Flash crash

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Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino by Jim McTague

Alan Greenspan, algorithmic trading, automated trading system, Bear Stearns, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, 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, junk bonds, locking in a profit, Long Term Capital Management, machine readable, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, Nixon triggered the end of the Bretton Woods system, pattern recognition, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, uptick rule, 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.

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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.

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., 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 Monday: stock market crash in 1987, Black Swan, Bob Geldof, centre right, collapse of Lehman Brothers, data science, Donald Trump, Elliott wave, eurozone crisis, family office, financial engineering, Flash crash, Great Grain Robbery, high net worth, High speed trading, information asymmetry, Jeff Bezos, Kickstarter, land bank, margin call, market design, market microstructure, Market Wizards by Jack D. Schwager, Navinder Sarao, Nick Leeson, offshore financial centre, pattern recognition, Ponzi scheme, proprietary trading, Ralph Nelson Elliott, Reminiscences of a Stock Operator, Ronald Reagan, selling pickaxes during a gold rush, sovereign wealth fund, spectrum auction, Stephen Hawking, the market place, Timothy McVeigh, Tobin tax, tulip mania, yield curve, zero-sum game

The DOJ’s indictment stated Sarao’s trading “contributed to the order-book imbalance that the CFTC and the SEC have concluded, in a published report, was a cause, among other factors, of the Flash Crash,” a formulation that was hard to refute. 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.

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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.

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Leuchtkafer’s comments, from April 16, 2010, are published on the SEC’s website, www.sec.gov. noticed that some exchanges were intermittently being bombarded: “Analysis of the ‘Flash Crash’…Part 4, Quote Stuffing,” June 18, 2010, www.nanex.net. Intriguingly, one occurred as the e-mini collapsed: “Nanex Flash Crash Summary Report,” September 27, 2010, www.nanex.net. In July 2010, Hunsader was invited to Washington: Herbert Lash, “ ‘Flash Crash’ Report Ignores Research: Nanex,” Reuters, October 4, 2010. “Why would SEC regulators deny”: “Quote Stuffing Bombshell,” December 14, 2012, www.nanex.net. no multimarket “Splash Crash”: The term “splash crash” was coined by John Bates, a senior director at Progress Software and an adviser to the CFTC, to describe a situation whereby a Flash Crash–type event spills over into markets and asset classes around the world with potentially catastrophic effects.

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 engineering, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, machine readable, Mark Zuckerberg, market fragmentation, National best bid and offer, payment for order flow, Ponzi scheme, price discovery process, price mechanism, price stability, proprietary trading, 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, uptick rule, zero-sum game

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.

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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.

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It is simply a truism that whenever there is a lot of money surging into a risky area, where change in the market is dramatic, where there is no transparency and therefore no effective regulation, we have a prescription for disaster. We had a disaster in the fall of 2008, when the credit markets suddenly dried up and our markets collapsed. The Flash Crash was a near-disaster. The SEC continues to study the causes for the Flash Crash. I hope the agency has moved much closer to truly understanding the dramatic changes in market structure that have taken place in the past few years, the potential ramifications of HFT, and its impact on retail and institutional investors. But this is about more than investor confidence.

High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Bear Stearns, 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, Large Hadron Collider, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, National best bid and offer, natural language processing, offshore financial centre, pattern recognition, power law, price discovery process, price discrimination, price stability, proprietary trading, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

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.

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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.

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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.

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, 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, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, 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.

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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.

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By deliberately manipulating the price, Sarao could buy low and sell high, making a profit as the price moved. It would be overly simplistic to pin the blame for the Flash Crash on Sarao. He continued his alleged market manipulation for five years after the Flash Crash until finally arrested in 2015 and his spoofing algorithm was reportedly turned off during the sharpest downturn in the Flash Crash. His spoofing could have exacerbated instability in the E-mini market that day, however, contributing to the crash. AFTERMATH In the aftermath of the Flash Crash, regulators installed “circuit breakers” to limit future damage. Circuit breakers, which were first introduced after the 1987 Black Monday crash, halt trading if stock prices drop too quickly.

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, behavioural economics, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, data science, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, Flash crash, G4S, Higgs boson, 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 free rate, 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

This is particularly critical in classification problems where the most important classes have rare occurrences (King and Zeng [2001]). 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.

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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.

