–31 European Insurance and Occupational Pensions Authority (EIOPA), 217 European Investment Bank (EIB), 34 European junior bonds (EJBs), 225 European Monetary Fund (proposed), 20, 24, 297 European Monetary System, 80 European Monetary Union (EMU), 94, 100, 101 European Parliament: elections of 2014 for, 36, 247, 276; Hollande and Merkel speaking at, 382 European People’s Party, 37, 271 European Recovery Program (Marshall Plan), 71, 250 European safe bonds (ESBies), 113–14, 224–26, 389 European Securities and Markets Authority (ESMA), 217 European Stability Mechanism (ESM), 24, 27, 114, 124, 217–18, 304 European Supervisory Authorities (ESAs), 217 European Systemic Risk Board (ESRB), 217, 372 European System of Financial Supervision (ESFS), 217 European Union: completing, 376; creation of, 65–66, 383; ECB’s presidents on, 374; Fiscal Compact among members of, 149; no-bailout clause applied to, 98; separate from euro area, 7; threatened British exit from, 271–79; Treaty on the Functioning of, 220 Eurosystem, 321–25 Evans Pritchard, Ambrose, 268 ex ante crisis prevention policies, 206–9 exchange rate channel, 187 exchange rates: Bretton Woods system for, 77–79; for Chinese currency, 259–60; European Exchange Rate Mechanism, 79–82; gold standard for, 76–77, 90; “original sin” in, 105–6; in trilemma of international macroeconomics, 75–76 exit risks, 226–29; Greek threats of, 229–33 ex post monetary policy, 193–94 exposure limits, 184 federalism, 43–48, 380 Federal Open Market Committee (US), 265 Federal Reserve System (US), 95; ECB compared with, 326; during financial crisis, 254, 314; interdistrict settlement account in, 323–24; mortgage-backed securities bought by, 191; political attacks on, 373; on price stability, 320 Fekter, Maria, 263 Feldstein, Martin, 8, 250; euro warnings of, 251–52 Fernández Ordóñez, Miguel Ángel, 94 financial crises, 173–75; as catalyst, 216–17; Federal Reserve System during, 254; fiscal policy and regulatory measures for management of, 194–206; management of, 386–88; mechanisms for handling, 175–85; monetary policy for management of, 185–94; preventing, 206–9; price of gold during, 223–24; short-term funding ending during, 169–70; See also global financial crisis financial dominance, 185, 205–6, 214, 388 financial frictions, 120 financial sector, 157–59; capital markets in, 162–66; interaction with states by, 182–83; interbank market in, 166–72; money creation by, 160–61; traditional banking in, 159–62 Finland, 128 Fiscal Compact (2012), 149, 153 fiscal dominance, 93 fiscal policy, 385–86; for management of financial crises, 185; regulatory measures and, 194–206 fiscal unions: capital movement and, 104–5; flexible exchange rates and, 105–6; as insurance against asymmetric shocks, 100–103; labor mobility and, 103–4; openness in, 105; as transfer unions, 106–11 Fischer, Stanley, 305 Fisher, Irving, 188, 308 Fitch (rating agency), 198 Fitoussi, Jean-Paul, 73 flash crash of NYSE, 25, 346–47 Flexible Credit Line (FCL), 310 Fourcade, Marion, 68 franc (French currency), 82 France: anti-austerity parties in, 38; on bank bailouts, 181–82, 184; banking and finances in, 48, 165–66; on British threat to leave EU, 274; centralism in, 43–44; cultural differences between France and, 41–43; on currency unions, 210–11; as debtor, 3; decree law in, 45–46; on ECB’s supervision of European banks, 370; economic debate between Germany and, 2, 5–9, 27–28, 375–76, 379–84; economic philosophy of, 97–98, 253–54; economic tradition in, 67–74; European Parliament elections in, 37; flexibility in economic philosophy of, 86–87, 94–95; during global financial crisis, 175; gold standard used by, 90, 91; on Greek exit, 233; historic differences between Germany and, 40–41; Hollande becomes president of, 33–34; on IMF board, 296; inflation in, 55; labor unions in, 52–53; liquidity in economic philosophy of, 116; nineteenth-century economic philosophy in, 56–59; on Schuldenbremse, 149–50; on Stability and Growth Pact, 29–30, 135; as Third Republic, 45; universal banking in, 159 Frankfurter Allgemeine Zeitung (FAZ; newspaper), 64 Frederick the Great (king, Prussia), 44–45 Freiburg School (economic theory), 61, 62 French Revolution, 257 Friedman, Milton, 63, 249; on economic stimulus, 367; on Keynesianism, 139; on Mexican bailout, 292; monetarism of, 188; monetary targeting proposed by, 53; plucking model by, 143 Fuest, Clemens, 359 Fund for the Orderly Restructuring of the Banking Sector (FROB; Spain), 196 funding liquidity, 161 Funk, Walther, 289 Gabriel, Sigmar, 64 Gauweiler, Peter, 359 GDP bonds, 115 Geithner, Tim: during Asian and Mexican crises, 262; on bailout of Greece, 1; on Chinese currency, 281; at European summit, 271; on expansion of European Financial Stability Facility, 263; on German economic surplus, 260; on Greek exit, 230; on saving euro, 252 Gemeinwirtschaft (communal economy), 60 Genscher, Hans-Dietrich, 81 German Bundesbank (German central bank).
Moneyland: Why Thieves and Crooks Now Rule the World and How to Take It Back by Oliver Bullough
banking crisis, Bernie Madoff, bitcoin, blood diamonds, Bretton Woods, BRICs, British Empire, capital controls, central bank independence, corporate governance, cryptocurrency, cuban missile crisis, dark matter, diversification, Donald Trump, energy security, failed state, Flash crash, Francis Fukuyama: the end of history, full employment, high net worth, if you see hoof prints, think horses—not zebras, income inequality, joint-stock company, liberal capitalism, liberal world order, mass immigration, medical malpractice, offshore financial centre, plutocrats, Plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, rent-seeking, Richard Feynman, risk tolerance, Sloane Ranger, sovereign wealth fund, WikiLeaks
This might not be such a problem if the only takers for Nevis’ services were rich Americans keen to hide their wealth from their fellow citizens. However, just as with Warburg’s eurobonds, the island’s peculiar trade draws in crooks and tyrants from all over the world. The evil money always mixes with the naughty money. Name a scam, any scam, as long as it’s complex and international, and it will involve somewhere like Nevis. Navinder Sarao, the British day trader convicted in 2016 for ‘spoofing’ the US markets in the ‘Flash Crash’ of 2010 (when the Dow Jones Industrial Average lost more than 600 points in minutes, at least partly because Sarao sent fake orders to drive down prices, temporarily wiping trillions of dollars off the value of US shares), diverted his profits into two Nevis-registered trusts, one of which he called the NAV Sarao Milking Markets Fund. In Britain’s biggest ever tax fraud, a group of conspirators made £100 million by duping celebrities into investing in bogus green technology.
INDEX Abacha, Ibrahim 132 Abacha, Maryam 129, 132 Abacha, Mohammed 132, 183 Abacha, Sani 100, 129, 132, 182, 183 Abercia, Ralph 78–9 Abercia, Ralph Jr 78–9 Ablyazov, Mukhtar 165 Abramovich, Roman 235 Achebe, Chinua 123–5, 126 Adada, Loujain 158–9, 160 advanced fee fraud 128–9 Afanasiev, Dmitry 137 Afghanistan 9, 11, 12, 269, 270 Africa 47, 120, 123, 128 see also individual countries al-Juffali, Walid 157–60, 161–4 al-Sanea, Maan 169 Alabama 95 Alamieyeseigha, Diepreye 87 Alison-Madueke, Diezani 165 Aliyev, Ilham 6 Allen, John 12 Alliance Trust Company 257–8, 262, 263–4 Allseas 80, 84 Andreski, Stanislav 122–3, 125–6 Angola 10, 11, 211–16 Anguilla 144, 145, 146–8, 276 Anthony, Kenny 161, 164 Antigua and Barbuda 154, 155, 156, 165 Anton (driver) 3–4, 8, 12 Arab Spring 7, 195 Ardern, Danielle 83 Argentina 228 arms smuggling 92, 148 asset protection 51–3, 70–1, 256–8 asset recovery 10, 182–95 Astaforova-Yatsenko, Nina 170 Astaforova-Yatsenko, Nonna 170 Astaphan, Dwyer 144, 145–6, 148 Astute Partners Ltd 76 Australia 166 Austria 149, 155 Autonomous Nation of Anarchist Libertarians (ANAL) 18 Aveiro 275 Azerbaijan 6, 11, 55, 57, 273–5 Bahamas 21, 46 Bailhache, Philip 64 Baku 6 Bank Commerciale pour l’Europe du Nord 66 Bank of New York 69 banks City of London 32–4, 36–7, 44, 45 eurobonds 39–43 eurodollars 34–5, 36 FATCA 248–9, 252–3 secrecy 245–53, 259–61, 270 and sources of funds 99–101 Switzerland 37–8, 242–9, 259–60 United States 31, 36, 44, 45 Baring, Rowland 31 Barnard, Bill 49–50 Barrington, Robert 174 Basseterre, St Kitts 56, 139 Bates, Robert 120 BBC 35 Bean, Elise 249 bearer bonds 39–43, 45 Beatles 31, 39 Beckwith, Tamara 157–8 Belgium 40 Belize 9, 148 Benson, Sir Richard 78–9 Benton, Jon 278 Berezniki 219–20 Berezovsky, Boris 169, 201, 203, 204, 205, 207 Berger, Henry 79, 80 Berger, Michael 131–2 Bermuda 186 Bhatia, Lal 80 Biden, Hunter 193 Biden, Joe 193 Bin Mahfouz, Khalid 175 Birkenfeld, Bradley 38, 242–6, 248, 253, 258, 260, 276 Blake, Mr Justice Nicholas 190, 191–2 ‘Bloody Money’ 170–2, 179, 188 Blum, Jack 55, 126–7 Blythe (Europe) Lid 76 BNP Paribas 187, 189 Bond, James 29–30, 32, 34 bonds 37, 39–43, 45 Bongo, Omar 132–3 Borisovich, Roman 17 Brantley, Mark 266–8 Brazil 9, 185, 228, 270 Bretton Woods system 27–34, 39, 43–5, 71, 272–3, 277 Brexit referendum 138, 271, 272, 278 Britain see United Kingdom British Virgin Islands 9, 19, 98, 102, 189, 214, 276 Browder, Bill 177–80 Bryant, Fitzroy 142–3, 153 Buffett, Warren 259 Burisma 188, 191, 193 Burns, George 264, 265 Caines, Richard 141 Cambridge University Press (CUP) 172–4, 179 Canada 138, 156, 178, 235 Cancer Institute, Ukraine 103–6, 108–11, 115–17 Candy Brothers 224 Cane Garden Services Ltd 19 Cantrade 61–2 Capitalism – A Love Story 237 Capone, Al 228 Cardin-Lugar amendment 277 Carter, Edwin see Litvinenko, Alexander Cash, Johnny 254 Catch-22 (Heller) 169 Cayman Islands 19, 99, 101, 102, 267 Central African Republic 165 Charles, Prince 221 Charlestown, Nevis 56–7 Chastanet, Allen 164 child abuse 62–4 Chile 240 Chiluba, Frederick 90 China 154, 231, 270 anti-corruption campaign 238, 239–40 flight capital 9, 181 and Japanese surrogacy 85–6 Christensen, John 61–2 Christian Aid 251, 252 Christophe Harbour, St Kitts 151–3 Citibank 99–100, 132–3 Citigroup 233 citizenship 20, 136–56, 251, 277 City of London 32–5, 36–7, 44, 252 eurobonds 38–42 Club K 215 Coales, Edwina 83 Cohen, Michal 216 Cole, Julia 196, 197 Colombia 228, 270 colonies 120, 122, 128, 144 Common Reporting Standard (CRS) 249–50, 251, 252, 259, 262, 264, 265, 266 companies 90–2 information on 82–3, 275–6 shell companies 10, 17, 19, 50–5, 87–97 Constitutional Research Council 272 Conway, Ed 273 Corporate Nominees 82–3, 84 corruption 7–8, 11, 15, 16–17, 121–3, 125–34, 186, 240, 269 Angola 11, 213–14 China 238, 239–40 Kenya 184–5 Nigeria 86–7, 123–6, 128–30 Russia 17 Ukraine 6–7, 11–12, 17, 20, 103–17, 170–2, 270 Corruption Watch 89 Cotorceanu, Peter 258–9, 261–3, 265 Crawford, Greg 257, 263–4 Credit Suisse 247, 249 Creer, Dean 198 Crimea 11–12, 105 Cyprus 17, 265 citizenship 136, 138, 155 and Ukraine 9, 108, 188 Daniel, Simeon 49, 51, 267 Darby, Buddy 152 Dawisha, Karen 172–4, 179 de Botton, Alain 137 de Sousa, Bornito 214–16 Delaware 19, 50, 93, 95–6, 258, 266 Deloitte 238 democracy 24, 26, 127–8 Democratic Unionist Party (DUP) (Northern Ireland) 272 Denmark 16, 276 Depardieu, Gerard 97 Diana, Princess 221 Diogo, Naulila 211–12, 214–16, 235 diplomatic immunity 157–65 Disney Corporation 271 divorce settlements 51–3 Dogs of War, The (Forsyth) 118–19 Doing Business (World Bank) 91–2 Dom Lesnika 8, 76 Dominica 143, 149, 154, 155 dos Santos, Isabel 11, 213 dos Santos, José Eduardo 213, 214 Downing, Kevin 244 dynasty trusts 256 Eaton Square, London 18–20 Egypt 9, 92 Ehrenfeld, Rachel 175 Elliott, Amy 99, 100 Equatorial Guinea 7–8, 9, 118–20, 130–2, 133, 183–4, 270 Eritrea 91 errors & omissions (E&O) 181–2 Estonia 274 Estrada, Christina 157–8, 159, 160, 161–4 eurobonds 39–43, 45, 70–1, 259 eurodollars 34–5, 36, 66 The European Azerbaijan Society (TEAS) 273–5 Evening Standard 222, 223 Extractive Industries Transparency Initiative 277 FATCA (Foreign Account Tax Compliance Act) (US) 248–9, 251, 252–3, 258, 259, 261, 262, 266 Fenoli, Randy 212 Fenwick, Edward Henry 75 Fenwick, Samuel 74–5 Feynman, Richard 20–1 Field, Mark 274 15 Central Park West 218–20, 237 FIMACO 66–8, 69 Financial Conduct Authority (UK) 89 Financial Services Authority (FSA) (UK) 100–1 Finkel, Amy 11 Finnegan, Hugh 89–90 Firtash, Dmitry 224, 235 Fisher, Jeffrey 53 flags of convenience 25, 49 Flash Crash 54 Fleming, Ian 29–30, 32, 34 Flight 714 to Sydney (Hergé) 38 flight capital 181–2, 221, 222, 223 Florent, Gerry 78–9 Florida 95, 226–30, 260–1 Foreign Corrupt Practices Act (US) 111, 213 Formations House 77–84, 276 Forsyth, Frederick 118–19 419 scams 128–9 France 37, 114, 239, 271 Fraser, Ian 39–43 Freedom House 119 Frontline Club 171–2, 179, 188 Fukuyama, Francis 5, 128 Fyodorov, Boris 67 G20 251 Gabon 132–3 Galinski, Jaime 226 Geithner, Tim 248 generation-skipping transfers 255–6 Geneva 9 Gerashchenko, Viktor 67–8 Germany 17, 271 Gherson 194 Gibraltar 19, 21, 96, 98, 276 Giles 145 Global Financial Integrity 181 Global Shell Games 95 Global Witness 89–90, 213–14 globalisation 23, 42, 273, 278 Gluzman, Semyon 113 GML 96 gold 27–8, 29–30, 43–4 Goldfinger (Fleming) 29–30, 32, 34 Goldman, Marshall 68 Goncharenko, Andrei 18, 19 Gould, Richard 189, 190–1 Government Accountability Office (US) 95–6 Grant, Valencia 140 Great Britain see United Kingdom Greenaway, Karen 92–3, 96–7, 185–6 Grenada 155 Grieve, Dominic 186–7, 191 Gross, Michael 218–19, 237 Guernsey 19 Hadid, Zaha 6 Halliburton 213 Hamilton, Alexander 56 Harley Street, London 74–84 Harper, Lenny 62–4 Harrington, Brooke 102 Harris, Robert 93–4 Harris, Timothy 149, 153, 156 Harrison, George 31 Harry Potter 271 Haslam, John 172, 173 Hayden, Justice 162 Hector, Paul 242 Heller, Joseph 169 Hello!
157–8 Henley & Partners 136–9, 149–51, 155–6, 251 Henry, James 47 Herbert, William ‘Billy’ 144, 145–8 Hergé 38 Heritage Foundation 261 Heydarov, Kamaladdin 273–4 Heydarov, Nijat 11, 274 Heydarov, Tale 11, 274, 275 Holder, Eric 186 Hong Kong 19, 46, 98, 143–4 Hoppner, Harold 137 Human Rights Watch 119 Hydra Lenders 54 IBC Bank of Laredo 270 Idaho 57, 255 Iglesias, Julio 226 incorporation agents 77–8, 83–4, 93, 94–5 Indian Creek, Florida 226–7, 229 Indonesia 9, 10 inequality 5–6, 11, 14–15, 27, 102 and plutonomy 233–5, 240–1 International Maritime Organization (IMO) 159, 162 International Monetary Fund (IMF) 28, 34, 277 Angola 213 and corruption 133–4 illegal money 181 Russia 65, 66, 67 St Kitts and Nevis 151 Ukraine 192 Isle of Man 19, 21, 186, 265 Ismaylova, Khadija 55 Israel 240 Italy 81 Ivanov, Viktor 206 Ivanyushchenko, Yuri 194 Jackson, Michael 183 Japan 85–6, 166 Jersey 19, 46, 60–4, 71, 184, 250, 269, 276, 278 Christensen 61–2 and FIMACO 66–7 Powell and Harper 62–4 Kadyrov, Ramzan 166, 239 Kalin, Christian 135–6, 137, 149–51, 155–6 Kaplin, Sergei 116 Kapur, Ajay 233–7, 240–1 Karimova, Gulnara 97–8 Karpov, Pavel 179–80 Kasko, Vitaly 189–90, 191–2, 194–5 Kazakhstan 10, 92, 184 Kelly, Karen 242 Kensington and Chelsea 221–3 Kenya 184–5 Keogh, Jim 35 Keynes, John Maynard 28, 277 Khan, Nadeem 83–4 King, Justice Eleanor 52 Kleinfeld Bridal 210, 211, 212, 216 kleptocracy 122, 123, 125, 130 see also corruption Klitgaard, Robert 130–1 Knight, Pau 81 Korner, Eric 41–2 Kovtun, Dmitry 203–4, 206, 207 Kramer, Al 264–5 Kyrgyzstan 6 Labour Party (St Kitts and Nevis) 142–3, 144–5, 149 Landscape of Lies 81 Las Vegas 254 Latvia 98, 189, 193 Lawrence, Laurie 52 Legal Nominees Ltd 82–3, 84 Lenin, Vladimir 209 Lesin, Mikhail 208 Lethal Weapon 2 160–1 libel tourism 169, 172–5, 179–80 Liberia 19, 49 Libya 9, 11, 91, 195 Liechtenstein 19, 76, 183 Limited Liability Companies (LLCs) 50, 91 Lindblad, Göran 274 Litvinenko, Alexander 196–209 Litvinenko, Marina 196–200, 205 Lombard Odier 98 London 9, 23, 25, 33 Harley Street 74–84 Kleptocracy Tours 17–20 Litvinenko murder 196–209 private banking 99, 101, 102 property 10, 17–20, 87, 218, 221–4, 237, 269 see also City of London London Kleptocracy Tours 17–20 Los Angeles 9 Low, Jho 156 Lugovoy, Andrei 203–4, 205–7 Luxembourg 17, 46, 71 eurobonds 39, 40, 41 McGown, Ally 211, 214 Macias Nguema, Francisco 119, 130 McLean, Andrea 81 Macpherson, Elle 229 Macron, Emmanuel 55, 59 Magnitsky, Sergei 55, 92, 178–80 Mainichi Shimbun 85–6 Malaysia 7, 9, 240, 270 Malta 136, 137, 138, 155, 156 Manafort, Paul 13–14, 17, 19, 69, 270–1 Marchenko, Oleg 112 Marcos, Imelda 121 Marcovici, Philip 250 Marshall Islands 274 Marx, Karl 209 Mauritius 19 May, Theresa 187 Mayer, Jane 272 MC Brooklyn Holdings LLC 14 MCA Shipping 19 Merrill Lynch Bank 240 Mexico 15, 99–100, 111, 270 Mezhyhirya palace 1–3, 9 Miami 9, 23, 87–8, 226–30 Miller, Jed 18–19 Miller, Jonathan 220–1 Mishcon de Reya 162 Mitchell, Daniel 261 Mitchell, Don 146 Moghadam, Alizera 156 Monaco 9, 19, 186 Moneyland 21, 22–6, 278 creation 26, 27–48, 70–1 defending 20, 24, 135–209 fighting back 24, 242–53, 269–78 hiding wealth 10, 12–14, 24, 49–102, 254–68 spending 17–20, 24, 210–17, 218–41 stealing 1–12, 14–16, 23, 24, 103–34, 270–1 Montana 95 Montenegro 136 Montfler SA 275 Moore, Michael 237 Moran, Rick 242 Morgenthau, Henry 87, 272 Morning Star 50, 57 Moscow, John 61–2 MPLA 212, 213, 214 Mueller, Robert 12–13, 14, 69, 270 Murray, Andy 269 Musy, Oleg 113–15 NAV Sarao Milking Markets Fund 54 Nazarabayev, Nursultan 184 Netherlands 91, 98 Netherlands Antilles 43 Neufeld, David 50 Nevada 269, 278 company formation 93–4, 95, 96 trusts 255, 256–8, 261–5, 266 Nevis 49–60, 139, 144, 266–8, 269, 277 Nevis International Trust Company (NITC) 57 New York 9, 23, 25, 98 banking 33, 36, 101, 102 Manafort 12–14 property 10, 218–21, 224–6, 230–1, 269 New York Times 156 New Zealand 16, 92 Nigeria 10–11, 12, 270 Achebe 123–5 advanced fee fraud 128–9 asset recovery 10, 54–5, 182, 183, 184, 185 corruption 9, 86–7, 123–6, 128–30, 132, 269 Nisbett Invest SA 275 No Longer At Ease (Achebe) 123–4 Nobre, Luis 80–1, 84 Nominee Director Ltd 84 North Korea 16 Northern Ireland 272 Norway 81 Novata Gazeta 72–3 Obama, Barack 267 Obiang, Teodorin 131–2, 133, 237–8 Obiang, Teodoro 119, 131, 183, 237–8 Oesterlund, Robert 53 offshore 36, 45–7, 273 eurobonds 39–43 eurodollars 36, 252 sharing data 246, 248–53, 259 see also Moneyland offshore radio stations 35–6 O’Flaherty, Victoria 140–2 Okemo, Chrysanthus 184 Olenicoff, Igor 244 Olson, Mancur 21–2 Olswang 179 One Hyde Park 224 Onipko, Natalya 104–5, 108–10 Orange Revolution 23 Oregon 96 Organisation for Economic Cooperation and Development (OECD) 251 Organised Crime and Corruption Reporting Project (OCCRP) 57 Orwell, George 121 Owen, Robert 207 Oxfam 133, 251 P&A Corporate Services Trust Reg 76 Pakistan 12 Palmer, Richard 69–70 Panama 19, 270 Panoceanic Trading Corporation 19 passports 20, 136–56, 251, 277 Paton, Leslie 75 Pawar, Charlotte 83, 276 Penney, Andrew 258 Pennsylvania 95 People’s Action Movement (PAM) (St Kitts and Nevis) 142–3, 145, 149 People’s Prosecutor 116 Perepada, Gennady 224–6, 271 Perepilichny, Alexander 208 person with significant control (PSC) 276 Peru 7 Peters & Peters 171, 188, 191, 192 Philippines 7, 9, 121, 182–3, 240 Pichulik, Dylan 230–1 Piketty, Thomas 14, 233 pirate radio stations 35–6 plutonomy 217, 233–41 Poland 125 Politically Exposed Persons (PEPs) 100 polonium-210 202–3, 204, 207, 208 Pompolo Limited 270 Power, Graham 62–4 PR agencies 176–7 Premier Trust 257 privacy 273 bank accounts 245–53, 259–61, 270, 276 corporate structures 82–4, 275–6 trusts 261–3 private banking 99–101, 132–3 Professional Nominees 82–3, 84 Proksch, Reinhard 76 property 10, 57, 218–32, 237, 269, 276 London 17–20, 87 Purnell, Jon 98 Pursglove, Sarah 53 Putin, Vladimir 5, 16, 72–3, 272 and Browder 178 and Litvinenko 201, 204, 206 and organised crime 172–3 and Skuratov video 72 and Ukraine 166 Pyatt, Geoffrey 192–3 Qualified Intermediary (QI) scheme 245, 246, 247, 249 Rajatnaram, Sinnathamby 121–2, 125 Raven, Ronald 75 Rejniak, Marek 80 Reno 253, 254–5, 257–8, 261–3, 265 Riggs Bank 131 Rijock, Kenneth 146–7, 148 Robins, Craig 229 Rolling Stones 31 Romania 81 Rothschild & Co 258, 265 Rowling, J.K. 271 Russia 11, 121, 270 Bentley cars 5–6 Berezniki 219–20 Browder 177–80 corruption 17, 25, 65–70, 72–3, 95, 173 and Crimea 11–12, 105 FIMACO 65–8, 72 inequality 5–6, 15–16, 240 and Litvinenko murder 203–9 Magnitsky affair 92 and Nevis 55, 57, 58, 59 offshore wealth 9, 47, 66–70, 95, 182 overseas property 219–20, 222, 225, 228 sanctions 137, 166 Teva Pharmaceutical 111 and Ukraine 11–12, 166 and US presidential election 13, 271, 272 watches 238–9 Yukos oil company 96 Rybolovlev, Dmitry 219–20 Saez, Emmanuel 233 St Kitts 56, 139 St Kitts and Nevis 49, 139, 142, 278 Economic Citizenship Programme 139–56 see also Nevis St Lucia 138, 155, 159–60, 161–4 St Vincent and the Grenadines 17, 19 Sakvarelidze, David 192, 193, 194, 195 Salinas, Raul 99–100 Sanchez, Alex 260 Sarao, Navinder 54 Saviano, Roberto 127 Savills 223 Say Yes to the Dress 210–12, 214–16 Schwebel, Gerry 270 Scotland 9 Second World War 26, 27 secrecy see privacy Securities and Exchange Commission (SEC) (US) 111 Semivolos, Andrei 105–6, 116, 117 Serious Fraud Office (SFO) (UK) 187, 189, 190–1, 194 Seychelles 9, 19, 84 Sharp, Howard 184–5 Shchepotin, Igor 103, 105, 110, 114, 115–17 Shedada, Kamal 154–5 shell companies 10, 17, 19, 50–5, 87–97 Sheridan, Jim 274 Sherpa 89 Sherwin & Noble (S&N) 78–80 Shvets, Yuri 206 Sidorenko, Konstantin 104, 110–11 Sigma Tech Enterprises 84 Silkenat, James 89 