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Figure 22.6 Gradient tree boosting (GBT) appears to follow recent usage too closely and therefore not able to predict the baseline usage as well as the newly develop method named LTAP. (a) GTB on Control group. (b) LTAP on Control group. (c) GTB on Passive group. (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.

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, compensation consultant, computerized markets, computerized trading, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, payment for order flow, prediction markets, price discovery process, proprietary trading, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day

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.

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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 data didn’t exist.” It may take a while to unpack these three conclusions that Lewis links together.

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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.”

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, Alan Greenspan, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bear Stearns, Bernie Madoff, Black-Scholes formula, Bob Litterman, 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, currency risk, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, Glass-Steagall Act, global macro, 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 interest rates, low skilled workers, managed futures, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, National best bid and offer, negative equity, Northern Rock, Occupy movement, oil shock, price stability, proprietary trading, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Sam Peltzman, Savings and loan crisis, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, tail risk, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

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.

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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.

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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, Dutch auction, Fall of the Berlin Wall, financial intermediation, Flash crash, High speed trading, information security, latency arbitrage, National best bid and offer, pattern recognition, payment for order flow, Pershing Square Capital Management, proprietary trading, 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.

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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.

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It was a little unsettling that the geeks who now ran the financial markets were also expected to have the nerves of a test pilot. But by the time Don approached Zoran, it had grown clear that the 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 by 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.

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson

Alan Greenspan, algorithmic trading, automated trading system, banking crisis, bash_history, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, financial engineering, fixed income, Flash crash, Ford Model T, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, information security, Jim Simons, Joseph Schumpeter, junk bonds, latency arbitrage, Long Term Capital Management, machine readable, Mark Zuckerberg, market design, market microstructure, Michael Milken, military-industrial complex, pattern recognition, payment for order flow, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, seminal paper, 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, three-martini lunch, Tragedy of the Commons, transaction costs, uptick rule, Watson beat the top human players on Jeopardy!, zero-sum game

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 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.

…

., 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.

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The founder of Tradebot wasn’t sure Along with a team of journalists, I reported extensively on the Flash Crash for The Wall Street Journal. See “Did Shutdowns Make Plunge Worse?” by Scott Patterson, The Wall Street Journal, May 7, 2010 (in which I reported that Tradebot and other HFT firms had pulled out of the market on May 6); and “Computer Trading Is Eyed,” by Tom Lauricella, Scott Patterson, and Carolyn Cui, The Wall Street Journal, May 8, 2010. 3. At Nasdaq, 12,306 trades were canceled Several details about the Flash Crash are derived from Preliminary 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, May 18, 2010.

The Payoff by Jeff Connaughton

Alan Greenspan, algorithmic trading, bank run, banking crisis, Bear Stearns, 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, Glass-Steagall Act, locking in a profit, London Interbank Offered Rate, London Whale, Long Term Capital Management, naked short selling, Neil Kinnock, plutocrats, Ponzi scheme, proprietary trading, risk tolerance, Robert Bork, Savings and loan crisis, short selling, Silicon Valley, TED Talk, too big to fail, two-sided market, uptick rule, young professional

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.

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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.

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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.

Kings of Crypto: One Startup's Quest to Take Cryptocurrency Out of Silicon Valley and Onto Wall Street by Jeff John Roberts

4chan, Airbnb, Alan Greenspan, altcoin, Apple II, Bernie Sanders, Bertram Gilfoyle, Big Tech, bitcoin, blockchain, Blythe Masters, Bonfire of the Vanities, Burning Man, buttonwood tree, cloud computing, coronavirus, COVID-19, creative destruction, Credit Default Swap, cryptocurrency, democratizing finance, Dogecoin, Donald Trump, double helix, driverless car, Elliott wave, Elon Musk, Ethereum, ethereum blockchain, family office, financial engineering, Flash crash, forensic accounting, hacker house, Hacker News, hockey-stick growth, index fund, information security, initial coin offering, Jeff Bezos, John Gilmore, Joseph Schumpeter, litecoin, Marc Andreessen, Mark Zuckerberg, Masayoshi Son, Menlo Park, move fast and break things, Multics, Network effects, offshore financial centre, open borders, Paul Graham, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, radical decentralization, ransomware, regulatory arbitrage, reserve currency, ride hailing / ride sharing, Robert Shiller, rolodex, Ross Ulbricht, Sam Altman, Sand Hill Road, Satoshi Nakamoto, sharing economy, side hustle, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, smart contracts, SoftBank, software is eating the world, Startup school, Steve Ballmer, Steve Jobs, Steve Wozniak, transaction costs, Vitalik Buterin, WeWork, work culture , Y Combinator, zero-sum game