Simmonds, Kennedy 144, 148, 153 Singapore 46, 121, 240 Skripal, Sergei 208 Skuratov, Yuri 65–6, 72 Sloane Rangers 221–2 Smith, Mr Justice Peter 90 Smith, Vaughan 171–2 Snyder, Shawn 51 Soffer, Donald 229 Soffer, Jackie 229 Soffer, Jeffrey 229 Soloman, Sam 83 Somalia 16, 91, 127 Sonangol 213 Sooliman, Imtiaz 137 South Dakota 255, 258, 263 South Sudan 16 Soviet Union and Angola 212–13 dissolution 4–5 eurodollars 34, 35 healthcare 106 see also Azerbaijan; Kazakhstan; Kyrgyzstan; Russia; Ukraine; Uzbekistan SP Trading 92 Spain 206–7 spending 24, 216–17, 235–6 Say Yes to the Dress 210–16 watches 238–9 whisky 240 wine 239 see also property Spink, Mike 224 Spira, Peter 39, 40–1 Stephens, Mark 160 Stolen Asset Recovery (StAR) initiative 89 succession planning 60 sugar 149 Sukholuchya shooting lodge 3–4, 5, 8–9, 75–6 Sunny Isles Beach, Florida 228–30 Sutton, Heidi-Lynn 58–60, 277 Sweden 182 Switzerland 46, 102, 266 asset recovery 98, 182–3, 184, 185 bank secrecy 37–9, 40, 41, 42, 71, 99, 242–8, 253, 259–60, 261 sharing data 251 and United States 24, 242–8 watches 238 Syria 20 Taiwan 55, 59, 240 Takilant 98 Tax Justice Network 62, 89 TEAS (The European Azerbaijan Society) 273–5 Teliasonera 98 Teva Pharmaceutical 111–12 Thurlow, Edward 91 Tintin 38 Tobon, John 87–9, 228, 276 Tonga 143 Tornai, Pnina 210–12, 214–15, 216 Transparency International (TI) 16, 89, 119, 127, 174 Trump, Donald 210, 277–8 election 13, 69, 270, 277 properties 1, 220 Russian ties 228 trust 117 trusts 60, 255–8, 261–6 Tunisia 7 Turover, Felipe 72–3 UBS 61, 245, 246–7, 248, 258–9 Ukraine 11 2014 revolution 1–4, 10, 23, 104, 105–6, 113–14, 186 asset recovery 186–95 Aveiro 275 company reporting 275 corruption 6–7, 9, 11–12, 15, 17, 20, 103–17, 170–2, 269, 270 Crimea 11–12, 105 healthcare 103–17, 170–2 Manafort 13 Mezhyhirya palace 1–3, 9 and Nevis 55, 59 Orange Revolution 23 sanctions 166–9, 194 Sukholuchya shooting lodge 3–4, 5, 8–9, 75–6 ultra-high-net worth people (UNNWs) 101–2 UNITA 212, 213, 214 United Kingdom (UK) anti-corruption agenda 278 asset recovery 185, 186–95 and Azerbaijan 273–5 Brexit referendum 138, 271, 272, 278 Bribery Act 89 companies 77, 82, 91, 276 corruption 17, 127 currency crisis 34 Financial Conduct Authority 89 inequality 235 inflows of money 182 libel laws 169–75, 179 and Nevis 57 pirate radio stations 35–6 and Russia 173, 204–5, 222, 266 and St Lucia 161 sharing data 249–50 Skripal poisoning 208 and Ukraine 186–95 visas 138 Welfare State 31 see also City of London; London United Nations 126–7 United States 2016 presidential election 271, 272 and Angola 212, 213 anti-bribery measures 277–8 asset recovery 183–4, 185, 186, 190, 192–3 bank secrecy 260–1, 270 banks 31, 44, 45 Bretton Woods system 28, 34, 43–4 corruption 17 and Equatorial Guinea 131, 183–4 eurobonds 43 eurodollars 35 FATCA 248–9, 251, 252–3, 258, 259, 261, 262, 266 Flash Crash 54 free speech 174–5 inequality 14, 15, 235, 237 and Jersey 61–2 limited liability companies 91 Magnitsky laws 178 and Nevis 50–2, 54–5, 57, 58 offshore wealth 47 and Russia 68–70 and St Kitts 156 shell companies 87–90, 92–7 and Swiss banks 24, 242–8 trusts 255–8, 261–6 and Ukraine 166, 186, 190, 192–3 visas 138 see also individual states; Miami; New York Uralkali 219, 220 Uzbekistan 97–8, 184 Vanish (yacht) 151–2 Vedomosti 238–9 Venezuela 91, 228, 270 Ver, Roger 154 Vimpelcom 98 Virchis, Andres 200 Vlasic, Mark 195 Vogliano, Ernest 247–8 Wall Street Journal 61–2 Warburg, Siegmund 36–7, 38–9, 45, 262 Washington Post 193 watches 238–9 Wealth-X 101–2 weapons smuggling 92, 148 Wegelin 248, 261 Weill, Sandy 219 whisky 240 White, Harry Dexter 28, 272 Windward Trading Limited 184 wine 239 Wisconsin 255 World Bank Doing Business 91–2 Equatorial Guinea 130 StAR initiative 195 Stolen Asset Recovery initiative 89 Wyoming 50, 95, 258 Xiao Jianhua 165 Yanukovich, Viktor 1, 6, 7, 8–9, 71, 75–7, 188 assets blocked 193 Cancer Institute visit 103–5 and Manafort 13 Mezhyhirya palace 1–3, 9 and Nevis 55, 59 Sukholuchya shooting lodge 3–4, 5, 8–9, 75–6 Yeltsin, Boris 65, 66, 67, 220 Young, Robert 61 Yukos 96 Zambia 238 Zhang, Lu 92 Zlochevsky, Mykola 170–2, 187–93, 275 Zucman, Gabriel 37–8, 46–7, 266 ALSO FROM PROFILE BOOKS Red Card: FIFA and the Fall of the Most Powerful Men in Sports Ken Bensinger The full story behind the FIFA’s headline-grabbing corruption scandal, soon to be a major film.
Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong
It turns out that it’s incredibly difficult to explain to a computer exactly what we want it to do in ways that allow us to express the full complexity and subtlety of what we want. Computers do exactly what we program them to do, which isn’t always what we want them to do. For instance, when a programmer accidentally entered “/” into Google’s list of malware sites, this caused Google’s warning system to block off the entire Internet!1 Automated trading algorithms caused the May 6, 2010 Flash Crash, wiping out 9% of the value of the Dow Jones within minutes2—the algorithms were certainly doing exactly what they were programmed to do, though the algorithms are so complex that nobody quite understands what that was. The Mars Climate Orbiter crashed into the Red Planet in 1999 because the system had accidentally been programmed to mix up imperial and metric units.3 These mistakes are the flip side of the computer’s relentless focus: it will do what it is programmed to do again and again and again, and if this causes an unexpected disaster, then it still will not halt.
The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah
accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, MITM: man-in-the-middle, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative ﬁnance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus
This also allowed traders to take positions in anticipation of future price movements (“directional” strategy) and provided arbitrage opportunities between related assets. However, since the crisis, lower volatility, improved liquidity, rising costs of trading infrastructure, and regulatory scrutiny have declined the profitability of HFT, while dislocations such as the 2010 flash crash, the 2014 treasury flash crash, and the 2015 ETF flash crash have declined the popularity of HFT. In light of these shortcomings, FinTech firms using algorithmic trading strategies with smarter and faster machines are changing the market structure in terms of volume, liquidity, volatility, and spread of risk. Companies such as Neuro Dimension conduct technical analysis with AI (using neural networks and genetic algorithms) to “learn” patterns from historical data.
Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy
Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business process, Checklist Manifesto, cloud computing, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, meta analysis, meta-analysis, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, web application, zero day
Experience has shown that incorrectly configured automation can inflict significant damage and incur a great deal of financial loss in a very short period of time. For example, in 2012 Knight Capital Group encountered a “software glitch” that led to a loss of $440M in just a few hours.7 Similarly, in 2010 the US stock market experienced a Flash Crash that was ultimately blamed on a rogue trader attempting to manipulate the market with automated means. While the market was quick to recover, the Flash Crash resulted in a loss on the magnitude of trillions of dollars in just 30 minutes.8 Computers can execute tasks very quickly, and speed can be a negative if these tasks are configured incorrectly. In contrast, some companies embrace automation precisely because computers act more quickly than people. According to Eddie Kennedy, efficiency and monetary savings are key in the manufacturing industry, and automation provides a means to accomplish tasks more efficiently and cost-effectively.
In essence, Google has adapted known reliability principles that were in many cases developed and honed in other industries to create its own unique reliability culture, one that addresses a complicated equation that balances scale, complexity, and velocity with high reliability. 1 E911 (Enhanced 911): Emergency response line in the US that leverages location data. 2 Electrocardiogram readings: https://en.wikipedia.org/wiki/Electrocardiography. 3 https://en.wikipedia.org/wiki/Safety_integrity_level 4 https://en.wikipedia.org/wiki/Corrective_and_preventive_action 5 https://en.wikipedia.org/wiki/Competent_authority 6 http://ehstoday.com/safety/nsc-2013-oneill-exemplifies-safety-leadership. 7 See “FACTS, Section B” for the discussion of Knight and Power Peg software in [Sec13]. 8 “Regulators blame computer algorithm for stock market ‘flash crash’,” Computerworld, http://www.computerworld.com/article/2516076/financial-it/regulators-blame-computer-algorithm-for-stock-market—flash-crash-.html. Chapter 34. Conclusion Written by Benjamin Lutch1 Edited by Betsy Beyer I read through this book with enormous pride. From the time I began working at Excite in the early ’90s, where my group was a sort of neanderthal SRE group dubbed “Software Operations,” I’ve spent my career fumbling through the process of building systems.
Architects of Intelligence by Martin Ford
3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar
It’s worked up to now—but only because we haven’t made very intelligent machines, and the ones we have made we’ve only put in mini-worlds, like the simulated chessboard, the simulated Go board, and so on. When the AI that humans have so far made, get out into the real-world, that’s when things can go wrong, and we saw an example of this with the flash crash. With the flash crash, there was a bunch of trading algorithms, some of them fairly simple, but some of them fairly complicated AI-based decision-making and learning systems. Out there in the real world, during the flash crash things went catastrophically wrong and those machines crashed the stock market. They eliminated more than a trillion dollars of value in equities in the space of a few minutes. The flash crash was a warning signal about our AI. The right way to think about AI is that we should be making machines which act in ways to help us achieve our objectives through them, but where we absolutely do not put our objectives directly into the machine!
Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo
"Robert Solow", Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative ﬁnance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, Sam Peltzman, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, survivorship bias, Thales and the olive presses, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game
The criminal complaint, made jointly with the CFTC, alleged that Sarao had attempted to manipulate the price of E-Mini S&P 500 futures contracts on the Chicago Mercantile Exchange, a side effect of which was the Flash Crash. On November 9, 2016, Mr. Sarao pled guilty to one count of wire fraud and one count of “spoofing” (a form of price manipulation). The jury is still out on whether this lone trader was the Flash Crasher. The most remarkable aspect of this event is that more than six years later, we still haven’t identified the causes of a market disruption that involved a finite number of stocks with a finite number of market participants and took just over half an hour. Since then, we’ve experienced flash crashes in U.S. Treasury securities (October 14, 2014), foreign currencies (March 18, 2015), and exchange-traded funds (August 24, 2015). If we add to these events the technology failures associated with the initial public offerings of BATS and Facebook (March 23 and May 18, 2012), Knight Capital Group’s $458 million loss from accidental electronic trades, and the two-and-a-half hour Bloomberg terminal outage (April 17, 2015) that postponed a multi-billion-dollar government debt issue, a pattern emerges.
Subsequently, stock exchanges collectively agreed to cancel all trades that occurred during this period if their prices deviated 60 percent or more from their pre-1:32 p.m. prices. This was cold comfort for investors whose stocks dropped 59 percent and were cashed out by a stop-loss order—their portfolios were not returned to their regularly scheduled values. This extraordinary event has been seared into the memories of investors and market makers and, because of the speed with which it began and ended, is now known as the “Flash Crash.” A joint investigation by the Commodity Futures Trading Commission (CFTC) and the SEC traced the event to three conditions that created the perfect financial storm. The first was an unusually large automated sale of 75,000 futures contracts on the S&P 500 index by a mutual fund company looking to hedge its stock market exposure,51 which occurred 360 • Chapter 10 over an extremely short time period, creating a large order imbalance that apparently overwhelmed the small risk-bearing capacity of the high-frequency traders acting as market makers.52 The second condition was the response of these traders, which was to cancel their orders and leave the market temporarily.
Doyne, 218, 218, 278–279, 366 fat tails, 22, 273 fear: benefits of, 1, 3, 106; evolution of, 157–158, 187; hardwiring of, 81, 82; perils of, 3, 81–82, 83–84; of the unknown, 54–55 • 473 fear conditioning, 80, 103 Federal Deposit Insurance Corporation (FDIC), 300, 344, 377 Federal Housing Finance Agency House Price Index, 267 Federal Reserve, 7, 37, 243, 256, 292, 299, 344, 371, 377 Federal Reserve Bank of Kansas City, 315 Federal Reserve Bank of New York, 391–392 feedback loops, 104, 220, 329, 367–368, 383–384 Fehr, Ernst, 352–353 Feynman, Richard, 10, 13, 14 FFJR paper, 23–25, 47 Ficalora, Barbara, 124–127 Fichte, Johann Gottlieb, 219 Fielding, Eric, 379 fight-or-fl ight response, 1, 85, 101, 104, 117, 157, 261, 279, 414 Financial Crisis Inquiry Commission, 318 fi nancial crises, 4, 6, 7–8, 44, 62, 84, 261; in Asia, 241; global system and, 358; preventing, 364–394; of 2008, 296–329. See also “Quant Meltdown” (August 2007) fi nancial engineering, 212, 415 fi nancial sector, 330–332 fi nches, 226–227, 240, 244 FINRA (Financial Industry Regulatory Authority), 360 fi nancial technology, 248, 361, 399, 405 first-order false belief, 111 Fisher, Larry, 23 Fisher, Ronald Aylmer, 216–217 fi xed-income arbitrage, 243, 293 fi xed-rate commissions, 281 flash crashes, 358–359, 360 Food and Drug Administration (FDA), 404 food production, 8–9 footbridge dilemma, 339 foreign exchange, 12–16, 24, 38 Foundations of Economic Analysis (Samuelson), 177, 210, 212–213 Fouse, William L., 263 FOXP2 gene, 173–174 fractional reserve banking, 344 framing effects, 58–59, 388 France, 242 fraternal twins, 159, 161 Freddie Mac, 298, 379 Friedman, Milton, 25, 34 Fuld, Dick, 318 474 • Index functional magnetic resonance imaging, (fMRI), 77–78, 86, 88–89, 90, 101, 102, 186, 337, 338 funds of hedge funds, 293 futures contracts, 20, 34, 243, 268, 273, 276, 356 future value, 98 Galapagos Islands, 225–227 gambling, 17, 59–60, 67, 88–89, 91–92, 186 game theory, 170, 179, 212, 217, 336 Gaucher disease, 418, 419 Gaussian distribution (bell curve), 22, 273 Gazzaniga, Michael, 113, 115–117, 123, 313 Gekko effect, 348, 391 Gekko, Gordon (fictional character), 345, 346, 349, 387, 417 GenBank, 402–403 general equilibrium theory, 212, 213 genome sequencing, 401, 402 Genzyme, 419 geo-engineering, 416 Germany, 242 Gerrold, David, 190 Getmansky, Mila, 317, 376 Gibbs, Josiah Willard, 20, 210 Gibson, Rajna, 353 Gift of Fear, The (de Becker), 1 Gigerenzer, Gerd, 216 Gilovitch, Thomas, 68–69 Gimein, Mark, 317 Glimcher, Paul, 99 glucocerebrosidase, 419 Goldman Sachs, 242, 287, 295, 307, 308, 324 Goldfield, Jacob, 311 Good Night, Gorilla (Rathmann), 135 Google, 405 gorilla, 150 Gould, Stephen Jay, 171, 172 Government Accountability Office (GAO), 308, 311, 351–352 government bonds, 249, 292; U.S.
The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser
A Declaration of the Independence of Cyberspace, A Pattern Language, Amazon Web Services, augmented reality, back-to-the-land, Black Swan, borderless world, Build a better mousetrap, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, crowdsourcing, Danny Hillis, data acquisition, disintermediation, don't be evil, Filter Bubble, Flash crash, fundamental attribution error, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, Robert Metcalfe, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, social graph, social software, social web, speech recognition, Startup school, statistical model, stem cell, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, the scientific method, urban planning, Whole Earth Catalog, WikiLeaks, Y Combinator
But the more complex and “intelligent” the system gets, the harder it’ll be to tell. Pinpointing where bias or error exists in a human brain is difficult or impossible—there are just too many neurons and connections to narrow it down to a single malfunctioning chunk of tissue. And as we rely on intelligent systems like Google’s more, their opacity could cause real problems—like the still-mysterious machine-driven “flash crash” that caused the Dow to drop 600 points in a few minutes on May 6, 2010. In a provocative article in Wired, editor-in-chief Chris Anderson argued that huge databases render scientific theory itself obsolete. Why spend time formulating human-language hypotheses, after all, when you can quickly analyze trillions of bits of data and find the clusters and correlations? He quotes Peter Norvig, Google’s research director: “All models are wrong, and increasingly you can succeed without them.”
“Digitized Book Project Unveils a Quantitative ‘Cultural Genome,’ ” accessed Feb. 8, 2011, http://www.seas.harvard.edu/news-events/news-archive/2010/digitized-books. 200 “censorship and propaganda”: Ibid. 200 nearly sixty languages: Google Translate Help Page, accessed Feb. 8, 2011, http://translate.google.com/support/?hl=en. 201 better and better: Nikki Tait, “Google to translate European patent claims,” Financial Times, Nov. 29, 2010, accessed Feb. 9, 2010, www.ft.com/cms/s/0/02f71b76-fbce-11df-b79a-00144feab49a.html. 202 “what to do with them”: Danny Sullivan, phone interview with author, Sept. 10, 2010. 202 “flash crash”: Graham Bowley, “Stock Swing Still Baffles, with an Ominous Tone,” New York Times, Aug. 22, 2010, accessed Feb. 8, 2010, www.nytimes.com/2010/08/23/business/23flash.html. 202 provocative article in Wired: Chris Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Wired, June 23, 2008, accessed Feb. 10, 2010, http://www.wired.com/science/discoveries/magazine/16-07/pb_theory. 203 greatest achievement of human technology: Hillis quoted in Jennifer Riskin, Genesis Redux: Essays in the History and Philosophy of Artificial Life (Chicago: University of Chicago Press, 2007), 200. 204 “advertiser-funded media”: Marisol LeBron, “ ‘Migracorridos’: Another Failed Anti-immigration Campaign,” North American Congress of Latin America, Mar. 17, 2009, accessed Dec. 17, 2010, https://nacla.org/node/5625. 205 characters using the companies’ products throughout: Mary McNamara, “Television Review: ‘The Jensen Project,’ ” Los Angeles Times, July 16, 2010, accessed Dec. 17, 2010, http://articles.latimes. com/2010/jul/16/entertainment/la-et-jensen-project-20100716. 205 product-placement hooks throughout: Jenni Miller, “Hansel and Gretel in 3D?
Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe
3D printing, Albert Michelson, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, bitcoin, Black Swan, blockchain, Burning Man, buy low sell high, Claude Shannon: information theory, cloud computing, Computer Numeric Control, conceptual framework, crowdsourcing, cryptocurrency, data acquisition, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, frictionless, game design, Gerolamo Cardano, informal economy, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, Nate Silver, Network effects, neurotypical, Oculus Rift, pattern recognition, peer-to-peer, pirate software, pre–internet, prisoner's dilemma, Productivity paradox, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, technological singularity, technoutopianism, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, universal basic income, unpaid internship, uranium enrichment, urban planning, WikiLeaks
Suspects Hackers in China Breached About 4 Million People’s Records, Officials Say,” Wall Street Journal, June 5, 2015, http://www.wsj.com/articles/u-s-suspects-hackers-in-china-behind-government-data-breach-sources-say-1433451888. 29 James O’Shea, The Deal from Hell: How Moguls and Wall Street Plundered Great American Newspapers (New York: PublicAffairs, 2012). 30 Matt Levine, “Guy Trading at Home Caused the Flash Crash,” Bloomberg View, April 21, 2015, http://www.bloombergview.com/articles/2015-04-21/guy-trading-at-home-caused-the-flash-crash. 31 Melanie Mitchell, Complexity: A Guided Tour (New York: Oxford University Press, 2009), 10. 32 Ibid., 176 33 Ibid., 13 34 Page is referring to the famous scene in the mockumentary This Is Spinal Tap in which the mentally addled lead guitarist, Nigel Tufnel, tries to explain the significance of an amplifier with the capacity to exceed the conventional 10 on the volume knob.