The crash came to a halt when stock exchanges put a stop to all trading and then canceled transactions that had transpired during the machine-driven free-for-all. The 2010 flash crash led major exchanges to adopt a system called circuit breakers, which automatically pause trading in the case of unusual, logic-defying fluctuations. Seven years later, no such system existed at Coinbase. Ironically, the company had carried out a tabletop simulation of a flash crash earlier that month, but no one had thought to install circuit breakers. Adam White, who oversaw GDAX during the flash crash debacle, puts the blame on himself but also on amateurs in over their heads. These were so-called retail traders, who used GDAX’s powerful platform to trade for their own accounts, as opposed to professional traders who traded on behalf of institutions for a living.

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Morale at the office plummeted in response to customer anger over the crash and to the financial wipeout that befell many on staff. Two days later, Brian announced Coinbase would honor the trades that took place during the flash crash, while also reimbursing anyone who lost money from the haywire sell-offs—something of a lose-lose for Coinbase. This preserved goodwill among Coinbase customers on both sides of the ledger (and staff who thought they had lost it all). But the gesture cost Coinbase $20 million and later triggered an investigation from the CFTC. The flash crash proved to be an expensive education for Coinbase, though the company was hardly alone in learning painful lessons during these months of crypto mania.

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See Zhao, Changpeng DAO (Decentralized Autonomous Organization), 90–93, 145–146, 169–170 dApps, 188 Davenport, Ben, 57 decentralized finance (DeFi), 217–218 Dentacoin, 138 Dewitt, Dorothy, 224 Digital Asset, 104, 105 Digital Gold (Popper), 23 Dimon, Jamie, 103, 138, 211–213 diversity, 225 Dixon, Chris, 69, 93, 124, 157, 216 Dogecoin, 54, 181–182 Dorman, Jeff, 105–106 Draper, Tim, 167 Dread Pirate Roberts, 31, 59, 122 Earn.com, 186–187 Ehrsam, Fred, 10–14 on Ethereum and smart contracts, 93–95 on the future, 220, 225–226 the hedger and, 174 Olaf hired by, 28–29 Electronic Frontier Foundation, 100 electronic market makers, 192 Elliott Wave Theory, 151 enterprise blockchain, 73 ether, 92–93 Ethereum, 84, 87–97 Binance and, 182 blockchain issues in, 202 flash crash of, 139–141 hard fork at, 91–93 market caps of, 203 popularity of, 134–139 value of, 152 Ethereum Classic, 196 Facebook, 63, 64 Project Libra, 205–207 Winklevoss twins and, 114–115 Farmer, David, 159, 175 Federal Bureau of Investigation, 59–60, 126–127 Federal Reserve, 12, 53, 212 Federal Reserve Bank of New York, 102 Fidelity, 209–210 Financial Crimes Enforcement Network, 126 Financial Times, 136–137 Finney, Hal, 23 flash crashes, 139–141 Forbes magazine, 148 Force, Carl Mark, IV, 59–60 Fortune magazine, 206 Founder’s Fund, 166–167 Freeman, Kristian, 153 Galaxy Digital, 172 Garlinghouse, Brad, 224 GDAX (Global Digital Asset Exchange), 96–97, 101–103 flash crash in, 139–141 professional traders and, 113–117 Gemini, 97, 105, 116–117 Gemini Dollar, 205 geo-fencing, 40 Gilmore, John, 100 Give Crypto, 175 Goldman Sachs, 11–12, 37, 104, 171, 212 on bitcoin, 225 Elliott Wave Theory at, 151 Google, 40, 64, 157, 195–196 Google Ventures, 204 Graham, Paul, 36 Grayscale, 54 Grayscale Bitcoin Trust, 208 Hacker News, 78 Hacking Team, 197 Hammell, Craig, 38, 64 on adding currencies, 181–182 on Hirji, 191 on infrastructure, 155–156 Hanyecz, Laszlo, 22 hard forks, 92, 147 Haun, Katie, 17–18, 20, 155, 225 as Coinbase ally, 49 cryptocurrency expertise of, 59–60 on prosecuting bitcoin, 24, 31 at Stanford, 107, 218 Hearn, Mike, 76 hedge funds, 96, 172 Heroku, 156 hijackers, 142–143 Hilton, Paris, 144–145 Hirji, Asiff, 157–158, 173–175, 209 on Binance, 183 departure of, 198–199 on Earn.com, 187 on the future of crypto, 216–217 Srinivasan and, 193–200 style and personality of, 190–193 Hirschman, Albert, 48, 185 “hockey stick growth,” 51–52 hodlers, 83–84 HoweyCoin, 168–169 IBM, 90, 216 ICOs (initial coin offerings), 135–138 Binance and, 179 HoweyCoin and, 168–170 SEC on, 145–146, 168–170 swindles around, 141–145 impulse wave pattern, 151 infrastructure, 75–84, 155–159, 209–210 insider trading, 160 Internal Revenue Service, 121–126, 173 Jobs, Steve, 7, 99, 109, 111 JPM Coin, 212 JPMorgan Chase, 103, 104, 138, 211 Karpelès, Mark, 55–58 Knight, Phil, 39 KodakCoin, 167 Kraken, 96–97, 114 Lamborghinis, 146–147, 167 Langschaedel, Julian, 39 Lawsky, Benjamin, 127 Lee, Bobby, 82 Lee, Charlie, 39–40, 54, 80–81, 88 in Beijing, 81–83 departure of from Coinbase, 117–118 on infrastructure, 156 Litecoin and, 223 on Mt.

Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets by Donald MacKenzie

algorithmic trading, automated trading system, banking crisis, barriers to entry, bitcoin, blockchain, Bonfire of the Vanities, Bretton Woods, Cambridge Analytica, centralized clearinghouse, Claude Shannon: information theory, coronavirus, COVID-19, cryptocurrency, disintermediation, diversification, en.wikipedia.org, Ethereum, ethereum blockchain, family office, financial intermediation, fixed income, Flash crash, Google Earth, Hacker Ethic, Hibernia Atlantic: Project Express, interest rate derivative, interest rate swap, inventory management, Jim Simons, level 1 cache, light touch regulation, linked data, lockdown, low earth orbit, machine readable, market design, market microstructure, Martin Wolf, proprietary trading, Renaissance Technologies, Satoshi Nakamoto, Small Order Execution System, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, The Great Moderation, transaction costs, UUNET, zero-sum game

At the time of writing, there has been no repetition—even during the frantic pandemic-related trading in March 2020—of disruption to US share trading quite as extreme as in the 2010 flash crash. Osipovich (2020) attributes the greater robustness of stock-market infrastructure to the much enhanced attention to it that followed the flash crash, in particular the SEC’s 2014 Reg SCI (Regulation Systems Compliance and Integrity).26 At 9:33 a.m. on October 15, 2014, a different market—the market in US Treasurys—suffered an event broadly analogous to, although not as severe as, the flash crash: a brief upward price spike (very large by the standards of the Treasurys market), one that again quickly reversed, in this case within 12 minutes.

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“Thinning Liquidity in Key Futures Market Worries Traders.” Wall Street Journal, March 25. Available at https://wsj.com/articles/thinning-liquidity-in-key-futures-market-worries-traders-11553515200, accessed December 24, 2019. ________. 2020. “Post-Flash Crash Fixes Bolstered Markets during Coronavirus Selloff.” Wall Street Journal, May 5. Available at https://www.wsj.com/articles/post-flash-crash-fixes-bolstered-markets-during-coronavirus-selloff-11588671000, accessed May 5, 2020. Pagliari, Stefano, and Kevin Young. 2016. “The Interest Ecology of Financial Regulation: Interest Group Plurality in the Design of Financial Regulatory Policies.”

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Two factors, not mutually exclusive, are particularly relevant to this book.27 The first is simply that, as discussed in chapter 1, liquidity is “sticky,” or path dependent—once it becomes concentrated in a particular trading venue, it tends to stay there—and (other things being equal) price changes tend to manifest themselves in the most liquid venues before they do so in less liquid venues. If an institutional investor wants to execute a very large trade (for example, the sale of 75,000 ES contracts, the equivalent of selling shares worth $4.1 billion, which triggered the May 2010 “flash crash” in US share prices, discussed in chapter 7), he or she will turn to the market best able to handle the largest trades, which, participants report, has—at least until quite recently—been the CME’s index futures.28 In so doing, that investor helps maintain that market’s capacity to do so, and thus helps its prices continue to lead those in markets in which shares are traded.

Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better by Andrew Palmer

Affordable Care Act / Obamacare, Alan Greenspan, algorithmic trading, Andrei Shleifer, asset-backed security, availability heuristic, bank run, banking crisis, behavioural economics, Black Monday: stock market crash in 1987, 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 engineering, financial innovation, fixed income, Flash crash, Google Glasses, Gordon Gekko, high net worth, housing crisis, Hyman Minsky, impact investing, 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, low interest rates, margin call, Mark Zuckerberg, McMansion, Minsky moment, 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, Savings and loan crisis, short selling, Silicon Valley, Silicon Valley startup, Skype, South Sea Bubble, sovereign wealth fund, statistical model, subprime mortgage crisis, tail risk, Thales of Miletus, the long tail, transaction costs, Tunguska event, unbanked and underbanked, underbanked, Vanguard fund, web application

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.

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On those exchanges that still have traders on the floor, confusion reigned. 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.

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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. Now let’s turn to the buyers. In modern equity markets, a large proportion of transactions are carried out by high-frequency traders who are acting as market makers—intermediaries who have no intention of building an investment exposure to a specific stock.

The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber

asset allocation, bank run, Bear Stearns, behavioural economics, 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, data science, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial engineering, financial innovation, fixed income, Flash crash, geopolitical risk, Henri Poincaré, impact investing, 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, Robert Solow, Saturday Night Live, self-driving car, seminal paper, 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

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.

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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.

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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, Alan Greenspan, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Black-Scholes formula, book value, Brownian motion, business cycle, buy and hold, buy low sell high, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, currency risk, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, global macro, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, junk bonds, late capitalism, law of one price, Long Term Capital Management, low interest rates, managed futures, margin call, market clearing, market design, market friction, Market Wizards by Jack D. Schwager, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, Phillips curve, price discovery process, price stability, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, short squeeze, SoftBank, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, tail risk, technology bubble, time dilation, time value of money, total factor productivity, transaction costs, two and twenty, value at risk, Vanguard fund, yield curve, zero-coupon bond

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.

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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.

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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.”

Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies by Jeremy Siegel

Alan Greenspan, AOL-Time Warner, Asian financial crisis, asset allocation, backtesting, banking crisis, Bear Stearns, behavioural economics, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, 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, currency risk, Daniel Kahneman / Amos Tversky, Deng Xiaoping, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, equity risk 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, Glass-Steagall Act, housing crisis, Hyman Minsky, implied volatility, income inequality, index arbitrage, index fund, indoor plumbing, inflation targeting, invention of the printing press, Isaac Newton, it's over 9,000, John Bogle, joint-stock company, London Interbank Offered Rate, Long Term Capital Management, loss aversion, machine readable, market bubble, mental accounting, Minsky moment, Money creation, 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, proprietary trading, purchasing power parity, quantitative easing, random walk, Richard Thaler, risk free rate, risk tolerance, risk/return, Robert Gordon, 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, Tyler Cowen: Great Stagnation, uptick rule, 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.

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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.

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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, Anthropocene, Anton Chekhov, Apple II, Benoit Mandelbrot, Boeing 747, Chekhov's gun, citation needed, combinatorial explosion, Computing Machinery and Intelligence, Danny Hillis, data science, David Brooks, digital map, discovery of the americas, driverless car, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, Hans Moravec, HyperCard, Ian Bogost, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Neal Stephenson, Netflix Prize, Nicholas Carr, Nick Bostrom, Parkinson's law, power law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, SimCity, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, synthetic biology, systems thinking, the long tail, Therac-25, Tyler Cowen, 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.

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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.

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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, behavioural economics, Benoit Mandelbrot, Bletchley Park, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, Computing Machinery and Intelligence, 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

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. March 20, 2013. http://money.cnn.com/2013/03/20/investing/mini-flash-crash/. 124they found thousands of “ultrafast extreme events”: Johnson, Neil, Guannan Zhao, Eric Hunsader, Hong Qi, Nicholas Johnson, Jing Meng and Brian Tivnan. “Abrupt Rise of New Machine Ecology Beyond Human Response Time.” Scientific Reports 3 (2013). doi:10.1038/srep02627. 124“Humans are unable to participate in real time”: Quote from: “Robots Take Over Economy: Sudden Rise of Global Ecology of Interacting Robots Trade at Speeds Too Fast for Humans” (press release, University of Miami, September 11, 2013). 125The campus is a maze of neo-Gothic halls: Author experience. 125“The trees on the right were passing me”: Halmos, Paul.

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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.

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The speed of the game can lead to serious problems. After all, it’s hard to work out who will move first when algorithms are faster than the eye can see. “You don’t have much time to think,” Farmer said. “It creates a big danger of over-reaction and herding.” Some traders have reported that mini flash crashes happen frequently. These shocks are not severe enough to grab headlines, but they are still there to be found by anyone who looks hard enough. A share price might drop in a fraction of a second, or trading activity will suddenly increase a hundredfold. In fact, there might be several such crashes every day.

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, AlphaGo, Anthropocene, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, Boeing 747, British Empire, Brownian motion, Buckminster Fuller, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, coastline paradox / Richardson effect, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, drone strike, Edward Snowden, Eyjafjallajökull, Fairchild Semiconductor, fake news, fear of failure, Flash crash, fulfillment center, Google Earth, Greyball, Haber-Bosch Process, Higgs boson, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, ITER tokamak, James Bridle, John von Neumann, Julian Assange, Kickstarter, Kim Stanley Robinson, Large Hadron Collider, late capitalism, Laura Poitras, Leo Hollis, lone genius, machine translation, mandelbrot fractal, 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, security theater, self-driving car, Seymour Hersh, 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, warehouse robotics, 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.

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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.

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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.

Why Aren't They Shouting?: A Banker’s Tale of Change, Computers and Perpetual Crisis by Kevin Rodgers

Alan Greenspan, algorithmic trading, bank run, banking crisis, Basel III, Bear Stearns, Berlin Wall, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black-Scholes formula, buy and hold, buy low sell high, call centre, capital asset pricing model, collapse of Lehman Brothers, Credit Default Swap, currency peg, currency risk, diversification, Fall of the Berlin Wall, financial innovation, Financial Instability Hypothesis, fixed income, Flash crash, Francis Fukuyama: the end of history, Glass-Steagall Act, Hyman Minsky, implied volatility, index fund, interest rate derivative, interest rate swap, invisible hand, John Meriwether, latency arbitrage, law of one price, light touch regulation, London Interbank Offered Rate, Long Term Capital Management, Minsky moment, money market fund, Myron Scholes, Northern Rock, Panopticon Jeremy Bentham, Ponzi scheme, prisoner's dilemma, proprietary trading, quantitative easing, race to the bottom, risk tolerance, risk-adjusted returns, Silicon Valley, systems thinking, technology bubble, The Myth of the Rational Market, The Wisdom of Crowds, Tobin tax, too big to fail, value at risk, vertical integration, Y2K, zero-coupon bond, zero-sum game

., paragraph 50. 10 ‘Traders Said to Rig Currency Rates to Profit Off Clients’, Liam Vaughan, Gavin Finch and Ambereen Choudhury, Bloomberg, 12 June 2013, http://www.bloomberg.com/news/articles/2013-06-11/traders-said-to-rig-currency-rates-to-profit-off-clients 11 Ibid. 12 Ibid. 13 ‘FCA fines five banks £1.1 billion for FX failings and announces industry-wide remediation programme’, Financial Conduct Authority, 12 November 2014, http://www.fca.org.uk/news/fca-fines-five-banks-for-fx-failings 14 ‘Six banks fined $5.6bn over rigging of foreign exchange markets’, Financial Times, Gina Chon, Caroline Binham and Laura Noonan, 20 May 2015, http://www.ft.com/cms/s/0/23fa681c-fe73-11e4-be9f-00144feabdc0.html#slide0 15 Slightly clarified transcript from ‘Examples of Misconduct in Private Chat Rooms’, Commodity Futures Trading Commission, Office of Public Affairs, 2014, http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/hsbcmisconduct111114.pdf 16 ‘“Flash crash trader” Navinder Singh Sarao loses bail appeal’, Simon Goodley, Guardian, 20 May 2015, http://www.theguardian.com/business/2015/may/20/flash-crash-trader-navinder-singh-sarao-loses-bail-appeal 17 ‘Disruptive Practices Prohibited / Rule 575’, CME, CBOT, NYMEX & COMEX, 15 September 2014, http://www.cmegroup.com/rulebook/files/ra1405-5r.pdf 18 ‘Recommendations for Equitable Allocation of Trades in High Frequency Trading Environments’, John W.