Unhappy Union: How the Euro Crisis - and Europe - Can Be Fixed by John Peet, Anton La Guardia, The Economist
bank run, banking crisis, Berlin Wall, Bretton Woods, business cycle, capital controls, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, credit crunch, Credit Default Swap, debt deflation, Doha Development Round, eurozone crisis, Fall of the Berlin Wall, fixed income, Flash crash, illegal immigration, labour market flexibility, labour mobility, light touch regulation, market fundamentalism, moral hazard, Northern Rock, oil shock, open economy, pension reform, price stability, quantitative easing, special drawing rights, supply-chain management, The Great Moderation, too big to fail, transaction costs, éminence grise
As Greek bonds rose beyond 12%, contagion pushed Irish yields close to 6% and Portuguese ones up above 7%. Stockmarkets around the world slumped as investors fretted about the financial and political stability of a block that made up around a quarter of global output. Save the euro After months of indecision and half measures, the euro was now in mortal danger. The mood of foreboding grew darker still on May 6th 2010, the day of a strange “flash-crash” on Wall Street, in which the Dow Jones Industrial Average collapsed by about 1,000 points before recovering within minutes, perhaps because of a technical glitch. The ECB’s governing council, in Lisbon that day for its monthly meeting, faced a momentous decision: should it start buying sovereign bonds to stop the panic? The Federal Reserve and the Bank of England had been doing so under their policy of quantitative easing to bring down long-term borrowing costs.
., The Passage to Europe, Yale University Press, 2013 Appendix 4 How The Economist saw it at the time May 1st–7th 2010 July 10th–16th 2010 November 20th–26th 2010 December 4th–10th 2010 January 15th–21st 2011 March 12th-18th 2011 June 11th-17th 2011 June 25th-July 1st 2011 October 29th-November 4th 2011 November 5th-11th 2011 November 12th-18th 2011 November 26th-December 2nd 2011 February 18th–24th 2012 March 31st–April 6th 2012 May 19th–25th 2012 May 26th-June 1st 2012 July 28th-August 3rd 2012 August 11th-17th 2012 November 17th-23rd 2012 March 23rd-29th 2013 May 25th–31st 2013 September 14th–20th 2013 October 26th-November 1st 2013 January 4th-10th 2014 Index 1974–75 global recession 10 A accession treaties 112 accountability 125–129, 162 Alliance of Liberals and Democrats for Europe (ALDE) 130–131 Alogoskoufis, George 42 Amsterdam treaty 111–112, 193 Anastasiades, Nicos 2, 86–88 Anglo Irish Bank 53 Ansip, Anders 104 Arab spring 145–146 Argentina 5, 50 Armenia 149 Ashton, Catherine 28, 43, 144 Asmussen, Jörg 51, 82 Austria 111, 127 influence 108 interest rates 93 Azerbaijan 149 Aznar, José Maria 17 B Bagehot, Walter 9 bail-in rules 83, 90–91, 165 see also Cyprus bail-outs national approval requirement 127 no-bail-out rule 45, 162, 163–165 Balkans war 143 Bank of Cyprus 86–87 Bank of England 47, 157 bank recapitalisation 58–59, 74–77, 84 Bankia 72 banking sector characteristics 35 banking supervision see financial supervision banking union 23, 74–75, 77, 83–85, 90–92, 106, 165, 195 see also deposit guarantees; financial supervision Barnier, Michel 41, 138 Barroso, José Manuel early days of crisis 41 European Commission 97, 98, 141, 172 Greece 3, 78 Italy 63 Batista, Paulo Nogueira 46 Belarus 149 Belgium 17, 100, 127 Berlusconi, Silvio euro currency view 151 Italy’s failure to reform 59, 60, 62–63 People of Freedom party (PdL) 107 resignation 64 Black Wednesday 16–17 Blair, Tony 28, 112 BNP Paribas 40 Bolkestein directive 137 bond yields 37, 38, 61, 70, 89 bond spreads 37, 42, 70, 80, 88 Bootle, Roger 1 Bowles, Sharon 98, 129 Brandt, Willy 10 Bretton Woods 9–10 Brown, Gordon 24, 41, 48, 102, 112, 144 Bruegel think-tank 35, 74, 163, 166 budget deficits Maastricht ceiling 15 timescales for meeting targets 88–89 see also stability and growth pact budgets annual, European 21, 27, 118 central 13, 168–170 federal 164, 168 fiscal capacity 84 Bulgaria 108, 113, 124, 126, 147 Bundesbank 16, 23, 157 C Cameron, David 14, 17, 64–65, 117–119, 132, 140 Cannes G20 summit (2011) 62–64 Capital Economics 1 Cassis de Dijon judgment 21 Catalonia 178 CEBS (Committee of European Banking Supervisors) 35 central banks, national 22–23 Centre for European Policy Studies 34 Centre for European Reform 34 CFSP (Common Foreign and Security Policy) 142, 144 China 33, 139, 167 Chirac, Jacques 18, 23, 100, 127 Christofias, Demetris 86 Churchill, Winston 7, 115, 161 Clark, Christopher 178 climate change 135–136 Clinton, Hillary 144 Cockfield, Arthur 13 Committee of European Banking Supervisors (CEBS) 35 Committee of Permanent Representatives (COREPER) 20 Committee of Regions 21 common fisheries policy 100, 138 Common Foreign and Security Policy (CFSP) 142, 144 community method 19, 21–22 Competitiveness Pact see Euro Plus Pact complacency pre-crisis 36–37 Constâncio, Vítor 34 constitution proposals 26–27 convergence criteria 14–16, 41, 112, 193 COREPER (Committee of Permanent Representatives) 20 COSAC (Conference of Community and European Affairs Committees of Parliaments of the European Union) 133 Council of Ministers 20, 121, 130 Council of the European Union see Council of Ministers Court of Auditors 21 Court of First Instance 21 Crafts, Nicholas 9 credit ratings (countries) 69, 77–78, 108 Crimea 150 Croatia 113, 143, 147 current-account (im)balances 25, 31, 88–89, 167–168 customs union, German 9 Cyprus accession 147 bail-out 2, 85–88 entry to euro 112 finances pre-crisis 30 Cyprus Popular Bank (Laiki) 86–88 Czech Republic 113, 118 D Dayton agreement 143 de Gaulle, Charles 9, 22, 96 de Larosière, Jacques 41, 74 Deauville meeting between Sarkozy and Merkel 51–52, 102 debt mutualisation 74, 103, 166–167 defence and security 8, 143, 145 deflation 92 Delors, Jacques 11, 37, 97 Delpla, Jacques 167 democratic accountability 125–129, 162 democratic deficit 121, 129–132, 162–163, 171–172 Denmark European participation 112 justice and home affairs (JHA) 111, 139 ministerial accountability 133 opt-outs 139 referendums 16, 27, 132 shadowing of euro 113 single currency opt-out 110, 115 UK sympathies 119 deposit guarantees 5, 40–41, 74, 77, 91 Deutschmark 10, 12, 16 devaluation, internal 31, 65–66 Dexia 72 Dijsselbloem, Jeroen 24, 87 double majority voting 20, 114 Draghi, Mario 156 appointment as ECB president 23, 68 crisis-management team 2 demand for fiscal compact 64 Long Term Refinancing Operations (LTRO) 68–70 outright monetary transactions (OMT) 78–81 pressure on Berlusconi 59 “whatever it takes” London speech 79 Duisenberg, Wim 23 E e-commerce 137 east–west divide 108 ECB (European Central Bank) bond-buying 47–49, 59–60 crisis-management planning 2, 4 delays 156 European System of Central Banks 22 liquidity provision 40–42, 68–70 outright monetary transactions (OMT) 79–81, 164, 175–176 role and function 22–24, 39–40, 170–171 supervision 6, 99, 175, 195 troika membership 160–161 EcoFin meetings 20, 114 Economic and Financial Committee 20 economic and monetary union (EMU) 11, 112 Economic and Social Committee 21 economic imbalances 30–34 The Economist on ECB responsibilities 15 fictitious memorandum to Angela Merkel 1 ECSC (European Coal and Steel Community) 7–8 EEAS (European External Action Service) 142, 144 EEC (European Economic Community) 8 EFSF (European Financial Stability Facility) 26, 48, 55, 60–61, 81, 194 see also ESM (European Stability Mechanism) EFSM (European Financial Stabilisation Mechanism) 48 Eiffel group 120, 129, 164 elections, European 121, 129–130 Elysée treaty 100 emissions-trading scheme (ETS) 135–136 EMS (European Monetary System) creation of 11 exchange-rate mechanism 16 membership 15 EMU (economic and monetary union) 11, 112 EMU@10 36 energy policies 136 enhanced co-operation 111 enlargement 33, 146–147 environment summits 135 Erdogan, Recep Tayyip 148 ESM (European Stability Mechanism) 194 establishment 26, 55, 80–81 operations 58, 75, 76, 91 Estonia 65, 108 ETS (emissions-trading scheme) 135–136 EU 2020 strategy 137 euro break-up contingency plans 2–3 convergence criteria 14–16, 41, 112, 193 crash danger 47–48 introduction of 4, 18 notes and coins 18 special circumstances 3–4 euro crisis effect on world influence 143–146 errors 155–161 focus of attention 135–141 Euro Plus Pact 55, 195 euro zone 4 economic dangers 175–178 increasing significance of institutions 113–114, 120 performance compared with US 154–155 political dangers 175–178 political integration 125 trust 173 Eurobonds 54, 59, 74, 166–167 Eurogroup of finance ministers 24, 114 European Banking Authority 114, 195 European Central Bank (ECB) bond-buying 47–49, 59–60 crisis-management planning 2, 4 delays 156 European System of Central Banks 22 liquidity provision 40–42, 68–70 outright monetary transactions (OMT) 79–81, 164, 175–176 role and function 22–24, 39–40, 170–171 supervision 6, 99, 175, 195 troika membership 160–161 European Coal and Steel Community (ECSC) 7–8 European Commission commissioners 19, 172 errors 160 future direction 171–172 influence and power 96–97, 99, 119, 125 intrusiveness 127, 140–141 organisation 19 presidency 131, 144 proposals for economic governance 50 European Community 12 European Council 20, 98–99 European Court of Human Rights 21 European Court of Justice 21 European Defence Community 8 European Economic Community (EEC) 8 European External Action Service (EEAS) 142, 144 European Financial Stabilisation Mechanism (EFSM) 48 European Financial Stability Facility (EFSF) 26, 48, 55, 60–61, 81, 194 see also European Stability Mechanism (ESM) European Financial Stability Mechanism 26 see also European Stability Mechanism (ESM) European Investment Bank 21 European Monetary Institute 22 European Monetary System (EMS) creation of 11 exchange-rate mechanism 16 membership 15 European Parliament 20–21, 97–98, 99, 100, 119, 121, 129–132, 171 European People’s Party 117, 127, 130–131 European Political Co-operation 142 European semester 25, 195 European Stability Mechanism (ESM) 194 establishment 26, 55, 80–81 operations 58, 75, 76, 91 European Systemic Risk Board 41 European Union driving forces for monetary union 12–13 expansion 26 historical background 7–12 treaty making 26–28 world influence 140, 142–150 European Union Act (2011) 117, 132 Eurosceptics 13, 123 Finns Party 124 Jobbik 125 League of Catholic Families 125 National Front 124 Party of Freedom (PdL) 124 UK Independence Party (UKIP) 118, 125, 140 excessive deficit procedure 24, 88–89, 194, 195 exchange-rate systems 3, 9–11 exchange rates 164 F Farage, Nigel 98, 118 Federal Deposit Insurance Corporation (FDIC) 77 Federal Reserve (US) 23, 47, 48, 157 federalism 19, 110, 116, 161–165, 168–170, 177–178 financial integration 35–36 financial supervision 195 ECB 6, 99, 175, 195 Jacques de Larosière proposals 41 national 23, 35 single supervisor 76–77, 83–84, 90 Finland accession 26, 111 Finns Party 124 influence 108 ministerial accountability 133 fiscal capacity 84 fiscal compact treaty 25–26, 64–65, 118, 194–195 fiscal policy, focus on 30–31 Five Star Movement 124, 126 fixed exchange-rate systems 3, 9–10 Foot, Michael 116 forecasts, growth 92 foreign policy 142–143 Fouchet plan 22 France credit rating 69, 103 current-account balance 168 EMS exchange-rate mechanism 16 excessive deficit procedure 89 GDP growth 32 and Greece 44 influence 100–104, 142–143 Maastricht deal 12, 16 public debt 159 public opinion of EU 123, 124 single currency views 16–17 unemployment 159 veto of UK entry 115 vote to block European Defence Community 8 freedoms of movement 8, 13 G Gaulle, Charles de 9, 22, 96 Gazprom 136 GDP growth 32 Georgia 149 Germany 2013 elections 90, 106, 125 bond yields 37, 89 Bundesbank 16, 23, 157 constitutional (Karlsruhe) court 45, 95, 128, 158 credit rating 69, 77–78 crisis management errors 155–156 current-account surplus 89, 105, 167–168 demands post Greek bail-out 50–51 economic strengths and weaknesses 14 GDP growth 32 and Greece 44 influence 100–106 Maastricht deal 12, 15–16 national control and accountability 128, 133 parliamentary seats 100 political parties 93, 125 public debt 159 public opinion of EU 123 unemployment 159 unification 16 Zollverein 9 Giscard d’Estaing, Valéry 11, 18, 26, 100 Glienicker group 163, 170 gold standard 9–10 Golden Dawn 124 government spending (worldwide) 4 governments, insolvency of 50 great moderation 31 Greece 2012 election 73, 126 bail-out deal 45–47, 56–58, 65–67, 70, 158 bond yields 37, 61–62 current-account balance 168 debt crisis 42–45 euro membership 18, 112, 115 finances post bail-out 93–94 finances pre-crisis 30, 71 GDP growth 32 potential euro exit 1–5, 81–83 public debt 159, 166 public opinion of EU and euro 113, 123, 124 referendum on bail-out 2, 61–62 unemployment 159 Gros, Daniel 34 H Hague, William 151 Haider, Jörg 127 Hamilton, Alexander 162, 167 Heath, Edward 10, 116 Heisbourg, François 104 Hollande, François 73–74, 89, 103–104, 127 proposed reforms 177 Hungary 41, 113, 126, 147 Hypo Real Estate 41 I Iceland 53, 147 ideological differences 114–115 IKB Deutsche Industriebank 40 immigration 139–140, 146, 147 impossible trinity 13 inter-governmentalism 96, 128, 174 interest rates 93, 164 internal devaluation 31, 65–66 International Monetary Fund (IMF) banking union 74 crisis-management planning 2, 4–5 Cyprus 86–87 errors 160–161 euro zone support 48 Greece 44–46, 56–57, 66, 83, 93–95, 160 Latvia 65 rainy-day funds 169–170 special drawing rights (SDR) 63 Iraq 143 Ireland 89, 110 bail-out 53–54, 56, 57, 89 bank crises 40, 71 bond yields 37, 47, 53, 61, 89 current-account balance 168 finances pre-crisis 30 GDP growth 32 influence 107 opt-outs 111, 139 public debt 159, 166 public opinion of EU 123 referendums 27, 28, 132 unemployment 159 Italy 2013 elections 107, 124, 126 bond yields 37, 61, 89 convergence criteria 17 current-account balance 168 danger of collapse 59 EMS exchange-rate mechanism 16 excessive deficit procedure 89 GDP growth 32 influence 100, 104, 107 interest rates 93 public debt 159, 166 public opinion of EU 123 single currency views 17 unemployment 159 J Jenkins, Roy 11 Jobbik 125 Juncker, Jean-Claude 98, 104, 177 candidate for Commission Presidency 131 EU 2005 budget crisis 28 Eurobonds 54 Eurogroup president 24 justice and home affairs (JHA) 139 K Karamanlis, Kostas 42 Karlsruhe constitutional court 45, 95, 128, 158 Kauder, Volker 105 Kerry, John 144 Kohl, Helmut 12, 18, 100 L labour markets 14, 33–34 Lagarde, Christine 51, 58, 62, 92 Laiki 86–88 Lamers, Karl 111 Lamont, Norman 17 Larosière, Jacques de 41, 74 Latin Monetary Union 9 Latvia 41, 65, 67, 88, 108 Lawson, Nigel 16 League of Catholic Families 125 legislative path 21–22 Lehman Brothers, ECB reaction to collapse 4 Letta, Enrico 107–108 Libya 143, 145 Lipsky, John 57 Lisbon treaty 28, 45, 194 foreign policy 142 institutions 20, 131 justice and home affairs (JHA) 139 subsidiarity 133 voting 20, 114 Lithuania 88, 113, 153 Long Term Refinancing Operations (LTRO) 68–70, 72 Luxembourg 77–78, 100, 108, 169 Luxembourg compromise 97 M Maastricht treaty 11–12, 15, 22, 142, 193 opt-outs and referendums 16, 110–111 MacDougall report (1977) 13, 169 Major, John 12, 111, 116 Malta 100, 112 Maroni, Roberto 34 Mayer, Thomas 1 McCreevy, Charlie 41 MEPs 20–21, 130 Merkel, Angela 2013 re-election 90 banking union 74–77 Cannes G20 summit (2011) 63–64 crisis response 40–41, 44 European constitution 28 fictitious memorandum to 1 future direction 178 power and influence 89, 102–106, 153 Sarkozy collaboration 60, 61–62, 102–103 support for Cyprus 86 support for Greece 5, 45, 49–52, 81–82 support for UK 118–119 union method 22, 128 voter support 125 Messina conference 8, 115 migration 139–140, 146, 147 Miliband, David 144 Mitterrand, François 11, 12, 18, 100 Mody, Ashoka 163 Moldova 149 Monnet, Jean 8, 152 Montebourg, Arnaud 104 Montenegro 147 Monti, Mario 64 influence 70, 75–76, 107 A New Strategy for the Single Market (2010) 137–138 Morocco 146 Morrison, Herbert 8 Morsi, Muhammad 145 Moscovici, Pierre 75 multi-annual financial framework 21, 27, 118 Mundell, Robert 12–13 mutualisation of debt 74, 103, 166–167 N national budgets 89, 125 National Front 124 NATO defence spending targets 145 European security 8 membership 110 Netherlands credit rating 77–78 excessive deficit procedure 89 influence 100, 108 ministerial accountability 133 UK sympathies 119 Nice treaty 194 no-bail-out rule 45, 162, 163–165 north–south divide 33–34, 108 Northern Rock 40 notes and coins 18 Nouy, Danièle 90 Nuland, Victoria 149 O Obama, Barack 63 official sector involvement (OSI) 83 OMT (outright monetary transactions) 79–81, 164, 175–176 Germany’s constitutional court judgment 95, 128 optimal currency-area theory 12–13, 14–15 Orban, Viktor 126 Osborne, George 117, 119 OSI (official sector involvement) 83 outright monetary transactions (OMT) 79–81, 164, 175–176 Germany’s constitutional court judgment 95, 128 P Pact for the Euro see Euro Plus Pact Papaconstantinou, George 43 Papademos, Lucas 64 Papandreou, George 56, 60 election 43 Greek referendum 61–62 resignation 2, 64 Party of Freedom 124 Poland 109, 113 Policy Exchange 1 political parties 124–125, 139–140 political union 10, 12, 133–134 Pompidou, Georges 10 Poos, Jacques 143 Portugal 110 bail-out 54, 57, 89–90 bond yields 37, 47, 53, 61, 89 public opinion of EU and euro 113 power, balance of 99–101 price stability goal of ECB 23 private-sector involvement (PSI) in debt restructuring 51–52 Prodi, Romano 17, 25, 97 Progressive Alliance of Socialists and Democrats (S&D) 130–131 public debt 15, 158–159 see also sovereign debt public opinion of EU and euro 121–124 Putin, Vladimir 149–150 Q qualified-majority voting 13, 20, 99, 121 negative qualified-majority voting 25, 195 quantitative easing (QE) 47, 15 R Rajoy, Mariano 70, 75–76, 127 recapitalisation, bank 58–59, 74–77, 84 redenomination 3–4, 153–154, 175 Reding, Viviane 139 referendums 27, 28, 121–122, 132 REFIT initiative 172 Regling, Klaus 26 Renzi, Matteo 107–108 rescue fund see European Stability Mechanism (ESM) resolution mechanism 90–91, 165, 195 single resolution mechanism (SRM) 195 single supervisory mechanism (SSM) 195 Romania 41, 108, 113, 124, 126, 147 Rome treaty 8, 97, 110, 193 Rösler, Philipp 78 Rueff, Jacques 9 Rumsfeld, Donald 143 Russia, influence on Ukraine 149–150 Rutte, Mark 77 S Samaras, Antonis 2, 78, 82, 93–94 Santer, Jacques 97 Sarkozy, Nicolas crisis response 40–41, 44 economic governance 49–50 European constitution 28 LTROs and the Sarkozy trade 69 Merkel collaboration 51–52, 60, 61–62, 102–103 Schäuble, Wolfgang 62, 75, 84, 90–91, 106, 111, 154 Schengen Agreement 110, 111–112 Schmidt, Helmut 11, 100 Schröder, Gerhard 18, 101, 127 Schulz, Martin 131 Schuman Day 8 Schuman, Robert 7–8 Scotland 112, 178 SDR (special drawing rights) 63 Securities Market Programme (SMP) 48, 79 services directive 34 Shafik, Nemat 65 Sikorski, Radek 109 Simitis, Costas 18 Simms, Brendan 179 single currency benefits 152 club within a club 112 driving forces 12–14 importance of 113 vision for 9 see also euro Single European Act 13, 193 single market 4, 137–138, 174–175 Sinn, Hans-Werner 101 six-pack 25, 50, 195 Slovakia 112 adoption of euro 41 influence 108 Slovenia 88–89, 112 influence 108 SMP (Securities Market Programme) 48, 79 snake in the tunnel 10 Solana, Javier 142 sovereign debt 165–166 see also public debt Spain 110 bail-out 70–73, 89 bank recapitalisation 84 bond yields 37, 89 CDS premiums 72 current-account balance 168 danger of collapse 59 excessive deficit procedure 89 finances pre-crisis 30 GDP growth 32 influence 107 public debt 159 public opinion of EU 123, 124 single currency views 17 unemployment 159 special drawing rights (SDR) 63 stability and growth pact 18, 24, 29, 50–51, 127, 194 Stark, Jürgen 59, 106 Steinbrück, Peer 43 Strauss-Kahn, Dominique 24, 44, 57 stress tests, bank 72, 175 subsidiarity 133, 141 Sweden 109, 111, 112 euro opt-out 18, 115 UK sympathies 119 Syria 145 Syriza 124 T Target II 157 Thatcher, Margaret 27, 110, 116 third energy package 136 Tilford, Simon 34 Tindemans, Leo 111 trade policy 138 Transatlantic Trade and Investment Partnership (TTIP) 138–139 treaty making and change 26–27, 173–174 Treaty of Amsterdam 111–112, 193 Treaty of Lisbon 28, 45, 194 foreign policy 142 institutions 20, 131 justice and home affairs (JHA) 139 subsidiarity 133 voting 20, 114 Treaty of Nice 194 Treaty of Rome 8, 97, 110, 193 Treaty on European Union (Maastricht treaty) 11–12, 15, 22, 142, 193 opt-outs and referendums 16, 110–111 Treaty on Stability, Co-ordination and Governance (TSCG) see fiscal compact treaty Tremonti, Giulio 54, 60 Trichet, Jean-Claude 151, 156 bond-buying 47–48, 52–53 crisis-management planning 2 early warnings 39–40 ECB president 23 IMF 44 Italy 59 True Finns 124 Turkey 132, 147, 148 Tusk, Donald 109, 114 two-pack 25, 89, 195 U UK Independence Party (UKIP) 118, 125, 140 Ukraine 149–150, 179–180 unemployment 158–159, 170 union method 19, 22 United Kingdom current-account balance 168 economic strengths and weaknesses 14 EMS exchange-rate mechanism 16 euro crisis reaction 117–118 euro membership 112 European budget contribution 27–28 European involvement 8, 10, 12, 115–119 future status 174–175 influence 100–101, 106, 109, 142–143 initial application to join EEC 9 opt-outs 110–111, 139 public opinion of EU 123 single currency views 17 United Left party 124 United States abandonment of gold standard 10 federalism model 177 foreign policy 143 performance compared with euro zone 154–155 Urpilainen, Jutta 77 V Van Gend en Loos v Nederlandse Administratie der Belastingen (1963) 21 Van Rompuy, Herman 98 crisis-management planning 3 Cyprus 87 European Council presidency 20, 28 Italy 63 roadmap for integration 74–75, 84, 173 support for Greece 43–45 Venizelos, Evangelos 57, 62 Verhofstadt, Guy 131 Véron, Nicolas 35 Vilnius summit 149 von Weizsäcker, Jakob 166 W Waigel, Theo 17–18 Wall Street flash crash 47 Weber, Axel 49, 56, 106 Weidmann, Jens 40, 80, 82 Weizsäcker, Jakob von 166 Werner report (1971) 10 Wilson, Harold 116 Wolfson Prize 1 World Bank 33 World Trade Organisation 138–139 Y Yanukovych, Viktor 149 Z Zapatero, José Luis Rodríguez 59, 62 Zollverein 9 PublicAffairs is a publishing house founded in 1997. It is a tribute to the standards, values, and flair of three persons who have served as mentors to countless reporters, writers, editors, and book people of all kinds, including me.