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‘Think of it as the loaded tranquiliser gun at the zoo in case the bear escapes.’ It was never used. But whether computers have gone rogue or not, the new electronic fragility of both the FX and equity markets has been shown several times in recent years and the short-term effects have been spectacular. Late evening London time, 6 May 2010: ‘the Flash Crash’. Within a few minutes, driven by computerised orders, the Dow Jones Industrial Average records one of its biggest ever losses as it plummets 1,000 points (9 per cent), sending markets into pandemonium. Funds playing the correlations between markets start automatically hedging in FX and the Japanese yen rockets 4 per cent versus the US dollar in minutes.9 Early morning, Tokyo, 17 March 2011 – another extraordinary 4 per cent surge in the yen happens in 25 minutes as computerised stop losses for Mrs Watanabe hit an illiquid market with London and New York asleep.10 1 August 2012, Knight Capital, the largest trader on the New York Stock Exchange, is effectively wiped out as a computer glitch results in 4 million spurious executions in 154 stocks for more than 397 million shares in approximately 45 minutes.11 Academics, regulators and lawyers are still arguing about the precise causes and lessons of the Flash Crash but no one doubts that the hair-trigger response of interlinked computers exaggerated the move and sent it radiating out to other markets.

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Funds playing the correlations between markets start automatically hedging in FX and the Japanese yen rockets 4 per cent versus the US dollar in minutes.9 Early morning, Tokyo, 17 March 2011 – another extraordinary 4 per cent surge in the yen happens in 25 minutes as computerised stop losses for Mrs Watanabe hit an illiquid market with London and New York asleep.10 1 August 2012, Knight Capital, the largest trader on the New York Stock Exchange, is effectively wiped out as a computer glitch results in 4 million spurious executions in 154 stocks for more than 397 million shares in approximately 45 minutes.11 Academics, regulators and lawyers are still arguing about the precise causes and lessons of the Flash Crash but no one doubts that the hair-trigger response of interlinked computers exaggerated the move and sent it radiating out to other markets. Regulators have responded by instituting new exchange circuit breakers in the equities markets. No such controls are possible in FX. Another shadow across the rosy picture of a better and faster FX market is the issue of spreads.

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

Taleb, who had the title of Senior Scientific Advisor at Universa, was never involved in its daily operations. Instead, the firm leveraged his fame as a world-renowned writer and thinker to channel attention from wealthy investors. Universa had made a fortune during the 2008 Global Financial Crisis, as well as other turbulent periods such as the 2010 Flash Crash, the 2011 downgrade of U.S. debt, a freak implosion in 2015 that earned Universa $1 billion in less than a week, and other big spikes in volatility, like the so-called Volmageddon of 2018. Universa called the strategy the Black Swan Protection Protocol. The protocol’s goal: to shield its investors from Black Swans.

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The visit to SpaceX would be Taleb’s first and last Edge meeting. It marked the beginning of his deep concern about scientists tampering with genetics, which would later turn into a crusade against genetically modified organisms that put him squarely in the crosshairs of one of the world’s largest corporations. CHAPTER 11 FLASH CRASH Shortly after 2:15 p.m. Eastern time on May 6, 2010, a trader at Universa’s Santa Monica office placed an order with the firm’s broker, Barclays Capital, to purchase fifty thousand put option contracts in Chicago’s trading pits. The options would pay off if the S&P 500 fell to 800 by a certain date in June—a massive decline.

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The options would pay off if the S&P 500 fell to 800 by a certain date in June—a massive decline. The S&P was trading at 1135. Universa paid $7.5 million for that bet. If the S&P did fall to 800 by the June expiration date, the trade would be worth $1 billion. In less than half an hour, the U.S. stock market would witness one of the most bizarre, volatile moments in its history—the Flash Crash, as the financial press later called it. In a matter of minutes, the Dow industrials fell one thousand points. It was a Black Swan, truly unforeseen, the most sudden, volatile market crash since Black Monday. As stocks tumbled and volatility surged, put options Universa had bought in April for $2 a piece if the S&P 500 fell below 1100—it was trading at 1200 at the time—surged in value.

Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

The trader made a multitude of both good and bad decisions in the three hours around the 2010 Flash Crash. The trading described in this story is a microcosm of everything that can go right and wrong in trading. Traders make good, careful decisions and get rewarded, they make bad decisions and get punished … but then sometimes a good decision leads to a bad outcome … or a bad decision is rescued by good luck. Every trader is a steaming hot bowl of bias stew and must maintain self-awareness and lucidity behind the screens as the trading day oscillates between boredom and terror. That story of the 2010 Flash Crash, just like this book, is all about the razor thin line that separates success and failure in trading.