The Rise of Carry: The Dangerous Consequences of Volatility Suppression and the New Financial Order of Decaying Growth and Recurring Crisis by Tim Lee, Jamie Lee, Kevin Coldiron
active measures, Asian financial crisis, asset-backed security, backtesting, bank run, Bernie Madoff, Bretton Woods, business cycle, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, debt deflation, distributed ledger, diversification, financial intermediation, Flash crash, global reserve currency, implied volatility, income inequality, inflation targeting, labor-force participation, Long Term Capital Management, Lyft, margin call, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, negative equity, Network effects, Ponzi scheme, purchasing power parity, quantitative easing, random walk, rent-seeking, reserve currency, rising living standards, risk/return, sharing economy, short selling, sovereign wealth fund, Uber and Lyft, uber lyft, yield curve
If S&P 500 volatility has become “global volatility,” then it represents generic liquidity risk—the risk that defines the value of money. This must be the best-paying risk in the world. At the same time, this absorption of the generic liquidity risk premium must convert the S&P 500 itself into an extreme carry trade, with high expected returns and terrifying skew. The chance of all-but-zero-probability events, such as flash crashes or October 1987s, rises, from all but zero, to something meaningful. It also means that recessions and economic turbulence do not cause the S&P 500 to drop. Instead, now, they are caused by the S&P 500 dropping. The onset of the acute phase of the euro problem in 2011 can be interpreted in this manner; the widening of the generic liquidity risk premium blew up Italian and Spanish finances—or, probably more accurately, revealed their true state.
If the Fed is seen as the greatest volatility seller, then the claim that volatility selling is extremely important to the stock market is closely related to the quite conventional claim that the Fed is extremely important to the stock market. This idea may help in understanding the well-known chart (see Figure 6.5). S&P 500 (Left Axis) Fed Outright Holdings of Securities Maturity >5 Years, $ Billions (Right Axis) QE1 2,200 Flash Crash QE2 Euro Shock Twist QE3 3,200 2,000 2,800 1,800 2,400 1,600 2,000 1,400 1,600 1,200 1,200 1,000 800 800 400 600 Sep-08 Sep-09 Sep-10 Sep-11 Sep-12 Sep-13 Sep-14 0 FIGURE 6.5 S&P 500 and Fed holdings of long-duration securities Chart shows the S&P 500 on the left axis and total Fed assets with maturity of greater than 5 years on the right axis (in billions of dollars), with the four major phases of Fed long duration purchases, as well as the interludes between them, marked.
How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester
asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trickle-down economics, Washington Consensus, wealth creators, working poor, yield curve
History suggests that there are risks in the fact that many of the tricks involved are likely to do the same thing in the same way, and therefore be prone to dramatically exaggerating movements in the markets—remember, equity markets now mainly consist of this kind of trading. It was computer-based portfolio insurance—computer programs all doing the same thing at the same time—that caused the Wall Street crash of October 1987. It seems to have been high-frequency trading that caused the “flash crash” of 6 May 2011, in which the US stock market fell by more than 10 percent and lost $1 trillion of value in less than twenty minutes. But the causes of the flash crash are still not really understood. That, right there, is really alarming.44 HNWI High net worth individual, a reference to a rich person as defined by the financial services industry. The definition is fixed: it means he or she has more than a million dollars in financial assets—meaning assets other than their “residences, collectables, consumer durables and consumables.”
Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths
4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, Berlin Wall, Bill Duvall, bitcoin, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, David Heinemeier Hansson, delayed gratification, dematerialisation, diversification, Donald Knuth, double helix, Elon Musk, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, George Akerlof, global supply chain, Google Chrome, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, Johannes Kepler, John Nash: game theory, John von Neumann, Kickstarter, knapsack problem, Lao Tzu, Leonard Kleinrock, linear programming, martingale, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, Sam Altman, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Stanford marshmallow experiment, Steve Jobs, stochastic process, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, urban planning, Vickrey auction, Vilfredo Pareto, Walter Mischel, Y Combinator, zero-sum game
It turns out that two of the sellers were setting their prices algorithmically as constant fractions of each other: one was always setting it to 0.99830 times the competitor’s price, while the competitor was automatically setting their own price to 1.27059 times the other’s. Neither seller apparently thought to set any limit on the resulting numbers, and eventually the process spiraled totally out of control. It’s possible that a similar mechanism was in play during the enigmatic and controversial stock market “flash crash” of May 6, 2010, when, in a matter of minutes, the price of several seemingly random companies in the S&P 500 rose to more than $100,000 a share, while others dropped precipitously—sometimes to $0.01 a share. Almost $1 trillion of value instantaneously went up in smoke. As CNBC’s Jim Cramer reported live, dumbfounded, “That … it can’t be there. That is not a real price. Oh well, just go buy Procter!
a sale price of more than $23 million: The pricing on this particular Amazon title was noticed and reported on by UC Berkeley biologist Michael Eisen; see “Amazon’s $23,698,655.93 book about flies,” April 23, 2011 on Eisen’s blog it is NOT junk, http://www.michaeleisen.org/blog/?p=358. worsen the irrationality of the market: See, for instance, the reactions of Columbia University economist Rajiv Sethi in the immediate wake of the flash crash. Sethi, “Algorithmic Trading and Price Volatility.” save the entire herd from disaster: This can also be thought of in terms of mechanism design and evolution. It is better on average for any particular individual to be a somewhat cautious herd follower, yet everyone benefits from the presence of some group members who are headstrong mavericks. In this way, overconfidence can be thought of as a form of altruism.
See also randomness San Francisco Sartre, Jean-Paul Saxena, Nitin saying no scale, sorting and scale-free distributions scheduling Schmidt, Eric Schmidt, Peter Schooler, Lael Science Scientific American Scientific Management Scientist in the Crib, The (Gopnik) Seale, Darryl search, gap between verification and search engines search-sort tradeoff self-organizing lists second-chance scenario secretary problem burglar variant full-information variant recall variant rejection variant seeding selfish routing self-organizing lists sequential information processing serendipity Shallit, Jeffrey Shaw, George Bernard Shi, Yong Shoenfield, Joseph shop hours Shortest Processing Time unweighted weighted Shoup, Donald Sibneft oil company Sieve of Erastothenes Silicon Valley Simulated Annealing Sinatra, Frank Single Elimination single-machine scheduling Siroker, Dan size dominance hierarchies and memory hierarchy and sorting and Skype Sleator, Daniel slot machines small data as big data in disguise Smith, Adam Smith, Dan soccer social media Social Network, The (film) social networks social policy socks, sorting software, term coined solid-state drives solitaire sorting Sorting and Searching (Knuth) sort-search tradeoff soy milk space-time tradeoffs SpaceX spinning sports league commissioner overfitting and season scheduling tournament structures Sports Scheduling Group squirrels SRAM standardized tests Statistical Science status pecking order and races vs. fights and Stewart, Martha Steyvers, Mark stock market. See also investment strategies algorithmic trading and flash crash of 2010 storage storytelling Stucchio, Chris sum of completion times sum of weighted completion times sum of weighted lateness of jobs super filing system Tail Drop Tardos, Éva Tarjan, Robert task switching Taylor, Frederick TCP sawtooth. See also Transmission Control Protocol (TCP) teaching to the test technical investors telegraph telephone temperature temporal locality Tenenbaum, Josh tennis tournaments Texas Hold ’Em text messages “TeX Tuneup of 2012, The” (Knuth) Thanksgiving commerce theft, irrational responses and Things a Computer Scientist Rarely Talks About (Knuth) 37% rule Thoreau, Henry David thrashing threading Three Princes of Serendip, The Threshold Rule throughput Tibshirani, Robert Tikhonov, Andrey time interval of timeboxing time costs time management time-space tradeoffs Tolins, Jackson Tomlinson, Ray town size distributions Toxoplasma gondii traffic tragedy of the commons training scars transit systems Transmission Control Protocol (TCP) ACKs and backchannels and flow control and price of anarchy and traveling salesman problem Treat, Tyler “Treatise on the Probability of the Causes of Events” (Laplace) Tree, Jean Fox Trick, Michael triple handshake triple-or-nothing game trip planning.
Model Thinker: What You Need to Know to Make Data Work for You by Scott E. Page
"Robert Solow", Airbnb, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Alvin Roth, assortative mating, Bernie Madoff, bitcoin, Black Swan, blockchain, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Checklist Manifesto, computer age, corporate governance, correlation does not imply causation, cuban missile crisis, deliberate practice, discrete time, distributed ledger, en.wikipedia.org, Estimating the Reproducibility of Psychological Science, Everything should be made as simple as possible, experimental economics, first-price auction, Flash crash, Geoffrey West, Santa Fe Institute, germ theory of disease, Gini coefficient, High speed trading, impulse control, income inequality, Isaac Newton, John von Neumann, Kenneth Rogoff, knowledge economy, knowledge worker, Long Term Capital Management, loss aversion, low skilled workers, Mark Zuckerberg, market design, meta analysis, meta-analysis, money market fund, Nash equilibrium, natural language processing, Network effects, p-value, Pareto efficiency, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, pre–internet, prisoner's dilemma, race to the bottom, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, school choice, sealed-bid auction, second-price auction, selection bias, six sigma, social graph, spectrum auction, statistical model, Stephen Hawking, Supply of New York City Cabdrivers, The Bell Curve by Richard Herrnstein and Charles Murray, The Great Moderation, The Rise and Fall of American Growth, the rule of 72, the scientific method, The Spirit Level, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, urban sprawl, value at risk, web application, winner-take-all economy, zero-sum game
As the crash unfolded, these insurers sold more and more stock. In effect, insurers acted as if they were populations of individuals with diverse thresholds. One portfolio insurer sold over $1 billion in stock. To put that in perspective, only $20 billion in stock was sold that entire day. The second crash, the May 6, 2010, “flash crash” dropped the Dow Jones Industrial Average by 5% in three minutes. It was the result of algorithmic trades. Owing to the complexity and speed of modern financial markets, no one knows for certain what exactly caused the flash crash. We know that a large mutual fund made a huge sell order, dumping over $4 billion of stock futures into a market containing high-speed trading algorithms that try to exploit beneficial trades. The algorithms sensed a price trend and starting executing trades at breakneck speed. Imagine the riot model at high speed.
See Food and Drug Administration, US Federal Reserve, US, 32 feedback negative, 201, 211, 220–222 positive, 69–70, 209–210, 345–346 Ferdinand, Franz, 167 Fermi, Enrico, 41 Ferrante, Elena, 83 Feynman, Richard, 59 financial collapse of 2008, 9 financial systems, systems dynamics models of, 209 (fig.) first fit algorithm, 18 first things first, 17 first-price auction, 288 fish, 280 fitness landscape model, 327–329 fixed transition rule, Perron-Frobenius theorem and, 193 flash crash, 225 Flores, Thomas, 192 Food and Drug Administration, US (FDA), 64 Ford, Henry, 329 forest fire model, defining, 74 forests, 93 formalism, 18 Forrester, Jay Wright, 201 Frank, Anne, 253 free ride, 139 Freedom House, 192 (fig.) friendship paradox, 16, 17 (fig.) defining, 124 full allocation, 111 functional signals, 302 functions, distribution and, 63–66 fundamental preferences, 240 Game of Life, 14, 171, 176–178, 187, 201 blinker in, 177 (fig.)
WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly
4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, strong AI, TaskRabbit, telepresence, the built environment, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar
The speed and scale of electronic networks are also changing the nature of financial market reflexivity in ways that we have not yet fully come to understand. Financial markets, which aggregate the opinions of millions of people in setting prices, are liable to biased design, algorithmically amplified errors, or manipulation, with devastating consequences. In the famous “Flash Crash” of 2010, high-frequency-trading algorithms responding to market manipulation by a rogue human trader dropped the Dow by 1,000 points (nearly a trillion dollars of market value) in only thirty-six minutes, recovering 600 of those points only a few minutes later. The Flash Crash highlights the role that the speed of electronic networks plays in amplifying the effects of misinformation or bad decisions. The price of goods from China was once known at the speed of clipper ships, then of telegrams. Now electronic stock and commodities traders place themselves closer to Internet points of presence (the endpoints of high-speed networks) to gain microseconds of advantage.
., 257 and misinformation, 210–11 and regulations, 172–73 serving itself vs. real economy, 251–52 shareholder capitalism, 240–41, 245–51, 256, 263–68, 292 social values as anathema, 240–41, 251 stock prices as a bad map, 243–45 system design leads to predictable outcomes, 238–41, 256–62 value investing, 271–72, 284–85 Fink, Larry, 242–43, 272 Firestein, Stuart, 340 fitness function, 106 of Amazon teams, 114, 118 for economy, 269, 367–68 of Facebook, 162–63, 219–20 and fake news, 225 and financial markets, 238–40, 242, 248, 303 of Google’s Search Quality team, 156–57, 173–74 making money as, 226, 239–41, 274, 352 and search engine ratings, 158 fitness landscape, xxii, 106 Flash Crash of stock market (2010), 237 Foo Camp (annual unconference), 50 Ford, Martin, 269 Foroohar, Rana, 251–52, 271 Foursquare, 84 Fox News, 208 free software, 16–19. See also open source software Freeware Summit (1998), 15–16, 19 Fried, Limor, 369–70, 371–72 Friedl, Jeffrey, 120–21 Friedman, Milton, 240 future effect of individual decisions, 13 Apple Stores, 321–22 business model map for, 65–70 gravitational cores and gradually attenuating influence, 65 inventing the future, 46–47, 153–54 living in, prior to even distribution, 19, 23, 29, 316 questions about, 300 seeing via innovators in the present, 14 and worker augmentation, 69 “Future of Firms, The” (Kilpi), 89 Gage, John, 28 Gall, John, 106 Gates, Bill, 17, 307, 360 Gebbia, Joe, 97–98 Gelsinger, Pat, 13 General Electric (GE), 241, 249, 303 General Theory of Employment, Interest, and Money (Keynes), 271–72 Generation Z, 341–42 genetic programming, 106 Getaround, 85 GiveDirectly, 305 global brain.
The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy
3D printing, Ada Lovelace, Albert Einstein, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Claude Shannon: information theory, computer vision, correlation does not imply causation, crowdsourcing, data is the new oil, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons
The price peaked at $23,698,655.93 (plus $3.99 shipping) on 18 April, when finally a human at profnath intervened, realising that something strange was going on. The price then dropped to $106.23. Predictably bordeebook’s algorithm offered their book at $106.23 × 1.27059 = $134.97. The mispricing of The Making of a Fly did not have a devastating impact for anyone involved, but there are more serious cases of algorithms used to price stock options causing flash crashes on the markets. The unintended consequences of algorithms is one of the prime sources of the existential fears people have about advancing technology. What if a company builds an algorithm that is tasked with maximising the collection of carbon, but it suddenly realises the humans who work in the factory are carbon-based organisms, so it starts harvesting the humans in the factory for carbon production?
., 244, 281 ELIZA 255–7, 259 Ellenberg, Jordan 180 elliptic curves 250 emergent phenomena 299–300 Emmy 195–207, 197, 214; Bach by Design 200; Cradle Falling 196 England football team 55 Enigma code 277 Eno, Brian 15, 229 Ensemble 88 Euclid 111, 239, 245, 246, 251; Elements 44–7, 45, 162–3, 166, 188 Euler, Leonhard 167, 237 European Union (EU) 95, 288 Expressionism 13 Facebook 67, 296 Fan Hui 29, 30, 31, 35, 97 Fantom app 227–8 Fermat’s Last Theorem 102, 152, 153, 166–7, 172, 177, 236, 245, 249, 250, 252 Ferranti Mark 1 277, 278 Ferrucci, David 263 Fibonacci numbers 187, 205, 292 Fields Medal 162, 179, 181, 182, 240, 241 FIFA 55 flash crashes 64 Flaubert, Gustave: Madame Bovary 268, 269 Flow Machine 221–4, 222; ‘Daddy’s Car’ 223–4; Hello World 224 fMRI scanner 125, 302, 305 Four-Colour Map Problem 169–70, 174–5 fractals 113–16, 124–5, 188 Frederick the Great, King 189, 190, 192 free will 105, 112–13, 217, 300–1 fugues 10, 186, 190–2, 191, 193, 198, 205 Fundamental Theorem of Algebra 237 Gale, David 57, 58, 59, 61 Galton, Francis 127–8 Game, The (musical competition) 200–2 gaming 25–8, 92, 97, 115–16, 132, 168 Ganesalingam, Mohan 240 Gardner, Lyn 291 Gauss, Carl Friedrich 14, 154, 167, 237, 285; Disquisitiones arithmeticae 14 General Adversarial Network 133, 134, 141, 142 General Data Protection Regulation, Article 22 (EU) (2018) 95 geometry 11, 46–7, 110–11, 127, 128, 152, 162, 163, 164, 165, 166, 170, 171, 184, 187, 188, 191, 210, 237, 244, 269, 291 George Washington University 294–5 Gerrard, Steven 55 Gillespie, Dizzy 214 Glass, Philip 11, 186, 188, 189, 204, 205, 207, 209; 1 + 1 188–9 Go (game) 18–20, 21–2, 23, 24–5, 28–43, 65, 66, 95–6, 97–8, 121, 131, 145, 148–9, 151, 153, 163, 209, 219–20, 233, 237, 261, 298 Go Seigen 42 God, concept of 231–2 Gödel, Kurt 178 Goethe, Johann Wolfgang von: The Sorrows of Young Werther 12 golden ratio 128 Golding, William 300 Goldsmiths, University of London 228 Gonthier, Georges 174, 175, 176 Goodfellow, Ian 133, 141–2 Google 67; Brain 133, 134, 235, 272; DeepDream 143–4, 145; DeepMind 28–9, 234–41; London campus 234–5; Pixel Buds 268; search algorithm 47–56, 50, 51, 52; Translate 269–71; visual recognition and 77, 78–9, 143–4, 145 Gowers, Timothy 240–1 GPS 44, 110 Greece, Ancient 13–14, 44–7, 161–2, 165, 166 Greene, Graham 245 Grierson, Mick 228, 229 Gu Li 39, 43 Guardian 120, 129–30, 148 hacking 26, 49, 53, 77–9, 270, 272 Hadid, Zaha 3, 11 Hadjeres, Gaëtan 210, 211 Haken, Wolfgang 170, 174 Hales, Thomas 170 Hardy, G.
Money: Vintage Minis by Yuval Noah Harari
23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, European colonialism, Flash crash, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, pattern recognition, Ponzi scheme, self-driving car, telemarketer, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game
Three years earlier, on 6 May 2010, the New York stock exchange underwent an even sharper shock. Within five minutes – from 14:42 to 14:47 – the Dow Jones dropped by 1,000 points, wiping out $1 trillion. It then bounced back, returning to its pre-crash level in a little more than three minutes. That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. They know algorithms were to blame, but are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers.
Pinpoint: How GPS Is Changing Our World by Greg Milner
Ayatollah Khomeini, British Empire, creative destruction, data acquisition, Dava Sobel, different worldview, digital map, Edmond Halley, Eratosthenes, experimental subject, Flash crash, friendly fire, Hedy Lamarr / George Antheil, Internet of things, Isaac Newton, John Harrison: Longitude, Kevin Kelly, land tenure, lone genius, low earth orbit, Mars Rover, Mercator projection, place-making, polynesian navigation, precision agriculture, race to the bottom, Silicon Valley, Silicon Valley startup, skunkworks, smart grid, the map is not the territory
The problem, Humphreys maintains, is that the traders’ computers do not have the anti-spoofing protection the exchange applies to its own computers. By jacking directly into the GPS data stream, they leave themselves vulnerable to a spoofed signal that scrambles their computers’ clocks. What might the results of a successful spoofing attack on trading computers look like? Humphreys thinks it could cause a more catastrophic version of the 2010 Flash Crash, a thirty-minute hiccup when the major markets all but collapsed and then quickly rebounded. Though the cause of the crash is still debated, some evidence points to automatic trading programs used for high-frequency trading, which have instructions to pull out of the market if the program senses a problem. In his testimony, Humphreys noted that the crash revealed that many trading algorithms included automatic checks triggered by “unusual latency” in the data coming from exchanges.
., 65, 67 speed estimates, 16 Spiers, Hugo, 133 spread-spectrum systems, 54–56, 77 Springfield, Mo., 89 Sputnik, 30–37, 251 sound signals emitted by, xviii, 36–37, 39 SRI International, 122 Stanford University, 48, 122, 142, 171 GPS Laboratory at, 61, 139, 140, 181 Stewart, Brian, xiii–xiv stimulus-and-response concept, 115–16, 118, 133 stock exchanges, 161–64 automated trading in, 161–62 high-frequency traders in, 161–63 importance of accurate timing in, 162 2010 Flash Crash of, 163 volatility in, 163 storms, 27, 192 Strathclyde, University of, 195 Strebe, Daniel, 241 Streetcorner Research (Schwitzgebel), 174 subatomic particles, xvii, 155–56 sugar beets, 73–75, 101–5, 274 sun, 24, 25, 245 activity on, 28 Earth’s orbit of, 41, 228 energy from, 227 radiation from, 258 Sunda peninsula, 3–4 Super Bowl, XLVII, 192 Supreme Court, German, 186, 187 Supreme Court, U.S., 178–80, 186, 188–91 Survey of the Coast, 248 Swiss Alps, 158 Switzerland, 158–59, 167 Syene (Aswan), 245 Synchronous Grid of Continental Europe, 158–59 Synchrophasors, 159–61, 163 connecting clocks to, 160, 161 Tahiti, 7–9, 10, 13, 24, 106, 263, 264–66 Taiwan, 4 Taliban, 72 Taos, N.Mex., 180 Tasmania, 4 TechRepublic, 191 Tehran, 84 U.S. hostages held in, 77, 87 Tele Atlas company, 242 Telefónica, 192 telematics industry, 183–84 telephones, 59, 156–57, 191 cellular, 95, 119, 183 digital protocols for, 156, 157 integration of GPS devices and, 119, 154, 244 long-distance service on, 81, 157 mobile, 192, 242 multiplexing techniques and, 156 911 calls on, 158 smart, 55, 72, 145, 223, 244 Yellow Pages guide to, 123 telescopes, 29 radio, 209, 261 Terrestrial Environment, The: Solid-Earth and Ocean Physics (Williamstown Report), 208–9 terrorism, 148, 153 possible targets of, 170–72 Terry, Ron, 50–51 Tevake, 11, 18, 264 disappearance of, 13–14, 21 Texas, 40, 42, 230 Texas, University of (UT), 151 Radio Navigation Laboratory at, 150 Texas Instruments, 58, 78–79, 214 theodolites, 251 think tanks, 122, 146 Thompson, Nainoa, 266 3M company, 179 TI-4100 receivers, 214 Timation project, 40, 42–45, 47, 56–57, 153 Tokyo, xv, 48–49, 91, 121, 225 Tolman, Edward, 115–20, 133, 276 TomTom company, 242–45 data-collection vans of, 242–44 mobile phone mapping program of, 242 WGS 84 referencer system used by, 244–45 topography, 124 torpedoes, radio-controlled, 54 Transcontinental Arc, 249 Transit program, 38–40, 42–43, 45, 76, 81, 259, 270 Transportation Department, U.S., xviii, 149, 165 Transworld Data, 191 Trimble, Charlie, 80–81, 83–94, 95–98, 104, 140–41, 211–12, 254 Trimble 4000A GPS, 87 Trimble GPS receivers, 158 Trimble Navigation, 81, 83–88, 93–94, 96–97, 126, 182 Trimpacks, 94, 95–96 Tripoli, 62 troposhere, 227 Tsikada program, 44 tsunamis, 202, 222, 225–26 Tuamotu Archipelago, 12 Tübingen, 130–31 Tuck, Ed, 88–90 Tupaia, 7–11, 269 Cook and, 7–10, 21–24, 26, 263–64, 266 illness and death of, 10, 11 navigational skill of, 8–10 Pacific map of, 10, 13, 14, 263–64 TV-3 rocket, 32–35 Twin Falls, Idaho, 111 Twitter, 194 U-2 reconnaissance aircraft, 67 UNAVCO (nonprofit university-funded consortium), 215, 224 United Kingdom, 27, 104, 156–57, 187–88, 197, 252 100 wealthiest people in, 242 see also England; Scotland United Nations, 63 Soviet delegation to, 35 United Parcel Service (UPS), 143, 184 United States, 9 bureaucracy in, 93 coastlands of, 108, 224 economy and security of, 143 farms in, 104 400 wealthiest Americans in, 127, 239 Great Plains of, 73 infrastructure sectors of, 143, 144–45 Northeastern, 170–72 Northwestern, 202 nuclear strike capabilities of, 62 Southwestern, 59 Soviet relations with, 29–30, 62, 82 Western, 74, 215, 224 United States Standard Datum, 249 United States v.
Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace
"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, hedonic treadmill, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game
High-frequency trading, where computers trade with each other at speeds no human can even follow – never mind participate in – took off in the early 21st century, although it has reportedly fallen back from around two-thirds of all US equity trades at the start of the 2008 credit crunch to around 50% in 2012. (8) There is still confusion about the impact of this on the financial markets. The “flash crash” of 2010, in which the Dow Jones lost almost 10% of its value in a few minutes was initially blamed on high-frequency trading, but later reports claimed that the AIs had actually mitigated the fall. The crash prompted the New York Stock Exchange to introduce “circuit breakers” which suspend trading of a stock whose price moves suspiciously quickly. The financial Armageddon which some pundits forecast has not arrived, and although there will undoubtedly be further shocks to the system, most market participants expect that new AI tools will continue to be developed for and absorbed by what has always been one of the most dynamic and aggressive sectors of the economy.
The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale
Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game
The mere existence of an AAA rating, or insurance from AIG, led to a FINANCE’S ALGORITHMS 131 false sense of security for many investors. Here, buy and sell signals can take on a life of their own, leading to momentum trading and herding.124 Algorithmic trading can create extraordinary instability and frozen markets when split-second trading strategies interact in unexpected ways.125 Consider, for instance, the flash crash of May 6, 2010, when the stock market lost hundreds of points in a matter of minutes.126 In a report on the crash, the CFTC and SEC observed that “as liquidity completely evaporated,” trades were “executed at irrational prices as low as one penny or as high as $100,000.”127 Traders had programmed split-second algorithmic strategies to gain a competitive edge, but soon found themselves in the position of a sorcerer’s apprentice, unable to control the technology they had developed.128 Though prices returned to normal the same day, there is no guarantee future markets will be so lucky.
David Golumbia, “High-Frequency Trading: Networks of Wealth and the Concentration of Power,” Social Semiotics 23 (2013): 278–299. 120. HFT often involves “very high order amounts; rapid order cancellation; a flat position at the end of the trading day; extracting very low margins per trade; and trading at ultra-fast speeds.” Andrew J. Keller, “Robocops: Regulating High Frequency Trading after the Flash Crash of 2010,” Ohio State Law Journal 73 (2012): 1459. 121. Matthew O’Brien, “High-Speed Trading Isn’t About Efficiency—It’s About Cheating,” The Atlantic, February 8, 2014. Available at http://www.the atlantic .com /business /archive /2014 /02/high-speed-trading -isnt-about-efficiency-its-about-cheating /283677/; Charles Schwab and Walt Bettinger, “Statement on High-Frequency Trading,” April 3, 2014.
Lone Survivor: The Eyewitness Account of Operation Redwing and the Lost Heroes of SEAL Team 10 by Marcus Luttrell, Patrick Robinson
It’s hard to survive when a door comes straight at you at one hundred miles an hour from point-blank range. Occasionally, if we had an element of doubt about the strength of the opposition behind that door, we would throw in a few flash-crashes, which do not explode and knock down walls or anything but do unleash a series of very loud, almost deafening bangs accompanied by searing white flashes. Very disorienting for our enemy. Right then our lead man would head the charge inside the building, which was always a shock for the residents. Even if we had not used the flash-crashes, they’d wake up real quick to face a group of big masked men, their machine guns leveled, shouting, daring anyone to make a move. Although these city houses were mostly two-story, Iraqis tend to sleep downstairs, all of them crowded together in the living room.
Warnings by Richard A. Clarke
active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bernie Madoff, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, cuban missile crisis, data acquisition, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Maui Hawaii, megacity, Mikhail Gorbachev, money market fund, mouse model, Nate Silver, new economy, Nicholas Carr, nuclear winter, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, Sam Altman, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, smart grid, statistical model, Stephen Hawking, Stuxnet, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K
While highly secretive, high-frequency trading is no sideshow oddity, it is consistently one of the most profitable trading strategies and accounts for 50 to 80 percent of daily global stock market volume, 1.6 billion shares bought per day in U.S. equities alone.22 But this effortless, automated trading can have a downside: weak AI can be fallible. On May 6, 2010, the U.S. stock market suffered a meltdown of epic proportions. During this “flash crash,” $1 trillion of value was wiped from the stock markets in less than ten minutes. Then, about as quickly as they slipped, the markets recovered. Later investigations suggested that errors in the autonomous algorithms of high-frequency traders were at least partly to blame. While AI has fundamentally shifted life on Wall Street, so too is it changing Main Street in new and potentially profound ways.
See also Upper Big Branch Mine disaster Cassandra system, 122, 125, 133, 137–38, 140–41 fatality rate, 123–24, 125–28 federal regulations, 124–30, 137–39 federal research program, 124–25 history of, 122–23 institutional refusal and, 137–42 Coastal wetlands, 41, 42–44 Coastal Wetlands Planning, Protection, and Restoration Act (CWPPRA), 43–44 Coding errors, 366–67 Cognitive biases, 34–35, 171–72 heuristics and, 189–91 Cognitive style, 14–15 Cold and the Dark, The: The World after Nuclear War (Sagan, Ehrlich, and Kennedy), 273–74 Cold Start doctrine, 264–65, 267, 270 Cold War, 25–26, 267–68, 271–74, 277–78 Collateralized debt obligations (CDOs), 147–48 Columbia University, 237, 238 Coming Plague, The (Garrett), 232 Complexity, and vulnerabilities, 366–67 Complexity Mismatch, 116, 178–79, 215, 299 Comprehensive Nuclear-Test-Ban Treaty (CTBT), 266 Computers in Crisis (Murray), 193–94 Conference on the Long-Term Worldwide Biological Consequences of Nuclear War (1983), 273 Congressional oversight committees, 355 Consensus science, 172–73 Continuous miners, 131–32 Conventional wisdom, 28, 355 Coplan, Jeremy, 186 Corvette hacks, 297–98 Cosmic Catastrophes (Morrison and Chapman), 302, 303, 304–5, 308–9, 312, 314–15, 319 Cost-benefit analysis, 361–62 Countervalue strike, 275, 278–79 Cowardice, 180 Cox, Jeff, 150 Cretaceous-Paleogene extinction event, 307–9 Crichton, Michael, 172–73 Crick, Francis, 328 Crimea, 285 CRISPR, 231–32, 326, 327, 329–49 CRISPR/Cas9, 326, 330–49, 360, 366–67 CRISPR Therapeutics, 333 Critical infrastructure protection (CIP), 287 Critics, 168, 170, 186–88 Crittenden, Gary, 143–44, 156 Crocker, Ryan, 73 Crocker’s Rules, 208–9 Cuban Missile Crisis, 26, 274 Cybersecurity, 283–300 Cynomolgus monkeys, 334–35 Daniel, 2 DARPA (Defense Advanced Research Projects Agency), 210, 382n Darwin, Charles, 325 Data, 36–37, 184 “Decay heat,” 85 Decision makers (the audience), 168, 170, 176–82, 380n false Cassandras and, 191–98 making the same mistakes, 189–91 responses, 358–64 scanning for problems, 354–56 Deep Impact (movie), 313–14 Deep learning, 210, 212 Demon core, 83 Deutsche Bank, 157 Devil’s advocates, 359, 379n DiBartolomeo, Dan, 105–6 Diffusion of responsibility, 176–77, 215, 235, 321, 348 Dinosaurs, 307–9 DiPascali, Frank, 107 Disembodied AI, 207 DNA, 326, 327–28, 336–37 Dole, Bob, 28–29 Dot-com bubble, 147 Doudna, Jennifer, 326–30, 335–36, 338–41, 343, 345, 346–49, 360 Drijfhout, Sybren, 253 Duchenne muscular dystrophy, 332 Duelfer, Charles, 30–31 Eagles, the (band), 305 Earth Institute, 238 Earthquake preparedness, 352–53 Ebola virus, 3, 219–20 Edwards, Edwin, 43 Eemian interglacial, 249, 250 Eggers, Dave, 39 Egypt, 59, 63, 66–67 Ehrlich, Paul, 192–93 Ein-Dor, Tsachi, 13, 186, 380n Einstein, Albert, 185 Eisman, Steve, 149, 152 Electricity Information Sharing and Analysis Center (E-ISAC), 287 Electric Power Research Institute, 286 Electromagnetic pulse (EMP), 274, 352 Embodied AI, 206 EMCON (emissions control), 29–30 Empire State Building, 260 Empirical method, 36, 184, 185 Energy policy, 243–44 Enron, 152 Enthoven, Alain, 361 Epidemic Intelligence Service, 354–55 Epidemic That Never Was, The (Neustadt), 196–97 EQ (emotional quotient), 183 Erasmus Medical Center, 222 Ermarth, Fritz, 27 Erroneous Consensus, 172–73 Ethics of AI growth, 205–6 of gene editing, 334, 339–40, 343 Eugenics, 342, 344 Evolution, 329–33 Expert Political Judgment (Tetlock), 13–15 Explainable AI, 210 Fairfield Greenwich Group, 108, 113 Fallujah, 68, 69 False Cassandras, 191–98 Famines, 192 Farmington Mine disaster, 127–28 Farson, Richard, 175 “Fast-failure” review, 357 Fatalism, 2 Fate of the States (Whitney), 153 Federal Bureau of Investigation (FBI), 8, 100, 112, 115 Federal Deposit Insurance Corporation (FDIC), 160 Federal Emergency Management Agency (FEMA), 40, 46–48, 51, 53–54, 323–24 Hurricane Pam exercise, 40, 47–49 Federal Reserve Bank, 159 Feedback loops, 16, 192–93 Fermi, Enrico, 373n Feynman, Richard, 240 Figueres, Christiana, 247 Financial Crisis Inquiry Commission, 162 Financial crisis of 2008, 143–65 Madoff fraud and SEC, 118–19 primary cause of, 147–48 Whitney and, 143–46, 148–50, 156–60 Flash Crash of 2010, 211 Fletcher, Charles, 256–57 Flood Control Act of 1928, 42 Flood Control Act of 1965, 46 Flu pandemic of 1918, 195, 198, 217, 221–24 Flu pandemic of 2009, 217–18, 221–22 Forbes, 154 Ford, Gerald, 196–97 Ford, Robert, 57–74 aid to Syrian opposition, 62–63, 64–65 ambassadorship in Egypt, 67 ambassadorship in Syria, 57–58 departure from Syria, 60–62 warning and prediction of, 64–74 Foreign Service, U.S., 57, 58, 67 Fortune, 146, 148–49, 161 Fossil fuels, 16, 42, 257–58.
The Spider Network: The Wild Story of a Math Genius, a Gang of Backstabbing Bankers, and One of the Greatest Scams in Financial History by David Enrich
Bernie Sanders, call centre, centralized clearinghouse, computerized trading, Credit Default Swap, Downton Abbey, Flash crash, Goldman Sachs: Vampire Squid, information asymmetry, interest rate derivative, interest rate swap, London Interbank Offered Rate, London Whale, Long Term Capital Management, Nick Leeson, Northern Rock, Occupy movement, performance metric, profit maximization, tulip mania, zero-sum game
Then, around two thirty in the afternoon of May 6 in New York, stock markets started nosediving. The Dow Jones Industrial Average plunged nearly 1,000 points within a few minutes, one of the largest drops ever. At first, market watchers stared at their screens, thinking they were witnessing the onset of another global stock market collapse. Then, just as quickly, the markets recovered most of their losses. The momentary event was soon dubbed the “flash crash.”* Despite the rebound, markets remained volatile; Hayes’s trading book yo-yoed up and down as much as $15 million a day. The couple remained in Malaysia, but any hopes for a relaxing vacation were dashed. When Hayes had to pee, he insisted that Tighe sit in front of the TV and shout if anything happened in the markets while he was relieving himself. One night, they went out to dinner. At the restaurant, Hayes hardly spoke to Tighe—he was cemented to his phone, checking the market and repeatedly calling Cecere in Tokyo.
252–53 Dow Jones Industrial Average, 286 Down syndrome, 328, 330 Drexel Burnham Lambert, 40–41 Ducrot, Yvan, x, 232, 233, 239 Dulles International Airport, 326 dumb money, 29 Eisler, Edward, 159 Elizabeth II of England, 12 Ellis, Paul, xi, 293, 445 Engel, Marcy, 75, 83 Enron, 200, 202, 266–67, 373–74 Ethiopia, 39–40 Euribor, 92 Eurodollar, 74–75 European debt crisis, 285 Ewan, John, xii, 67–68 background of, 67 at BBA, 68, 75–80 false Libor submissions, 181–83, 192–93, 195–96, 222 Hayes trial, 426–27 Libor investigations, 205–8, 270n, 272 Facebook, 110, 147, 381 Farah Pahlavi, 25 Farr, Clare, 45, 455–56 Farr, Sam, 119, 122, 345, 455–56 Farr, Terry, xii, 45–47 background of, 45–46, 119 compensation, 175–76, 378 firing of, 378 Hayes and, 45–47, 59, 87, 121–22, 213–14, 216–17, 224–25, 303–4, 344–46, 407–8 Hayes and Libor manipulation, 95, 108–9, 163–64, 168–70, 282 Libor investigations, 333–34, 344–46 Libor trial, 452–53, 455–56, 459 SFO criminal charges, 390–91, 401 SFO trial of Hayes, 407–8 Stenfors and, 119–22, 163, 170 switch trades, 169–77, 213–14 federal funds rate, 34, 34n, 70 Federal Reserve, base rate, 34, 34n, 70 Federal Reserve Bank of Minneapolis, 251–52 Federal Reserve Bank of New York, 195, 203, 280 Financial Conduct Authority (FCA), 407–8, 416–17 financial crisis of 2007–2008, 164–69, 181–82, 186–87, 248–49, 405 financial globalization, 71–72 Financial Services Authority (FSA), xiii, 63, 204 Libor investigation, 205–6, 257, 338–39, 344–46, 350–56 “light touch” strategy, 29–30, 196 Financial Times, 193 Financial Times Stock Exchange (FTSE), 67–68 Finma, 271 Finsbury Square, 54 Fisher, Paul, 207–8 Flash Crash of 2010, 286, 286n Fleet Street, 187–88 Foreign Exchange and Money Markets Committee (FXMMC), 78, 183, 195–96, 222, 244 forward rate agreements (FRAs), 160 FourFourTwo, 87 Frank, Anne, 228 Freud, Lucian, 49 Fulcrum Chambers, 347–49, 395, 415 futures contract, 31–32 “gardening leave,” 241, 265 Geithner, Tim, 195, 203–4, 357 Gensler, Francesca Danieli, 253–54 Gensler, Gary, xii, 246–49 background of, 246–47 at CFTC, 248–49, 253–54, 312–13 Barclays settlement, 357–59 Libor investigation, 264–65 Libor investigation, 399–400 personality of, 253, 254 Treasury undersecretary, 247–48 Gensler, Robert, 246–48 Gensler, Sam, 246 Gibson, Dunn & Crutcher, 316, 317–19, 400 Gilmour, Jim, xii, 118 arrest of, 364–65 background of, 118 compensation, 176, 315 firing of, 378 Hayes and Libor submissions, 163–64, 165–66, 365–66 Libor trial, 452–53, 455–56, 458–59 SFO criminal charges, 390–91, 401 Gilmour, Lisa, 118 Glass-Steagall Act, 19 gold standard, 32 Goldman Sachs, 21, 157–58 culture of, 158–59 Gensler at, 247 Hayes job offers, 157–59, 212 Libor report, 207–8 Golestan Palace, 25 Goodman, Colin, xi, 97–100 background of, 97–98 commissions, 130–32 FSA/CFTC interview, 355–56 Journal stories, 198 Justice criminal charges, 394–95 Justice investigation, 343–44 Libor manipulation, 129, 133–34, 148–49, 149n, 274 Libor run-throughs, 97–100, 116, 116n, 130–31, 130n, 385, 403, 431 Libor trial, 452–53, 455–56 SFO criminal charges, 404 suspension of, 354–55 Goodwin, Fred expansion strategy for RBS, 36–37, 53–54 Libor manipulation, 211–12 Gray’s Sporting Journal, 188 Great Depression, 19 Great Recession, 223, 256, 266 Greek government-debt crisis, 285 Green, David, xiii, 359–62, 366–67, 457 Green, Kevin, 301–2 Griffin, Ken, 20 Grosvenor House, 262 Grübel, Oswald, 212, 336 Gulf International Bank, 182–83 Haile Selassie, 39–40 Hammond, Scott, xiii, 318, 323, 400 Harris, Scott, 115 Harvard University, 26 Hatton, Ricky, 134–35, 136 Hawes, Neil, xiii, 410, 425–33, 436–37, 438, 453 Hayes, Anthony “Abbo,” xi, 235–36, 445 Hayes, Joshua, 337, 363, 370, 373, 383, 384, 388, 409, 415–16, 433–34 Hayes, Nick, ix, 10–11, 104, 152, 419n Hayes, Raymond, 12–13 Hayes, Robin, ix, 10, 110, 123, 140, 145, 146, 151–52, 321, 381 Hayes, Sandy, ix, 10–11, 13, 15, 16, 21, 305–6, 373, 419, 425 Hayes, Tom, ix Ainsworth and.
Destined for War: America, China, and Thucydides's Trap by Graham Allison
9 dash line, anti-communist, Berlin Wall, borderless world, Bretton Woods, British Empire, capital controls, Carmen Reinhart, conceptual framework, cuban missile crisis, currency manipulation / currency intervention, Deng Xiaoping, disruptive innovation, Donald Trump, facts on the ground, Flash crash, Francis Fukuyama: the end of history, game design, George Santayana, Haber-Bosch Process, industrial robot, Internet of things, Kenneth Rogoff, liberal world order, long peace, Mark Zuckerberg, megacity, Mikhail Gorbachev, Monroe Doctrine, mutually assured destruction, Nelson Mandela, one-China policy, Paul Samuelson, Peace of Westphalia, purchasing power parity, RAND corporation, Ronald Reagan, Scramble for Africa, selection bias, Silicon Valley, Silicon Valley startup, South China Sea, special economic zone, spice trade, the rule of 72, The Wealth of Nations by Adam Smith, too big to fail, trade route, UNCLOS, Washington Consensus, zero-sum game
He announces that until payment is received, he is imposing tariffs on Chinese companies that have been exploiting stolen intellectual property, including telecommunications company Huawei and appliance manufacturer Midea. China retaliates with its own tariffs on equivalent American products. As they move up this escalation ladder, US financial markets suffer a series of cyber glitches similar to the 2010 “flash crash” when high-frequency traders caused the stock market to lose $1 trillion in a half hour (although it quickly recovered).35 Unlike that singular incident, such flash crashes happen repeatedly over the course of a week, and though each time the markets bounce back, they do not recover their losses. In investigating the cause, the FBI discovers that malicious software has been inserted in critical financial systems. While the digital signatures point to China, agents cannot dismiss the possibility of a false flag.
Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts by David Gerard
altcoin, Amazon Web Services, augmented reality, Bernie Madoff, bitcoin, blockchain, Blythe Masters, Bretton Woods, clean water, cloud computing, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, distributed ledger, Ethereum, ethereum blockchain, Extropian, fiat currency, financial innovation, Firefox, Flash crash, Fractional reserve banking, index fund, Internet Archive, Internet of things, Kickstarter, litecoin, M-Pesa, margin call, Network effects, peer-to-peer, Peter Thiel, pets.com, Ponzi scheme, Potemkin village, prediction markets, quantitative easing, RAND corporation, ransomware, Ray Kurzweil, Ross Ulbricht, Ruby on Rails, Satoshi Nakamoto, short selling, Silicon Valley, Silicon Valley ideology, Singularitarianism, slashdot, smart contracts, South Sea Bubble, tulip mania, Turing complete, Turing machine, WikiLeaks
Some banks in the UK241 and Australia242 have closed accounts for Bitcoin-related activity – it has a stigma as a currency widely used for questionable transactions. From the first bubble to the second After the 2013 bubble and 2014 price crash, people lost interest and the trading volume declined. The price slowly rose again and was $630 by mid-October 2016 and bubbled to a peak of $3000 in June 2017 – but large holders trying to sell their bitcoins risk causing a flash crash; the “price” is not realisable for any substantial quantity. The market remains thin enough that single traders can send the price up or down $30,243 and an April 2017 crash from $1180 to 6 cents (due to configuration errors on Coinbase’s GDAX exchange) was courtesy 100 BTC of trades.244 As well as drugs and ransomware, non-speculative usage includes various “Republic of Bitcoin” schemes run by the infamous Russian MMM concern, who perpetrated the largest Ponzi in history in the 1990s.
Profiting Without Producing: How Finance Exploits Us All by Costas Lapavitsas
"Robert Solow", Andrei Shleifer, asset-backed security, bank run, banking crisis, Basel III, borderless world, Branko Milanovic, Bretton Woods, business cycle, capital controls, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, computer age, conceptual framework, corporate governance, credit crunch, Credit Default Swap, David Graeber, David Ricardo: comparative advantage, disintermediation, diversified portfolio, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, financial deregulation, financial independence, financial innovation, financial intermediation, financial repression, Flash crash, full employment, global value chain, global village, High speed trading, Hyman Minsky, income inequality, inflation targeting, informal economy, information asymmetry, intangible asset, job satisfaction, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, London Interbank Offered Rate, low skilled workers, M-Pesa, market bubble, means of production, money market fund, moral hazard, mortgage debt, Network effects, new economy, oil shock, open economy, pensions crisis, price stability, Productivity paradox, profit maximization, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, race to the bottom, regulatory arbitrage, reserve currency, Robert Shiller, Robert Shiller, savings glut, Scramble for Africa, secular stagnation, shareholder value, Simon Kuznets, special drawing rights, Thales of Miletus, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, total factor productivity, trade liberalization, transaction costs, union organizing, value at risk, Washington Consensus, zero-sum game
., appendix 2. 22 BIS, ‘Amendment to the Capital Accord to Incorporate Market Risks’, January 1996. 23 For standard analysis, see Anthony Saunders and Linda Allen, Credit Risk Measurement, 2nd ed., New York: John Wiley and Sons, 2002, pp. 84–106; and Darrell Duffie and Kenneth Singleton, Credit Risk: Pricing, Measurement, and Management, Princeton, NJ: Princeton University Press, 2003, pp. 31–42. 24 David Easley et al., ‘The Microstructure of the Flash Crash: Flow Toxicity, Liquidity Crashes, and the Probability of Informed Trading’, The Journal of Portfolio Management 37:2, 2011. 25 BIS, ‘International Convergence of Capital Measurement and Capital Standards: A Revised Framework’, Comprehensive Version’, June 2006. 26 For further discussion, see Steven Zhu and Michael Pykhtin, ‘Measuring Counterparty Risk for Trading Products under Basel II’, in The Basel Handbook: A Guide for Financial Practitioners, 2nd ed., ed.
Dymski, Gary, ‘Genie out of the Bottle: The Evolution of Too-Big-to-Fail Policy and Banking Strategy in the US’, paper presented at the Post-Keynesian Studies Group meeting at SOAS, University of London, 8 June 2011; available at post-keynesian.net. Dymski, Gary, ‘Racial Exclusion and the Political Economy of the Sub-Prime Crisis’, Historical Materialism 17:2, 2009, pp. 149–79; also published in Lapavitsas (ed.), Financialisation in Crisis, pp. 51–82. Easley, David, Marcos Lopez de Prado, and Marueen O’Hara, ‘The Microstructure of the Flash Crash: Flow Toxicity, Liquidity Crashes, and the Probability of Informed Trading’, The Journal of Portfolio Management 37:2, 2011, pp. 118–28. Eatwell, John, and Lance Taylor, Global Finance at Risk, Cambridge: Polity Press, 2000. Edwards, Franklin R., The New Finance: Regulation and Financial Stability, Washington, DC: AEI Press, 1996. Edwards, Jeremy, and Sheilagh Ogilvie, ‘Universal Banks and German Industrialization: A Reappraisal’, Economic History Review 49:3, 1996, pp. 427–46.
Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari
23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, drone strike, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, job automation, John Markoff, Kevin Kelly, lifelogging, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Monkeys Reject Unequal Pay, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, The Future of Employment, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!, zero-sum game
Three years previously, on 6 May 2010, the New York stock exchange underwent an even sharper shock. Within five minutes – from 14:42 to 14:47 – the Dow Jones dropped by 1,000 points, wiping out $1 trillion. It then bounced back, returning to its pre-crash level in a little over three minutes. That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. We know algorithms were to blame, but we are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings, who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers.5 And these lawyers won’t necessarily be human.
Goode’ 257–61, 358, 387, 388 Bible 46; animal kingdom and 76–7, 93–5; Book of Genesis 76–8, 77, Dataism and 381; 93–4, 97; composition of, research into 193–5; evolution and 102; homosexuality and 192–3, 195, 275; large-scale human cooperation and 174; Old Testament 48, 76; power of shaping story 172–3; scholars scan for knowledge 235–6; self-absorption of monotheism and 173, 174; source of authority 275–6; unique nature of humanity, promotes 76–8 biological poverty line 3–6 biotechnology 14, 43–4, 46, 98, 269, 273, 375 see also individual biotech area Bismarck, Otto von 31, 271 Black Death 6–8, 6, 7, 11, 12 Borges, Jorge Luis: ‘A Problem’ 299–300 Bostrom, Nick 327 Bowden, Mark: Black Hawk Down 255 bowhead whale song, spectrogram of 358, 358 brain: Agricultural Revolution and 156–7, 160; artificial intelligence and 278, 278; biological engineering and 44; brain–computer interfaces 48, 54, 353, 359; consciousness and 105–13, 116, 118–19, 121–4, 125; cyborg engineering and 44–5; Dataism and 368, 393, 395; free will and 282–8; happiness and 37, 38, 41; self and 294–9, 304–5; size of 131, 132; transcranial stimulators and manipulation of 287–90; two hemispheres 291–4 brands 156–7, 159–60, 159, 162 Brezhnev, Leonid 273 Brin, Sergey 28, 336 Buddhism 41, 42, 94, 95, 181, 185, 187, 221, 246, 356 Calico 24, 28 Cambodia 264 Cambridge Declaration on Consciousness, 2012 122 capitalism 28, 183, 206, 208–11, 216–17, 218–19, 251–2, 259, 273–4, 369–73, 383–6, 396 see also economics/economy Caporreto, Battle of, 1917 301 Catholic Church 147, 183; Donation of Constantine 190–2, 193; economic and technological innovations and 274; marriage and 26; papal infallibility principle 147, 190, 270–1; Protestant revolt against 185–7; religious intolerance and 198; Thirty Years War and 242, 243, 246; turns from creative into reactive force 274–5 see also Bible and Christianity Ceauçescu, Nicolae 133–4, 134, 135–6, 137 Charlie Hebdo 226 Château de Chambord, Loire Valley, France 62, 62 Chekhov Law 17, 18, 55 child mortality 10, 33, 175 childbirth, narration of 297–8, 297 China 1, 269; biotech and 336; Civil War 263; economic growth and 206, 207, 210; famine in 5, 165–6; Great Leap Forward 5, 165–6, 376; Great Wall of 49, 137–8, 178; liberalism, challenge to 267–8; pollution in 213–14; Taiping Rebellion, 1850–64 271; Three Gorges Dam, building of 163, 188, 196 Chinese river dolphin 188, 196, 395 Christianity: abortion and 189; animal welfare and 90–6; change from creative to reactive force 274–6; economic growth and 205; homosexuality and 192–3, 225–6, 275–6; immortality and 22 see also Bible and Catholic Church Chukwu 47 CIA 57, 160, 293–4 Clever Hans (horse) 128–30, 129 climate change 20, 73, 151, 213, 214–17, 376, 377, 397 Clinton, Bill 57 Clovis, King of France 227, 227 Cognitive Revolution 156, 352, 378 Cold War 17, 34, 149, 206, 266, 372 cold water experiment (Kahneman) 294–5, 338 colonoscopy study (Kahneman and Redelmeier) 296–7 Columbus, Christopher 197, 359, 380 Communism 5, 56, 57, 98, 149, 165, 166, 171, 181; cooperation and 133–7, 138; Dataism and 369, 370–3, 394, 396; economic growth and 206, 207, 208, 217, 218; liberalism, challenge to 264–6, 271–4; religion and 181, 182, 183; Second World War and 263 computers: algorithms and see algorithms; brain–computer interfaces 48, 54, 287, 353, 359; consciousness and 106, 114, 117–18, 119, 120; Dataism and 368, 375, 388, 389 Confucius 46, 267, 391–2; Analects 269, 270 Congo 9, 10, 15, 19, 168, 206, 257–61, 387, 388 consciousness: animal 106–7, 120–32; as biologically useless by-product of certain brain processes 116–17; brain and locating 105–20; computer and 117–18, 119, 120, 311–12; current scientific thinking on nature of 107–17; denying relevance of 114–16; electrochemical signatures of 118–19; intelligence decoupling from 307–50, 352, 397; manufacturing new states of 360, 362–3, 393; positive psychology and 360; Problem of Other Minds 119–20; self and 294–5; spectrum of 353–9, 359, 360; subjective experience and 110–20; techno-humanism and 352, 353–9 cooperation, intersubjective meaning and 143–51, 155–77; power of human 131–51, 155–77; revolution and 132–7; size of group and 137–43 Cope, David 324–5 credit 201–5 Crusades 146–8, 149, 150–1, 190, 227–8, 240, 305 Csikszentmihalyi, Mihaly 360 customer-services departments 317–18 cyber warfare 16, 17, 59, 309–10 Cyborg 2 (movie) 334 cyborg engineering 43, 44–5, 66, 275, 276, 310, 334 Cyrus, King of Persia 172, 173 Daoism 181, 221 Darom, Naomi 231 Darwin, Charles: evolutionary theory of 102–3, 252, 271, 372, 391; On the Origin of Species 271, 305, 367 data processing: Agricultural Revolution and 156–60; Catholic Church and 274; centralised and distributed (communism and capitalism) 370–4; consciousness and 106–7, 113, 117; democracy, challenge to 373–7; economy and 368–74; human history viewed as a single data-processing system 377–81, 388; life as 106–7, 113, 117, 368, 377–81, 397; stock exchange and 369–70; value of human experience and 387–9; writing and 157–60 see also algorithms and Dataism Dataism 366, 367–97; biological embracement of 368; birth of 367–8; computer science and 368; criticism of 393–5; economy and 368–74; humanism, attitude towards 387–8; interpretation of history and 377–80; invisible hand of the data flow, belief in the 385–7; politics and 370–4, 375–6; power/control over data 373–7; privacy and 374, 384–5; religion of 380–5; value of experience and 387–9 Dawkins, Richard 305 de Grey, Aubrey 24, 25, 27 Deadline Corporation 331 death, 21–9 see also immortality Declaration of the Rights of Man and of the Citizen, The 308–9 Deep Blue 320, 320 Deep Knowledge Ventures 322, 323 DeepMind 321 Dehaene, Stanislas 116 democracy: Dataism and 373–5, 376, 377, 380, 391, 392, 396; evolutionary humanism and 253–4, 262–3; humanist values and 226–8; liberal humanism and 248–50, 262–7, 268; technological challenge to 306, 307–9, 338–41 Dennett, Daniel 116 depression 35–6, 39, 40, 49, 54, 67, 122–4, 123, 251–2, 287, 357, 364 Descartes, René 107 diabetes 15, 27 Diagnostic and Statistical Manual of Mental Disorders (DSM) 223–4 Dinner, Ed 360 Dix, Otto 253; The War (Der Krieg) (1929–32) 244, 245, 246 DNA: in vitro fertilisation and 52–4; sequencing/testing 52–4, 143, 332–4, 336, 337, 347–8, 392; soul and 105 doctors, replacement by artificial intelligence of 315, 316–17 Donation of Constantine 190–2, 193 drones 288, 293, 309, 310, 310, 311 drugs: computer-assisted methods for research into 323; Ebola and 203; pharmacy automation and 317; psychiatric 39–41, 49, 124 Dua-Khety 175 dualism 184–5, 187 Duchamp, Marcel: Fountain 229–30, 233, 233 Ebola 2, 11, 13, 203 economics/economy: benefits of growth in 201–19; cooperation and 139–40; credit and 201–5; Dataism and 368–73, 378, 383–4, 385–6, 389, 394, 396, 397; happiness and 30, 32, 33, 34–5, 39; humanism and 230, 232, 234, 247–8, 252, 262–3, 267–8, 269, 271, 272, 273; immortality and 28; paradox of historical knowledge and 56–8; technology and 307–8, 309, 311, 313, 318–19, 327, 348, 349 education 39–40, 168–71, 231, 233, 234, 238, 247, 314, 349 Eguía, Francisco de 8 Egypt 1, 3, 67, 91, 98, 141, 142, 158–62, 170, 174–5, 176, 178–9, 206; Lake Fayum engineering project 161–2, 175, 178; life of peasant in ancient 174–5, 176; pharaohs 158–60, 159, 174, 175, 176; Revolution, 2011 137, 250; Sudan and 270 Egyptian Journalists Syndicate 226 Einstein, Albert 102, 253 electromagnetic spectrum 354, 354 Eliot, Charles W. 309 EMI (Experiments in Musical Intelligence) 324–5 Engels, Friedrich 271–2 Enki 93, 157, 323 Epicenter, Stockholm 45 Epicurus 29–30, 33, 35, 41 epilepsy 291–2 Erdoğan, Recep Tayyip 207 eugenics 52–3, 55 European Union 82, 150, 160, 250, 310–11 evolution 37–8, 43, 73–4, 75, 78, 79–83, 86–7, 89, 102–5, 110, 131, 140, 150, 203, 205, 252–3, 260, 282, 283, 297, 305, 338, 359, 360, 388, 391 evolutionary humanism 247–8, 252–7, 260–1, 262–3, 352–3 Facebook 46, 137, 340–1, 386, 387, 392, 393 famine 1–6, 19, 20, 21, 27, 32, 41, 55, 58, 166, 167, 179, 205, 209, 219, 350 famine, plague and war, end of 1–21 First World War, 1914–18 9, 14, 16, 52, 244, 245, 246, 254, 261–2, 300–2, 301, 309, 310 ‘Flash Crash’, 2010 313 fMRI scans 108, 118, 143, 160, 282, 332, 334, 355 Foucault, Michel: The History of Sexuality 275–6 France: famine in, 1692–4 3–4, 5; First World War and 9, 14, 16; founding myth of 227, 227; French Revolution 155, 308, 310–11; health care and welfare systems in 30, 31; Second World War and 164, 262–3 France, Anatole 52–3 Frederick the Great, King 141–2 free will 222–3, 230, 247, 281–90, 304, 305, 306, 338 freedom of expression 208, 382, 383 freedom of information 382, 383–4 Freudian psychology 88, 117, 223–4 Furuvik Zoo, Sweden 125–6 Future of Employment, The (Frey/Osborne) 325–6 Gandhi, Indira 264, 266 Gazzaniga, Professor Michael S. 292–3, 295 GDH (gross domestic happiness) 32 GDP (gross domestic product) 30, 32, 34, 207, 262 genetic engineering viii, 23, 25, 41, 44, 48, 50, 52–4, 212, 231, 274, 276, 286, 332–8, 347–8, 353, 359, 369 Germany 36; First World War and 14, 16, 244, 245, 246; migration crisis and 248–9, 250; Second World War and 255–6, 262–3; state pensions and social security in 31 Gilgamesh epic 93 Gillies, Harold 52 global warming 20, 213, 214–17, 376, 377, 397 God: Agricultural Revolution and 95, 96, 97; Book of Genesis and 77, 78, 93–4, 97, 98; Dataism and 381, 382, 386, 389, 390, 393; death of 67, 98, 220, 234, 261, 268; death/immortality and 21, 22, 48; defining religion and 181, 182, 183, 184; evolutionary theory and 102; hides in small print of factual statements 189–90, 195; homosexuality and 192–3, 195, 226, 276; humanism and 220, 221, 222, 224, 225, 226, 227, 228, 229, 234–7, 241, 244, 248, 261, 268, 270, 271, 272, 273, 274, 276, 305, 389, 390–1; intersubjective reality and 143–4, 145, 147–9, 172–3, 179, 181, 182, 183, 184, 189–90, 192–3, 195; Middle Ages, as source of meaning and authority in 222, 224, 227, 228, 235–7, 305; Newton myth and 97, 98; religious fundamentalism and 220, 226, 268, 351; Scientific Revolution and 96, 97, 98, 115; war narratives and 241, 244 gods: Agricultural Revolution and theist 90–6, 97, 98, 156–7; defining religion and 180, 181, 184–5; disappearance of 144–5; dualism and 184–5; Epicurus and 30; humans as (upgrade to Homo Deus) 21, 25, 43–9, 50, 55, 65, 66, 98; humanism and 98; intersubjective reality and 144–5, 150, 155, 156–7, 158–60, 161–3, 176, 178–80, 323, 352; modern covenant and 199–200; new technologies and 268–9; Scientific Revolution and 96–7, 98; spirituality and 184–5; war/famine/plague and 1, 2, 4, 7, 8, 19 Google 24, 28, 114, 114, 150, 157, 163, 275, 312, 321, 322, 330, 334–40, 341, 384, 392, 393; Google Baseline Study 335–6; Google Fit 336; Google Flu Trends 335; Google Now 343; Google Ventures 24 Gorbachev, Mikhail 372 Götze, Mario 36, 63 Greece 29–30, 132, 173, 174, 228–9, 240, 265–6, 268, 305 greenhouse gas emissions 215–16 Gregory the Great, Pope 228, 228 guilds 230 hackers 310, 313, 344, 382–3, 393 Hadassah Hospital, Jerusalem 287 Hamlet (Shakespeare) 46, 199 HaNasi, Rabbi Yehuda 94 happiness 29–43 Haraway, Donna: ‘A Cyborg Manifesto’ 275–6 Harlow, Harry 89, 90 Harris, Sam 196 Hassabis, Dr Demis 321 Hattin, Battle of, 1187 146, 147 Hayek, Friedrich 369 Heine, Steven J. 354–5 helmets: attention 287–90, 362–3, 364; ‘mind-reading’ 44–5 Henrich, Joseph 354–5 Hercules 43, 176 Herodotus 173, 174 Hinduism 90, 94, 95, 181, 184, 187, 197, 206, 261, 268, 269, 270, 348, 381 Hitler, Adolf 181, 182, 255–6, 352–3, 375 Holocaust 165, 257 Holocene 72 Holy Spirit 227, 227, 228, 228 Homo deus: Homo sapiens upgrade to 43–9, 351–66; techno-humanism and 351–66 Homo sapiens: conquer the world 69, 100–51; end famine, plague and war 1–21; give meaning to the world 153–277; happiness and 29–43; Homo deus, upgrade to 21, 43–9; immortality 21–9; loses control, 279–397; problems with predicting history of 55–64 homosexuality 120, 138–9, 192–3, 195, 225–6, 236, 275 Hong Xiuquan 271 Human Effectiveness Directorate, Ohio 288 humanism 65–7, 98, 198, 219; aesthetics and 228–9, 228, 233, 233, 241–6, 242, 245; economics and 219, 230–1, 232, 232; education system and 231, 233, 233, 234; ethics 223–6, 233; evolutionary see evolutionary humanism; formula for knowledge 237–8, 241–2; homosexuality and 225–6; liberal see liberal humanism; marriage and 223–5; modern industrial farming, justification for 98; nationalism and 248–50; politics/voting and 226–7, 232, 232, 248–50; revolution, humanist 220–77; schism within 246–57; Scientific Revolution gives birth to 96–9; socialist see socialist humanism/socialism; value of experience and 257–61; techno-humanism 351–66; war narratives and 241–6, 242, 245, 253–6; wars of religion, 1914–1991 261–7 hunter-gatherers 34, 60, 75–6, 90, 95, 96–7, 98, 140, 141, 156, 163, 169, 175, 268–9, 322, 355, 360, 361, 378 Hussein, Saddam 18, 310 IBM 315–16, 320, 330 Iliescu, Ion 136, 137 ‘imagined orders’ 142–9 see also intersubjective meaning immigration 248–50 immortality 21–9, 30, 43, 47, 50, 51, 55, 56, 64, 65, 67, 138, 179, 268, 276, 350, 394–5 in vitro fertilisation viii, 52–3 Inanna 157, 323 India: drought and famine in 3; economic growth in modern 205–8, 349; Emergency in, 1975 264, 266; Hindu revival, 19th-century 270, 271, 273; hunter-gatherers in 75–6, 96; liberalism and 264, 265; population growth rate 205–6; Spanish Flu and 9 individualism: evolutionary theory and 103–4; liberal idea of undermined by twenty-first-century science 281–306; liberal idea of undermined by twenty-first-century technology 327–46; self and 294–304, 301, 303 Industrial Revolution 57, 61, 270, 274, 318, 319, 325, 374 inequality 56, 139–43, 262, 323, 346–50, 377, 397 intelligence: animal 81, 82, 99, 127–32; artificial see artificial intelligence; cooperation and 130–1, 137; decoupling from consciousness 307–50, 352, 397; definition of 130–1; development of human 99, 130–1, 137; upgrading human 348–9, 352 see also techo-humanism; value of consciousness and 397 intelligent design 73, 102 internet: distribution of power 374, 383; Internet-of-All-Things 380, 381, 382, 388, 390, 393, 395; rapid rise of 50 intersubjective meaning 143–51, 155–77, 179, 323, 352 Iraq 3, 17, 40, 275 Islam 8, 18, 21, 22, 64, 137, 188, 196, 205, 206, 207, 221, 226, 248, 261, 268, 269, 270, 271, 274, 275, 276, 351, 392; fundamentalist 18, 196, 226, 268, 269, 270, 275, 351 see also Muslims Islamic State (IS) 275, 351 Isonzo battles, First World War 300–2, 301 Israel 48, 96, 225–6, 249 Italy 262, 300–2, 301 Jainism 94–5 Jamestown, Virginia 298 Japan 30, 31, 33, 34, 207, 246, 349 Jefferson, Thomas 31, 192, 249, 282, 305 Jeopardy!
The New Depression: The Breakdown of the Paper Money Economy by Richard Duncan
asset-backed security, bank run, banking crisis, banks create money, Ben Bernanke: helicopter money, Bretton Woods, business cycle, currency manipulation / currency intervention, debt deflation, deindustrialization, diversification, diversified portfolio, fiat currency, financial innovation, Flash crash, Fractional reserve banking, income inequality, inflation targeting, Joseph Schumpeter, laissez-faire capitalism, liquidity trap, market bubble, market fundamentalism, mass immigration, Mexican peso crisis / tequila crisis, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, private sector deleveraging, quantitative easing, reserve currency, Ronald Reagan, savings glut, special drawing rights, The Great Moderation, too big to fail, trade liberalization
In early May, the results of the bank stress test came as a relief to markets. Nonetheless, the impact on stock prices of the creation and injection of $1.75 trillion in new fiat money into the credit market should not be underappreciated–particularly considering movements in stock prices after QE1 came to an end. Quantitative Easing: Round Two Five weeks after QE1 ended on March 31, 2010, the U.S. stock market experienced a flash crash when, in one day, stock prices plummeted 10 percent before recovering to close down only 3 percent on the day. By early July the stock market was down 14 percent from its post-QE1 peak of 11,205 on April 26. That drop destroyed trillions of dollars in paper wealth, producing a negative wealth effect that immediately impacted consumption. The 2010 “soft patch” had begun. By August, most economic indicators were flashing red, and concerns over the risks of a double-dip recession began to take hold.
Crashed: How a Decade of Financial Crises Changed the World by Adam Tooze
Affordable Care Act / Obamacare, Apple's 1984 Super Bowl advert, Asian financial crisis, asset-backed security, bank run, banking crisis, Basel III, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, Boris Johnson, break the buck, Bretton Woods, BRICs, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, dark matter, deindustrialization, desegregation, Detroit bankruptcy, Dissolution of the Soviet Union, diversification, Doha Development Round, Donald Trump, Edward Glaeser, Edward Snowden, en.wikipedia.org, energy security, eurozone crisis, Fall of the Berlin Wall, family office, financial intermediation, fixed income, Flash crash, forward guidance, friendly fire, full employment, global reserve currency, global supply chain, global value chain, Goldman Sachs: Vampire Squid, Growth in a Time of Debt, housing crisis, Hyman Minsky, illegal immigration, immigration reform, income inequality, interest rate derivative, interest rate swap, Kenneth Rogoff, large denomination, light touch regulation, Long Term Capital Management, margin call, Martin Wolf, McMansion, Mexican peso crisis / tequila crisis, mittelstand, money market fund, moral hazard, mortgage debt, mutually assured destruction, negative equity, new economy, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shale / tar sands, old-boy network, open economy, paradox of thrift, Peter Thiel, Ponzi scheme, predatory finance, price stability, private sector deleveraging, purchasing power parity, quantitative easing, race to the bottom, reserve currency, risk tolerance, Ronald Reagan, savings glut, secular stagnation, Silicon Valley, South China Sea, sovereign wealth fund, special drawing rights, structural adjustment programs, The Great Moderation, Tim Cook: Apple, too big to fail, trade liberalization, upwardly mobile, Washington Consensus, We are the 99%, white flight, WikiLeaks, women in the workforce, Works Progress Administration, yield curve, éminence grise
The euro plunged, losing 2.5 cents by early afternoon.52 What was going on between terminals on both sides of the Atlantic that afternoon would later become a matter of dispute in American courtrooms. But at 2:32 p.m. the market went into spasm.53 Half an hour later, by 3:05 p.m., the main American stock markets had given up 6 percent of their value, erasing $1 trillion from portfolios. As panicked traders fled to quality, demand for US Treasurys surged, driving yields down from 3.6 to 3.25 percent in a matter of minutes. Thanks to the transatlantic time difference, news of the “flash crash” hit the BlackBerrys of the ECB board as they sat down to dinner in Lisbon. Eighteen months on from the Lehman crisis, it seemed that the delay in bailing out Greece was about to precipitate a second financial catastrophe. Even the head of the Bundesbank, the tough-talking Axel Weber, realized that the ECB could not maintain the hard line that Trichet had taken that morning. The “ECB must buy government bonds,” he exclaimed across the dinner table.54 For the conservative team at the head of the ECB, it was a dramatic moment.
Earlier and more sharply than in any other recession in recent history, the fiscal screw was turned. On both sides of the Atlantic the result was to stunt the recovery. I The most remarkable instance of austerity contagion was the UK. The hotly contested election that would bring an end to the long reign of New Labour concluded on May 6, 2010, the same day that banks burned in Athens and the flash crash sent US financial markets plunging. Fiscal politics were key to both the election and the coalition negotiations that followed. Britain had been among those hardest hit by 2007–2008. Though the Bank of England, unlike the ECB, never let there be any question of official support for UK Treasury debt, and though the UK maintained its credit rating, sterling plummeted against the dollar and the euro.
See Freddie Mac Federal Housing Enterprises Financial Safety and Soundness Act of 1992, 47 Federal Reserve AIG bailout and, 178 China’s yuan panic of 2015 and, 606–7 CHOICE Act and, 589–90 Comprehensive Capital Analysis and Review, 310–11 Dodd-Frank Act and, 304, 305 dual mandate of, 366 interest rate hikes, 2015-2018, 590, 608 Lehman collapse and, 176–77 as liquidity providers of last resort to global banking system, 9–10, 11–12, 202–3, 206–21 low interest rate policy, investment climate created by, 472–75 quantitative easing (See quantitative easing (QE)) short-term interest rate policy of, 2000–2006, 37–38, 55–56, 69–70 stress tests, 298–301 Volcker shock, 11, 37, 43–44, 46, 50, 68 Fekter, Maria, 404 Ferguson, Niall, 35, 346–47, 368 FIAT, 123 financial crisis of 2007–2009, 1–4, 143–69, 609–10, 613, 614 ABCP market, implosion of, 146–47 AIG and, 150–52, 178–79 automobile industry and, 157–59, 449–50 Bear Stearns collapse and, 147–48 bursting of housing bubble and initial mortgage lender failures, 143–45 China and, 7, 242–54 credit default swaps and, 150–51 dollar-funding shortage for European banks and, 8, 154–55, 203–6 in East and Southeast Asia, 257–61 in Eastern Europe, 220–38 election of 2016, impact on, 566–67, 574–75 European banks funding crisis and, 8, 154–55, 203–6 European banks US mortgage market exposure, 73–75 Federal Reserve as liquidity provider to global banking system, 9–10, 11–12, 202–3 G20 and, 261–75 global nature of, 5–6, 159–60 household wealth lost in, 156–57 Keynesian macroeconomics as inadequate to understanding, 8–9 Lehman Brothers collapse and, 149, 176–77 lending, collapse in, 155–56 liquidity crisis (See liquidity crisis) money market mutual funds (MMF) and, 152–53 Northern Rock collapse and, 145–46 Rajan’s warning of financial risks and, 67–68 regulatory changes and, 69 repo market, run on, 146–50 syndicated loans, drop in, 153–54 Wall Street versus Main Street in, 164–65 financial reform, 301–17 Basel III accord, 311–14 Dodd-Frank Act of 2010, 302–9 entanglement of regulators, law firms and banks, 309–11 Larosière committee recommendations, 314–15 Financial Services Authority (FSA), 81, 541 Financial Services Modernization Act of 1999, 68, 82 Financial Stability Board, 269–70, 311 Financial Stability Forum (FSF), 89 Financial Stability Oversight Council, 303, 309 Financial Times, 481, 525, 526, 587 Fink, Larry, 481 Finland, 105, 421 fiscal compact. See eurozone financial crisis Fisher, Richard, 476 Fitch, 49, 64, 338, 536 fixed exchange rate system, 32–35, 39, 92–93 Fix the Debt campaign, 464 flash crash, 341 FN. See National Front (FN) foreclosures, 156, 280, 281, 306, 321, 366 Foreign Affairs, 35 Fortis, 154, 185, 209, 358 France, 167, 322 bank bailouts in, 193, 194 East European crises and, 4, 138, 502 elections 2012, 429 elections 2017, 562 European elections 2014, 513 frank fort policy of, 92 Greek bailout and, 325–26, 328, 531 S&P downgrades sovereign debt of, 421 Spanish crisis 2012 and, 434–35 See also eurozone financial crisis; Sarkozy, Nicolas Frank, Barney, 176, 302, 303, 305 Freddie Mac, 46, 55–56, 63–64, 172 bailout of, 172–75 Russian sale of bonds of, 137 See also government-sponsored enterprises (GSEs) Fridman, Mikhail, 224–25 Friedman, Milton, 38–39 Froman, Michael, 200–201 FSA.
The Asylum: The Renegades Who Hijacked the World's Oil Market by Leah McGrath Goodman
anti-communist, Asian financial crisis, automated trading system, banking crisis, barriers to entry, Bernie Madoff, computerized trading, corporate governance, corporate raider, credit crunch, Credit Default Swap, East Village, energy security, Etonian, family office, Flash crash, global reserve currency, greed is good, High speed trading, light touch regulation, market fundamentalism, peak oil, Peter Thiel, pre–internet, price mechanism, profit motive, regulatory arbitrage, reserve currency, rolodex, Ronald Reagan, side project, Silicon Valley, upwardly mobile, zero-sum game
Do you think you can really cut Corporate America?’ You’d think being the vice chairman of the world’s biggest oil market would count for something, but I guess not. So I retired. Now I have a lot of season tickets to games and watch a lot of live sports.” Vinnie Viola Still a trader, Viola runs a Madison Avenue market-making firm and is a well-known champion of the kind of high-speed trading blamed for the disastrous “flash crash” of May 2010, when the stock market inexplicably plummeted, bleeding hundreds of billions of dollars, before bouncing back in just twenty minutes. He has continued opening other trading shops and running his banks in Texas. It’s a far cry from where he started, betting on gasoline in the pits with his boyhood friends from Brooklyn. “I don’t know what I think of my time at Nymex. I have gone on to do lots of other things.
See also specific exchanges defined, 381 exemptions, 227–28 “Bona Fide Hedging” and Goldman Sachs, 183–85 defined, 381 expiration, 43–44 defined, 381 Exxon (ExxonMobil), 7, 71, 72, 140, 336, 343 Faber, Joel, 70, 72, 126 Facciponti, Joseph, 360 Faison, Les, 162 Falco, Robert, 259–60 Federal Bureau of Investigation (FBI), 128–29, 136–39, 244–45 Federal Energy Regulatory Commission (FERC), 331, 332, 347 defined, 381 Federal Reserve, 59, 144, 194, 233, 356–57 Federal Trade Commission (FTC), 347–48 defined, 381 Federbush, Charles, 284, 319–20 Feinstein, Dianne, 348 Fiduciary Trust, 217 Fight Club (movie), 20 Filer, Herbert, 82 Filer, Schmidt & Company, 82 FI Magazine, 225 Fimat Group, 264 financial crisis of 2008, 194–95, 206n, 222, 324, 343, 348, 356–57, 362 Financial Times, 106, 203 firearms (guns), 27, 45 Fisher, Harold, 68, 173–74 Fisher, Jessica, 64, 273, 297 Fisher, Mark Bradley, xiv background of, 63–64 CFTC charges, 165–66, 223, 232, 240–41 CME-Nymex merger and, 270, 273–74, 342 at the Crobar party, 297 hazing of traders by, 1–6, 175–76, 294–95 Newsome and, 232, 239–41, 248 9/11 attacks (2001), 223 on Nymex traders, 18–19 private-equity proposals and, 270–74, 284, 286–87, 306 protégés of, 174–76 Rappaport and, 62–63, 166, 180–81 start at Nymex, 63–64, 164–65 tenacity of, 63–64 “flash crash” of 2010, 373 flat market, 43 Ford, Bill, 273, 306–7, 317–18, 341, 354 foreign currency futures trading, 56–57, 59 Forrestal, James, 60 Foster, Vincent, 195 Four Seasons, 219 Francis, Melissa, 261 Friedman, Milton, 318, 328 front-running, 146, 214 defined, 381 “Fuck ICE” guy, 264, 304, 375 Fulton, Linda, 282 fundamentals, 86 defined, 381 Future Farmers of America, 51–52, 230–31 futures contracts, 84–85, 109–10 defined, 381 Futures Industry Association, 90, 100, 224–25, 327, 328 futures market origins of, 45 overview of, 12–14 underlying utility of, 44 gambling, 147–48 Gary Williams Energy Company, 345 gasoline prices, 7–8, 98, 259, 282–83 Gateway Plaza, 148–49, 173, 198, 217, 307, 376–77 General Atlantic, 273, 284, 287, 290–91, 299–300, 302, 304, 306–7, 310, 317–18, 322–23, 341, 354–55 Gensler, Gary, 357, 364, 365 George Mason University, 222 George School, 26 Gerald Inc., 127, 151–52, 156–62, 186–87 Gero, Anthony George, 124, 160, 168 Giuliani, Rudolph, 324 Glass, Gary, xiv, 77, 286 background of, 82 CFTC case against, 156–62, 185–93 Guttman and, 81–83 Magid trades and, 152–53, 156–62, 185–93 private-equity proposals and, 284, 301 silver coin futures trading and, 83–85 update on, 371 global warming, 358 Globex, 126, 264, 308–9 glossary of terms, 380–83 Goerl, Conrad, 205–6 Goldfarb, Sanford, 236, 296, 297 Goldman Sachs, 228–29, 237, 360 author’s investigation of, 327–29 “Bona Fide Hedging” exemption, 183–85 financial crisis of 2008 and, 356–57 heating-oil futures and, 88 oil prices and, 335, 351–52 Rappaport and, 202–3, 204 Goldman Sachs Commodity Index, 328–29 defined, 381 Good, the Bad and the Ugly, The (movie), 167–68 Gore, Al, 218 Government Accountability Office, U.S., 219 Gramm, Florence, 221–22 Gramm, Wendy, xvi, 160–61, 193–94, 222, 232–33 Gramm, William Philip “Phil,” xvi, 160, 221–22 Grasso, Dick, 301n gravestone, the, 86 Gray, Linda, 102 Greenberg, David, xv–xvi, 306, 333 electronic trading and, 317, 371 metals market and, 165 9/11 attacks (2001), 214–16 at Quantico Marine Base, 178 Schaeffer and, 317, 328 update on, 371–72 Viola and, 211 Greenberg, Martin, xiii chairmanship of Comex, 61–62, 179 IPO and, 320 9/11 attacks (2001), 214–15 private-equity proposals and, 270, 289 at Quantico Marine Base, 178 Viola and, 211, 213 Greenberger, Michael, 349 greenmail, 98 defined, 98n, 381–82 Greenpeace, 268–69 Greenspan, Alan, 59, 194, 222, 233 Gulf of Aden, 345–46 Guttman, Aranka, 77–78, 79–80, 112–13, 158, 207 Guttman, Connie, 242 Guttman, Herman, 78–80, 112–13, 117, 158 Guttman, Magda, 77 Guttman, Zoltan Louis “Lou,” xiii, 77–82, 117–39 author’s interview with, 155 background of, 77–80 Beneficial Labs affair, 130–32 bonuses of, 142–43, 169–70 Bradt-Tafaro affair and, 117–23 Bürgenstock incident, 123–24 CFTC appeal by, 192, 196–97 CFTC case against, 152–53, 156–62, 167–69, 185–93, 241 CFTC fine of, 191, 197, 206–7 chairmanship of, 123–26, 130–35, 139–43, 148, 156, 161, 166–70, 173 CME deal and, 124–26, 133–34, 274, 308–9 Collins and, 241–42 FBI moles and, 136–39 at Gateway Plaza, 148–49, 173, 198, 217, 307, 376–77 Goldman and, 203 on hedge funds, 144–45 invisibility of, 154–55 IPO and, 321 as kingmaker, 207, 208, 210, 211 McFadden and, 123–24, 126–27, 129–30, 132–35 Magid-Glass trades and, 149–53, 156–62, 185–93 on the Marks, 106–7, 110, 111–13 marriage of, 242 natural-gas trading and, 126–27, 141–42 Newsome and, 241–42, 253–54 Nymex seats of, 80–81, 206–7, 296 obstruction of justice charges, 139 platinum trading, 81, 86–87 polygraph test of, 159–60, 187 private-equity proposals and, 270, 287–88, 298, 302, 305 salary of, 140 Schaeffer and, 257, 307, 338–39, 354–55, 367–68 start at Nymex, 79, 80–82 update on, 376–77 vice chairmanship of, 113, 117–19 Viola and, 134, 208, 210, 211, 212, 307 Waldorf-Astoria speech, 124 on Wall Street and Nymex, 103, 105 wilding at Nymex, 148–49, 285 World Trade Center attacks (1993), 162–64 Hakes, Jay, 220 Halper, Robert “Bobby,” 149, 306–7, 340–41 hanging man, the, 86 Harbour Drive, 366–67, 377 Hardly Able Oil Company, 137 Hard Rock Hotel (Las Vegas), 278–79 Harkin, Tom, 275 Harley Futures Inc., 127, 153, 171 Harvard Business School, 14 Harvard University, xvi, 14, 261 Having It All (Brown), 59 Hazelcorn, Howard, 70, 109, 113, 169 Hearst, Patricia, 159 heating-oil futures trading, 68–76 hedge funds, 144–45, 277, 324, 331–32 defined, 382 hedging (hedges), 47, 144–45, 183–85, 219 defined, 382 Heinhold Commodities, 49 Hellman & Friedman, 274 Helmig, Albert, 340 Hemingway, Ernest, 144 Henry Hub (Erath, Louisiana), 127, 141, 245 Hickson, Lenel, 186–89 Hills Department Stores, 98 Hogan, Frank, 159 Horsnell, Paul, 336 Hotel Plaza Athénée, 97, 341 Houston Texans, 278 Hughes, Marianne, 138, 163 Hunt, Haroldson L., 128–29 Hunt, Nelson B., 128–29 Hunter, Brian, xvii background of, 314 CFTC and, 314–17, 331, 347–48, 360 energy trading debacle of, 314–17, 329–32 Hunter, Carrie, 314 Hurricane Ivan, 235 Hurricane Katrina, 235, 292 Hurricane Rita, 292 Hussein, Saddam, 137, 234, 334 Icahn, Carl, 82 ICE (IntercontinentalExchange), 202 CFTC and, 227, 263, 309, 316–17 Cohn and, 204 Greenpeace protest, 268–69 International Petroleum Exchange and, 261–65, 268–70 IPO of, 292 Middle East oil futures contract on, 337 Nymex competition with, 236–37, 247, 262–63, 292, 302–4, 308, 309 Rappaport and, 202–3, 204 Viola and, 236–37, 254 Idaho potatoes, 40–41, 46–53 illegal trading, 152, 240, 325, 346–47 Magid-Glass trades, 185–93, 196 Insatiable, The, 367 Institutional Investor, 16 Interior Department, U.S., 345 Internal Revenue Service (IRS), 128 International Petroleum Exchange, 118–19, 201–2, 262–65, 268–70 Intrepid, USS, 177, 313 inverted hammer, the, 86 IPOs (initial public offerings), 299, 319–21 defined, 382 Iran, 4, 87 Iran-Iraq War, 87–88 Iraq, Gulf War, 99, 143, 233–34 Iraq War, 26, 233–36, 259, 274–75, 327, 359, 362–63 illegal deals, 137 oil prices, 2, 234–35, 259–61 Jarecki, Henry, 56–57, 140, 180, 293 Johnson, Philip, 90–91 Jones, Paul Tudor, 165, 378 J.P.
The Unwinding: An Inner History of the New America by George Packer
Affordable Care Act / Obamacare, Apple's 1984 Super Bowl advert, bank run, big-box store, citizen journalism, cleantech, collateralized debt obligation, collective bargaining, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, deindustrialization, diversified portfolio, East Village, El Camino Real, Elon Musk, family office, financial independence, financial innovation, fixed income, Flash crash, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, housing crisis, income inequality, informal economy, Jane Jacobs, life extension, Long Term Capital Management, low skilled workers, Marc Andreessen, margin call, Mark Zuckerberg, market bubble, market fundamentalism, Maui Hawaii, Menlo Park, Neil Kinnock, new economy, New Journalism, obamacare, Occupy movement, oil shock, paypal mafia, peak oil, Peter Thiel, Ponzi scheme, Richard Florida, Robert Bork, Ronald Reagan, Ronald Reagan: Tear down this wall, shareholder value, side project, Silicon Valley, Silicon Valley startup, single-payer health, smart grid, Steve Jobs, strikebreaker, The Death and Life of Great American Cities, the scientific method, too big to fail, union organizing, urban planning, We are the 99%, We wanted flying cars, instead we got 140 characters, white flight, white picket fence, zero-sum game
But as Wall Street aggressively fought any but the smallest changes, inertia set in at the SEC, and, once again, nothing happened. May 6, 2010, was the day when Connaughton’s second life in government began to end. In the early afternoon, the stock market suddenly plummeted seven hundred points in eight minutes before reversing itself, with the momentary disappearance of almost a trillion dollars in wealth. The flash crash, as it came to be called, was caused by the kind of automated trading that Kaufman had warned about. A few hours later, Kaufman was sitting in the presiding officer’s chair when Mark Warner, the Virginia Democrat, explained to the Senate what had just happened. “I have become a believer,” he said, and invited Kaufman to come down to the floor and essentially say to the world, “I told you so”—which Kaufman did.
While we disagree with one another on substantive issues, we can enjoy each other’s company on a personal level, a civil level.” And Senator Dodd wished Senator Shelby a very happy birthday. Later that night, Kaufman returned to his office in Russell. Connaughton asked him what he should put into a press release. Kaufman could muster only three words: “I am disappointed.” They had known it was doomed, but the size of their defeat was devastating. In the span of a few hours, they had been vindicated by the flash crash, then thoroughly whipped on too big to fail. The southerner in Connaughton, the romantic believer in lost causes, told the staff, “Some things are worth fighting for.” * * * On May 21, the Dodd bill passed the Senate, and on July 21, President Obama signed the Dodd-Frank Wall Street Reform and Consumer Protection Act into law. The Volcker Rule was now a ghost of itself, with the details left to regulators.
Geek Sublime: The Beauty of Code, the Code of Beauty by Vikram Chandra
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Apple II, barriers to entry, Berlin Wall, British Empire, business process, conceptual framework, create, read, update, delete, crowdsourcing, don't repeat yourself, Donald Knuth, East Village, European colonialism, finite state, Firefox, Flash crash, glass ceiling, Grace Hopper, haute couture, iterative process, Jaron Lanier, John von Neumann, land reform, London Whale, Norman Mailer, Paul Graham, pink-collar, revision control, Silicon Valley, Silicon Valley ideology, Skype, Steve Jobs, Steve Wozniak, supercomputer in your pocket, theory of mind, Therac-25, Turing machine, wikimedia commons, women in the workforce
Every programmer is familiar with the most infamous bugs: the French Ariane 5 rocket that went off course and self-destructed forty seconds after lift-off because of an error in converting between representations of number values; the Therac-25 radiation therapy machine that reacted to a combination of operator input and a “counter overflow” by delivering doses of radiation a hundred times more intense than required, resulting in the agonizing deaths of five people and injuries to many others; the “Flash Crash” of 2010, when the Dow Jones suddenly plunged a thousand points and recovered just as suddenly, apparently as a result of automatic trading programs reacting to a single large trade. These are the notorious bugs, but there are bugs in every piece of software that you use today. A professional “cyber warrior,” whose job it is to find and exploit bugs for the US government, recently estimated that “most of the software written in the world has a bug every three to five lines of code.”18 These bugs may not kill you, but they cause your system to freeze, they corrupt your data, and they expose your computers to hackers.
Swimming With Sharks: My Journey into the World of the Bankers by Joris Luyendijk
activist fund / activist shareholder / activist investor, bank run, barriers to entry, Bonfire of the Vanities, bonus culture, collapse of Lehman Brothers, collective bargaining, corporate raider, credit crunch, Credit Default Swap, Emanuel Derman, financial deregulation, financial independence, Flash crash, glass ceiling, Gordon Gekko, high net worth, hiring and firing, information asymmetry, inventory management, job-hopping, light touch regulation, London Whale, Nick Leeson, offshore financial centre, regulatory arbitrage, Satyajit Das, selection bias, shareholder value, sovereign wealth fund, the payments system, too big to fail
As a last resort people actually rip the cable out of the computer if things start to go wrong, the compliance officer went on. ‘I’ve seen that happen, but it seems ridiculously primitive in such technological environments.’ A number of back- and middle-office workers claimed to have witnessed high frequency trading computers being disconnected: after a tsunami, an unusually large terrorist attack or the sudden prospect of a Greek default. What if the plug cannot be pulled in time? In the ‘flash crash’ of 6 May 2012, share prices suddenly lost hundreds and hundred of points in a matter of minutes. Then, as quickly and inexplicably as they had crashed, prices recovered – while the world of finance looked on in terrified bewilderment. Complexity can render even insiders helpless, explained the ‘head of structured credit’ at a big bank. In his mid thirties, slightly restless but quick to laugh, he is one of the most good-natured people I have met in two years of researching.
The Only Game in Town: Central Banks, Instability, and Avoiding the Next Collapse by Mohamed A. El-Erian
activist fund / activist shareholder / activist investor, Airbnb, balance sheet recession, bank run, barriers to entry, break the buck, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, collapse of Lehman Brothers, corporate governance, currency peg, disruptive innovation, Erik Brynjolfsson, eurozone crisis, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, friendly fire, full employment, future of work, Hyman Minsky, If something cannot go on forever, it will stop - Herbert Stein's Law, income inequality, inflation targeting, Jeff Bezos, Kenneth Rogoff, Khan Academy, liquidity trap, Martin Wolf, megacity, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Norman Mailer, oil shale / tar sands, price stability, principal–agent problem, quantitative easing, risk tolerance, risk-adjusted returns, risk/return, Second Machine Age, secular stagnation, sharing economy, sovereign wealth fund, The Great Moderation, The Wisdom of Crowds, too big to fail, University of East Anglia, yield curve, zero-sum game
The advanced economies’ failure to grow nominal GDP has made life a lot more difficult for emerging countries—a phenomenon that accentuates not only their internal challenges but also the trials of navigating a global financial system that is inevitably distorted by the pursuit of unconventional policies and is periodically subject to discomforting bouts of financial instability. Examples include the “taper tantrum” of May–June 2013, the U.S. Treasury “flash crash” in October 2014, the Swiss currency shock of January 2015, the volatility in German bond rates in May–June 2015, and the surprise China currency move in August 2015—all of which highlighted the combined impact of disorderly unwinds by levered traders, little appetite for risk taking among broker-dealers and other intermediaries, and a tendency for some end investors to head quickly to the door at the sign of trouble.
Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl
Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, book scanning, borderless world, call centre, cellular automata, Claude Shannon: information theory, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, Ray Kurzweil, recommendation engine, remote working, RFID, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!
A more notable case of AI wreaking havoc took place on 6 May 2010 – an otherwise normal day – when close to $1 trillion of wealth vanished into the digital ether. At 2.42 p.m. on America’s East Coast, the Dow Jones Industrial Average fell by almost 1,000 points in the span of three minutes: by far the largest single-day drop in history. Some share prices fell from their usual trading positions of $30 to $40 down to $0.01, only to ricochet back up almost immediately. Apple careened from $250 to $100,000 per share. The ‘flash crash’ anomaly has fortunately never been repeated, but it was almost certainly the result of a simple rule-based AI becoming locked in a feedback loop. But the fact remains that artificial stupidity managed to ‘steal’ more money from its rightful owners than the biggest, most well-orchestrated human heists in history. The Perils of Black Boxes Whether you’re talking superintelligence or artificial stupidity, several things make it difficult to intervene in the case of a rogue AI.
The Behavioral Investor by Daniel Crosby
affirmative action, Asian financial crisis, asset allocation, availability heuristic, backtesting, bank run, Black Swan, buy and hold, cognitive dissonance, colonial rule, compound rate of return, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, endowment effect, feminist movement, Flash crash, haute cuisine, hedonic treadmill, housing crisis, IKEA effect, impulse control, index fund, Isaac Newton, job automation, longitudinal study, loss aversion, market bubble, market fundamentalism, mental accounting, meta analysis, meta-analysis, Milgram experiment, moral panic, Murray Gell-Mann, Nate Silver, neurotypical, passive investing, pattern recognition, Ponzi scheme, prediction markets, random walk, Richard Feynman, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, science of happiness, Shai Danziger, short selling, South Sea Bubble, Stanford prison experiment, Stephen Hawking, Steve Jobs, stocks for the long run, Thales of Miletus, The Signal and the Noise by Nate Silver, tulip mania, Vanguard fund
List of some notable manias, panics and crashes Tulip mania (Netherlands) – 1637 South Sea Bubble (UK) – 1720 Bengal Bubble (UK) – 1769 Credit Crisis of 1772 (UK) Financial Crisis of 1791 (US) Panic of 1796–7 (US) Panic of 1819 (US) Panic of 1825 (UK) Panic of 1837 (US) Panic of 1847 (UK) Panic of 1857 (US) Panic of 1866 (UK) Black Friday (US) – 1869 Paris Bourse crash of 1882 (France) “Encilhamento” (Brazil) – 1890 Panic of 1893 (US) Panic of 1896 (US) Panic of 1901 (US) Panic of 1907 (US) Great Depression (US) – 1929 Recession of 1937–8 (US) Brazilian Market Crash of 1971 British Market Crash of 1973–4 Souk Al-Manakh Crash (Kuwait) – 1982 Black Monday (US) – 1987 Rio de Janeiro Stock Exchange Crash – 1989 Japanese Asset Price Bubble – 1991 Black Wednesday (UK) – 1992 Asian Financial Crisis – 1997 Russian Financial Crisis – 1998 dot.com Bubble (US) – 2000 Chinese Stock Bubble – 2007 Great Recession of 2007–9 (US) European Sovereign Debt Crisis (2010) Flash Crash of 2010 (US) Notes 114 L. Swedroe, ‘Why alpha’s getting more elusive,’ ETF.com (November 21, 2014). 115 T. Howard, Behavioral Portfolio Management (Harriman House, 2014). 116 W. Buffett, ‘The Superinvestors of Graham-and-Doddsville,’ Columbia Business School (May 17, 1984). Chapter 16. Sample Behavioral Investment Factors This final chapter will apply previous lessons around testing investing methods to two of the most discussed ideas in asset management – value and momentum.
The Long Good Buy: Analysing Cycles in Markets by Peter Oppenheimer
"Robert Solow", asset allocation, banking crisis, banks create money, barriers to entry, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, business cycle, buy and hold, Cass Sunstein, central bank independence, collective bargaining, computer age, credit crunch, debt deflation, decarbonisation, diversification, dividend-yielding stocks, equity premium, Fall of the Berlin Wall, financial innovation, fixed income, Flash crash, forward guidance, Francis Fukuyama: the end of history, George Akerlof, housing crisis, index fund, invention of the printing press, Isaac Newton, James Watt: steam engine, joint-stock company, Joseph Schumpeter, Kickstarter, liberal capitalism, light touch regulation, liquidity trap, Live Aid, market bubble, Mikhail Gorbachev, mortgage debt, negative equity, Network effects, new economy, Nikolai Kondratiev, Nixon shock, oil shock, open economy, price stability, private sector deleveraging, Productivity paradox, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, Simon Kuznets, South Sea Bubble, special economic zone, stocks for the long run, technology bubble, The Great Moderation, too big to fail, total factor productivity, trade route, tulip mania, yield curve
1961–1962 ‘Kennedy Slide’: Rising rates from 1959 Cold War tension No - 1966 Inflation following Johnson Great Society programme; Fed raised rates by approximately 1.5% in 1 year No - 1968–1970 Vietnam war and inflation; Fed raised rates to 9% from 4% 2 years before; between the start of 1968 and mid-1968 rates rose by 3% Yes Dec 1969 – Nov 1970 1973–1974 The crash after the collapse of the Bretton Woods system over the previous 2 years, with the associated ‘Nixon Shock’ and USD devaluation under the Smithsonian Agreement 1973 Oil Crisis: Price of oil rose from $3 per barrel to nearly $12 Yes Nov 1973 – Mar 1975 1980–1982 ‘Volcker crash’; the 1979 second oil crisis was followed by strong inflation; the Fed raised its rates from 9% to 19% in six months Yes Jan 1980 – July 1980 Jul 1981 – Nov 1982 1987 Black Monday: Flash Crash: computerised ‘programme trading’ strategies swamped the market; tensions between the US and Germany over currency valuations No - 1990 Gulf War: Iraq invasion of Kuwait; oil prices doubled Yes July 1990 – Mar 1991 2000–2002 Dotcom bubble; technology companies bankruptcy; Enron scandal; 09/11 attacks Yes Mar 2001 – Nov 2001 2007–2009 Housing bubble; sub-prime loan & CDS collapse; US housing market collapse Yes Dec 2007 – Jun 2009 Extending this analysis shows that, on the standard definition (of declines of 20% or more), there have been 27 bear markets in the S&P 500 since 1835 and 10 in the post-war period.
Radicalized by Cory Doctorow
activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Bernie Sanders, call centre, crowdsourcing, cryptocurrency, Edward Snowden, Flash crash, G4S, high net worth, information asymmetry, license plate recognition, obamacare, old-boy network, six sigma, TaskRabbit
The firm he’d picked had a long waiting list, but when he described the size of the job he was planning, they brought in trusted subs they’d worked with on other big projects and started right away. Now it was done, he was more like 75–25 on The Event, and of course, it was possible that he was feeling that way because Fort Doom was so fucking cool, it would be awesome to hole up there and wait out the chaotic months or years until stability was restored. * He called it too early. When the markets opened on January second, there was a flash-crash, seemingly precipitated by wildfires in the Canadian prairies, which were supposed to be under too much ice for anything to burn at that time of year. A bone-dry autumn, a failed harvest, then an unseasonably hot winter that screwed up things in the big oil sands pipeline, touching off a spill in a wooded area that turned into a forest fire that turned into a wildfire. It had been rumbling along all through the Christmas week, a constant background beat to the news reports: Here’s how fucked up things are in Saskatchewan; and now, a story about a soldier who made it home in time for Christmas.
The Glass Cage: Automation and Us by Nicholas Carr
Airbnb, Airbus A320, Andy Kessler, Atul Gawande, autonomous vehicles, Bernard Ziegler, business process, call centre, Captain Sullenberger Hudson, Charles Lindbergh, Checklist Manifesto, cloud computing, computerized trading, David Brooks, deliberate practice, deskilling, digital map, Douglas Engelbart, drone strike, Elon Musk, Erik Brynjolfsson, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, global supply chain, Google Glasses, Google Hangouts, High speed trading, indoor plumbing, industrial robot, Internet of things, Jacquard loom, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, knowledge worker, Lyft, Marc Andreessen, Mark Zuckerberg, means of production, natural language processing, new economy, Nicholas Carr, Norbert Wiener, Oculus Rift, pattern recognition, Peter Thiel, place-making, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, TaskRabbit, technoutopianism, The Wealth of Nations by Adam Smith, turn-by-turn navigation, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche
Miscalculations of risk, exacerbated by high-speed computerized trading programs, played a major role in the near meltdown of the world’s financial system in 2008. As Tufts University management professor Amar Bhidé has suggested, “robotic methods” of decision making led to a widespread “judgment deficit” among bankers and other Wall Street professionals.20 While it may be impossible to pin down the precise degree to which automation figured in the disaster, or in subsequent fiascos like the 2010 “flash crash” on U.S. exchanges, it seems prudent to take seriously any indication that a widely used technology may be diminishing the knowledge or clouding the judgment of people in sensitive jobs. In a 2013 paper, computer scientists Gordon Baxter and John Cartlidge warned that a reliance on automation is eroding the skills and knowledge of financial professionals even as computer-trading systems make financial markets more risky.21 Some software writers worry that their profession’s push to ease the strain of thinking is taking a toll on their own skills.
The Fear Index by Robert Harris
algorithmic trading, backtesting, banking crisis, dark matter, family office, Fellow of the Royal Society, fixed income, Flash crash, God and Mammon, high net worth, implied volatility, mutually assured destruction, Neil Kinnock, Renaissance Technologies, speech recognition
and saw Hoffmann with fire coming out of his fingers. Not now, he thought, don’t do it yet – not till VIXAL has finished its trades. Beside him Gabrielle screamed, ‘Alex!’ Quarry flung himself towards the door. The fire left Hoffmann’s hand, seemed to dance in the air for an instant, and then expanded into a brilliant bursting star. THE SECOND AND decisive liquidity crisis of the seven-minute ‘flash crash’ had begun just as Hoffmann dropped the empty jerry can, at 8.45 p.m. Geneva time. All over the world investors were watching their screens and either ceasing to trade or selling up altogether. In the words of the official report: ‘Because prices simultaneously fell across many types of securities, they feared the occurrence of a cataclysmic event of which they were not aware, and which their systems were not designed to handle … A significant number withdrew completely from the markets.’
What Algorithms Want: Imagination in the Age of Computing by Ed Finn
Airbnb, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, bitcoin, blockchain, Chuck Templeton: OpenTable:, Claude Shannon: information theory, commoditize, Credit Default Swap, crowdsourcing, cryptocurrency, disruptive innovation, Donald Knuth, Douglas Engelbart, Douglas Engelbart, Elon Musk, factory automation, fiat currency, Filter Bubble, Flash crash, game design, Google Glasses, Google X / Alphabet X, High speed trading, hiring and firing, invisible hand, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, Just-in-time delivery, Kickstarter, late fees, lifelogging, Loebner Prize, Lyft, Mother of all demos, Nate Silver, natural language processing, Netflix Prize, new economy, Nicholas Carr, Norbert Wiener, PageRank, peer-to-peer, Peter Thiel, Ray Kurzweil, recommendation engine, Republic of Letters, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, social graph, software studies, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, supply-chain management, TaskRabbit, technological singularity, technoutopianism, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, Turing machine, Turing test, Uber and Lyft, Uber for X, uber lyft, urban planning, Vannevar Bush, Vernor Vinge, wage slave
Drawing on the historical figure of the automaton, a remarkable collection of Mechanical Turk-powered poetry titled Of the Subcontract, and Adam Smith’s conception of empathy in his Theory of Moral Sentiments, I explore the consequences of computational capitalism on politics, empathy, and social value. The root of the algorithmic sea change is the reimagination of value in computational terms. Chapter 5 leads with the flash crash in 2010 and the growing dominance of algorithmic trading in international markets (described by journalist Michael Lewis’s Flash Boys, among others) to frame a reading of Bitcoin and related cryptocurrencies. By defining the unit of exchange through computational cycles, Bitcoin fundamentally shifts the faith-based community of currency from a materialist to an algorithmic value system. Algorithmic arbitrage is forcing similar transitions in the attribution of value and meaning in many spaces of cultural exchange, from Facebook to journalism.
Learn Algorithmic Trading by Sebastien Donadio
active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, buy and hold, buy low sell high, cryptocurrency, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, p-value, paper trading, performance metric, prediction markets, quantitative trading / quantitative ﬁnance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game
The final level of risk violation is what would be considered a maximum possible risk violation, which is a violation that should never, ever happen. If a trading strategy ever triggers this risk violation, it is a sign that something went very wrong. This risk violation means that the strategy is no longer allowed to send any more order flow to the live markets. This risk violation would only be triggered during periods of extremely unexpected events, such as a flash crash market condition. This severity of risk violation basically means that the algorithmic trading strategy is not designed to deal with such an event automatically and must freeze trading and then resort to external operators to manage open positions and live orders. Differentiating the measures of risk Let's explore different measures of risk. We will use the trading performance from the volatility adjusted mean reversion strategy we saw in Chapter 5, Sophisticated Algorithmic Strategies, as an example of a trading strategy in which we wish to understand the risks behind and quantify and calibrate them.
Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen
Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stocks for the long run, survivorship bias, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game
But again, a careful study of expected return might have easily, perhaps even more easily, led to the same protection by avoiding certain investments on the ground that their expected returns were too low. Frankly, I think a lot of the answer is that discussing risk is inherently sexier than discussing expected returns. Good forecasts of expected return add up over the long term, but don’t matter for squat next week. Risk can kill you, or make you a hero, in the time it takes you to say “flash crash”. Would you rather write about a tsunami or erosion? More specifically, from a safe perch, say the hindsight of authoring a book, which event would be more dramatic to narrate? The analogy, forced though it may be, continues to work as erosion, or expected return, might be mind-numbingly boring at any one moment, but it has a heck of a lot to say about how the future will be shaped. For perspective, you want to see a black swan?
For example, the New York Stock Exchange has lost market share in U.S. equity turnover to various electronic platforms and dark pools. Market-makers used to be the only explicit liquidity providers but they have increasingly been superseded by hedge funds and other stat arb traders. High-frequency traders are important suppliers of short-term liquidity, but they are accused of being “fair-weather liquidity providers” who leave the market when problems arise (witness the Flash Crash in May 2010). Figure 18.7 plots the histories of an illiquidity proxy and the gross annual profits of a liquidity provision strategy from a study by Rinne–Suominen (2010). (The illiquidity proxy is based on the idea that frequent return reversals are indicative of an asset’s illiquidity as buying or selling demand causes a larger temporary price concession for less liquid assets. Estimates of return reversal tendencies for single stocks are then aggregated to get a proxy of market-wide illiquidity.)
The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum by Camila Russo
4chan, Airbnb, algorithmic trading, altcoin, always be closing, Any sufficiently advanced technology is indistinguishable from magic, Asian financial crisis, bitcoin, blockchain, Burning Man, crowdsourcing, cryptocurrency, distributed ledger, diversification, Donald Trump, East Village, Ethereum, ethereum blockchain, Flash crash, Google Glasses, Google Hangouts, hacker house, Internet of things, Mark Zuckerberg, Maui Hawaii, mobile money, new economy, peer-to-peer, Peter Thiel, pets.com, Ponzi scheme, prediction markets, QR code, reserve currency, RFC: Request For Comment, Richard Stallman, Robert Shiller, Robert Shiller, Sand Hill Road, Satoshi Nakamoto, semantic web, sharing economy, side project, Silicon Valley, Skype, slashdot, smart contracts, South of Market, San Francisco, the payments system, too big to fail, tulip mania, Turing complete, Uber for X
The two-year-old coin had become almost as valuable as the grandfather of crypto. But ether had recently breached $400 for the first time and was struggling to advance much further. Then, five days later, on June 22, a multimillion-dollar sell ordered triggered so-called stop-loss orders, or instructions to automatically sell when the price falls below a certain point, and that cascaded into even more stop losses. The domino effect caused a flash crash that pulled ether from $320 to 10 cents in seconds. The price recovered just as quickly but started plunging again two days later as the market remained jittery and rumors surfaced online that Vitalik had been involved in a deadly accident. “Vitalik Buterin confirmed dead. Insiders unloading ETH. Fatal car crash,” someone posted on 4chan, an anonymous online forum known for enabling harassment and pranks.
Pound Foolish: Exposing the Dark Side of the Personal Finance Industry by Helaine Olen
American ideology, asset allocation, Bernie Madoff, buy and hold, Cass Sunstein, Credit Default Swap, David Brooks, delayed gratification, diversification, diversified portfolio, Donald Trump, Elliott wave, en.wikipedia.org, estate planning, financial innovation, Flash crash, game design, greed is good, high net worth, impulse control, income inequality, index fund, London Whale, longitudinal study, Mark Zuckerberg, money market fund, mortgage debt, oil shock, payday loans, pension reform, Ponzi scheme, post-work, quantitative easing, Ralph Nader, RAND corporation, random walk, Richard Thaler, Ronald Reagan, Saturday Night Live, Stanford marshmallow experiment, stocks for the long run, too big to fail, transaction costs, Unsafe at Any Speed, upwardly mobile, Vanguard fund, wage slave, women in the workforce, working poor, éminence grise
Schiff is still something of a joke to many, but others, like Roubini, who predicted a major bank collapse just weeks before Bear Stearns went under, and Swiss-born Hong Kong–residing investment manager Marc Faber who, demonstrating his sly sense of humor, named his investment newsletter the Gloom Boom & Doom Report, are well-respected by most of the establishment. Needless to say, bad times are almost always good times for anyone predicting economic Armageddon. Behavioral finance experts explain this by claiming what they call the recency effect; that is, our natural human bias to overemphasize the recent past over other experiences (in other words, goodbye Internet bubble, hello Flash Crash!). I suspect something else, however. There can be a strange comfort in economic Armageddon. Gurus with their doomsday scenarios bring an odd sort of order to what otherwise could seem like a random series of ghastly events. According to them, all this bad stuff is happening for a reason. And if you understand the reason, their sales pitch goes, you can be protected from the disaster to come.
The Clash of the Cultures by John C. Bogle
asset allocation, buy and hold, collateralized debt obligation, commoditize, corporate governance, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, estate planning, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, Flash crash, Hyman Minsky, income inequality, index fund, interest rate swap, invention of the wheel, market bubble, market clearing, money market fund, mortgage debt, new economy, Occupy movement, passive investing, Paul Samuelson, Ponzi scheme, post-work, principal–agent problem, profit motive, random walk, rent-seeking, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, statistical arbitrage, survivorship bias, The Wealth of Nations by Adam Smith, transaction costs, Vanguard fund, William of Occam, zero-sum game
In theory, this gives investors all the tools they need to design the portfolio which closely matches their risk preferences and economic outlook.” Not What It Says on the Tin “One risk relates to liquidity. In some sectors, like emerging markets, it is easier for investors to buy and sell an ETF than to trade in the underlying illiquid assets. But the liquidity risk has not gone away. During the market turmoil known as the “flash crash” in May 2010, the Dow Jones Industrial Average briefly dropped 1,000 points as liquidity evaporated: 60–70 percent of the trades that subsequently had to be canceled were in ETFs. “Another risk concerns a change in the nature of ETFs—the development of “synthetic ETFs” and related products such as exchange-traded notes (ETNs). These funds do not own assets like shares or bonds; instead they arrange a derivative deal with an investment bank, which guarantees to deliver the return of the targeted benchmark, exposing the ETF investor to the risk that the bank fails to pay up. . . .
Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford
"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce
Likewise, automated trading algorithms are now responsible for nearly two-thirds of stock market trades, and Wall Street firms have built huge computing centers in close physical proximity to exchanges in order to gain trading advantages measured in tiny fractions of a second. Between 2005 and 2012, the average time to execute a trade dropped from about 10 seconds to just 0.0008 seconds,56 and robotic, high-speed trading was heavily implicated in the May 2010 “flash crash” in which the Dow Jones Industrial Average plunged nearly a thousand points and then recovered for a net gain, all within the space of just a few minutes. Viewed from this perspective, financialization is not so much a competing explanation for our seven economic trends; it is rather—at least to some extent—one of the ramifications of accelerating information technology. In this, there is a strong cautionary note as we look to the future: as IT continues its relentless progress, we can be certain that financial innovators, in the absence of regulations that constrain them, will find ways to leverage all those new capabilities—and, if history is any guide, it won’t necessarily be in ways that benefit society as a whole.
Augmented: Life in the Smart Lane by Brett King
23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks
Between 2009 and 2013, machine intelligent HFT algorithms accounted for between 49 and 73 per cent of all US equity trading volume, and 38 per cent in the European Union in 2014. On 6th May 2010, the Dow Jones plunged to its largest intraday point loss, only to recover that loss within minutes. After a five-month investigation, the US Securities and Exchange Commission (SEC) and the Commodities Future Trading Commission (CFTC) issued a joint report that concluded that HFT had contributed significantly to the volatility of the so-called “flash” crash. A large futures exchange, CME Group, said in its own investigation that HFT algorithms probably stabilised the market and reduced the impact of the crash. For an industry that has developed trading into a fine art over the last 100 years, HFT algorithms represent a significant departure from the trading rooms of Goldman Sachs, UBS and Credit Suisse. The algorithms themselves have departed significantly from typical human behaviour.
Team of Teams: New Rules of Engagement for a Complex World by General Stanley McChrystal, Tantum Collins, David Silverman, Chris Fussell
Airbus A320, Albert Einstein, Atul Gawande, autonomous vehicles, bank run, barriers to entry, Black Swan, butterfly effect, call centre, Captain Sullenberger Hudson, Chelsea Manning, clockwork universe, crew resource management, crowdsourcing, Edward Snowden, Flash crash, Frederick Winslow Taylor, global supply chain, Henri Poincaré, high batting average, interchangeable parts, invisible hand, Isaac Newton, Jane Jacobs, job automation, job satisfaction, John Nash: game theory, knowledge economy, Mark Zuckerberg, Mohammed Bouazizi, Nate Silver, Pierre-Simon Laplace, RAND corporation, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, supply-chain management, The Wealth of Nations by Adam Smith, urban sprawl, US Airways Flight 1549, WikiLeaks, zero-sum game
When hackers infiltrated the Associated Press’s Twitter account in 2013 and sent out a message claiming the White House had been hit by two explosions and President Obama was injured, the Dow Jones fell 143 points in a brief but widespread market panic. The tweet was deleted as soon as it appeared, but its momentary presence was enough to trigger both impulsive human behavior and the high-frequency trading algorithms now used throughout the markets, which “read” the news and perform trades in response in mere nanoseconds. One trader saw the Associated Press–induced flash crash as “a comment on how vulnerable the markets are to random pieces of information.” A more lighthearted example: When musician Dave Carroll’s guitar was broken by United Airlines baggage handlers, he spent nine months navigating the company’s telephone-directory maze of customer service representatives to no avail, so he wrote a song called “United Breaks Guitars” and posted the video on YouTube.
Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell
3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game
The question is whether the computer system remains a tool of humans, or humans become tools of the computer system—supplying information and fixing bugs when necessary, but no longer understanding in any depth how the whole thing is working. The answer becomes clear when the system goes down and global chaos ensues until it can be brought back online. For example, a single “computer glitch” on April 3, 2018, caused fifteen thousand flights in Europe to be significantly delayed or canceled.40 When trading algorithms caused the 2010 “flash crash” on the New York Stock Exchange, wiping out $1 trillion in a few minutes, the only solution was to shut down the exchange. What happened is still not well understood. Before there was any technology, human beings lived, like most animals, hand to mouth. We stood directly on the ground, so to speak. Technology gradually raised us up on a pyramid of machinery, increasing our footprint as individuals and as a species.
Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All by Robert Elliott Smith
Ada Lovelace, affirmative action, AI winter, Alfred Russel Wallace, Amazon Mechanical Turk, animal electricity, autonomous vehicles, Black Swan, British Empire, cellular automata, citizen journalism, Claude Shannon: information theory, combinatorial explosion, corporate personhood, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, desegregation, discovery of DNA, Douglas Hofstadter, Elon Musk, Fellow of the Royal Society, feminist movement, Filter Bubble, Flash crash, Gerolamo Cardano, gig economy, Gödel, Escher, Bach, invention of the wheel, invisible hand, Jacquard loom, Jacques de Vaucanson, John Harrison: Longitude, John von Neumann, Kenneth Arrow, low skilled workers, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, mutually assured destruction, natural language processing, new economy, On the Economy of Machinery and Manufactures, p-value, pattern recognition, Paul Samuelson, performance metric, Pierre-Simon Laplace, precariat, profit maximization, profit motive, Silicon Valley, social intelligence, statistical model, Stephen Hawking, stochastic process, telemarketer, The Bell Curve by Richard Herrnstein and Charles Murray, The Future of Employment, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Malthus, traveling salesman, Turing machine, Turing test, twin studies, Vilfredo Pareto, Von Neumann architecture, women in the workforce
Engineer and economist Bill Phillips even built a hydraulic machine called MONIAC (the Monetary National Income Analogue Computer), complete with transparent water tanks and circular pipes, to demonstrate how money flowed around Samuelson’s system like fluids in a hydraulic mechanism. But the modern economy is light years away from this simplistic factory-based model peopled by capitalists, workers and consumers. Now algorithms are also active, independent players in the market, though often their workings and motivations are obscure. These days high-frequency traders are more algorithmic rather than human, trading billions in milliseconds and causing inexplicable ‘flash crashes’ that even their own systems engineers can’t explain because, as complexity scientist Neil Johnson has shown,2 these algorithms are now operating in a new all-machine ecology. Furthermore, they remain ‘unseen’ and unrepresented in even the most complex models of our economic systems and they are barely understood at the highest levels of business and government. And, yet, despite our woefully out-of-date economic model and bare grasp of how algorithms operate and interact with each other in our economic systems, we are now being told that robots and superintelligent AIs will soon displace not only the labour of human hands, but that of human minds in the knowledge and service economy as well.
Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else by Chrystia Freeland
activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, assortative mating, banking crisis, barriers to entry, Basel III, battle of ideas, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Boris Johnson, Branko Milanovic, Bretton Woods, BRICs, business climate, call centre, carried interest, Cass Sunstein, Clayton Christensen, collapse of Lehman Brothers, commoditize, conceptual framework, corporate governance, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Deng Xiaoping, disruptive innovation, don't be evil, double helix, energy security, estate planning, experimental subject, financial deregulation, financial innovation, Flash crash, Frank Gehry, Gini coefficient, global village, Goldman Sachs: Vampire Squid, Gordon Gekko, Guggenheim Bilbao, haute couture, high net worth, income inequality, invention of the steam engine, job automation, John Markoff, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, liberation theology, light touch regulation, linear programming, London Whale, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, NetJets, new economy, Occupy movement, open economy, Peter Thiel, place-making, plutocrats, Plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, postindustrial economy, Potemkin village, profit motive, purchasing power parity, race to the bottom, rent-seeking, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, self-driving car, short selling, Silicon Valley, Silicon Valley startup, Simon Kuznets, Solar eclipse in 1919, sovereign wealth fund, starchitect, stem cell, Steve Jobs, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tony Hsieh, too big to fail, trade route, trickle-down economics, Tyler Cowen: Great Stagnation, wage slave, Washington Consensus, winner-take-all economy, zero-sum game
Before the invention of the personal computer, the securitization of mortgages—which turned out to be part of the kindling for the financial crisis—would not have been possible. Nor would the algorithmic trading revolution, in which machines are replacing centuries-old stock exchanges and a couple of lines of corrupt code can trigger a multibillion-dollar loss of market value in moments, as occurred during the “flash crash” on May 6, 2010. — Revolution is the new global status quo, but not everyone is good at responding to it. My shorthand for the archetype best equipped to deal with it is “Harvard kids who went to provincial public schools.” They got into Harvard, or, increasingly, its West Coast rival, Stanford, so they are smart, focused, and reasonably privileged. But they went to public schools, often in the hinterlands, so they ha