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A weak poker player might beat a professional in a one-hour session but he will go bust in a marathon cash game. This discussion is important because luck plays a major role in trading. It operates through multiple channels like short-term variance and path dependence. In the Flash Crash story at the start of this book, there were multiple instances where the trader got lucky and if you re-ran that Flash Crash USD- JPY scenario a million times, there would be plenty of instances where the trader came out deep in the red. What seems like luck in life is often just careful observation and the ability to capitalize on opportunity. Richard Wiseman, who has spent years researching luck, set up a variety of interesting experiments to show that luck is not really luck.

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These are impulsive trades with no rationale. His planned stop loss is 90.85 but before he has time to input a stop loss order, he notices S&Ps lurch lower on a huge volume surge. He puts on his headset and fires up the S&P squawk to see what’s going on. If you want to hear the soundtrack to what happens next, Google “Flash crash stock market 2010 squawk” and select one of the YouTube replay videos The announcer’s voice is strained as he narrates an unexplained fall in stocks from 1150 to 1120. USDJPY skips through 91.00 and the trader’s P&L shrinks to $2.0 million. He tries to sell at 90.80 and whiffs. USDJPY is suddenly in freefall. 90.10 trades. 90.00 breaks.

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, Bear Stearns, behavioural economics, Bernie Madoff, Black Swan, buy and hold, Carl Icahn, centralized clearinghouse, clean water, compensation consultant, 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 engineering, financial innovation, financial intermediation, fixed income, Flash crash, Glass-Steagall Act, income inequality, index fund, information asymmetry, invisible hand, John Bogle, Kenneth Arrow, Kickstarter, light touch regulation, London Whale, Long Term Capital Management, moral hazard, Myron Scholes, Northern Rock, passive investing, Paul Volcker talking about ATMs, payment for order flow, performance metric, Ponzi scheme, post-work, principal–agent problem, rent-seeking, Ronald Coase, seminal paper, 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

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.

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“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.

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“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.

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, Apollo 13, backtesting, Bear Stearns, big-box store, Black Monday: stock market crash in 1987, Black-Scholes formula, call centre, Charles Babbage, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, financial engineering, Flash crash, G4S, Gödel, Escher, Bach, Hacker News, High speed trading, Howard Rheingold, index fund, Isaac Newton, Jim Simons, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, machine translation, Marc Andreessen, Mark Zuckerberg, market bubble, Max Levchin, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, proprietary trading, 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

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.”

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“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.

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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.

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, air gap, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, Bletchley Park, brain emulation, California energy crisis, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, Computing Machinery and Intelligence, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, dual-use technology, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Hacker News, Hans Moravec, Isaac Newton, Jaron Lanier, Jeff Hawkins, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, machine translation, mutually assured destruction, natural language processing, Neil Armstrong, Nicholas Carr, Nick Bostrom, optical character recognition, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, precautionary principle, prisoner's dilemma, Ray Kurzweil, Recombinant DNA, Rodney Brooks, rolling blackouts, 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 Jurvetson, Steve Wozniak, strong AI, Stuxnet, subprime mortgage crisis, 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

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.

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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.

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And an intelligence explosion would be opaque in the computational finance universe, for at least four reasons. Like many cognitive architectures, it would probably use neural networks, genetic programming, and other “black box” AI techniques. Second, the high-bandwidth, millisecond-fast transmissions take place faster than humans can react—look at what happened during the Flash Crash. Third, the system is incredibly complex—there’s no one quant or even a group of quants (a quantum? a gaggle? what’s the quants’ collective noun?) who can explain the algorithm ecosystem of Wall Street and how the algorithms interact. Finally, if a formidable intelligence emerged from computational finance, it would almost certainly be kept secret so long as it was making money for its creators.

Never Let a Serious Crisis Go to Waste: How Neoliberalism Survived the Financial Meltdown by Philip Mirowski

"there is no alternative" (TINA), Adam Curtis, Alan Greenspan, Alvin Roth, An Inconvenient Truth, Andrei Shleifer, asset-backed security, bank run, barriers to entry, Basel III, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, blue-collar work, bond market vigilante , bread and circuses, Bretton Woods, Brownian motion, business cycle, capital controls, carbon credits, 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, democratizing finance, disinformation, do-ocracy, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, facts on the ground, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, Flash crash, full employment, George Akerlof, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Greenspan put, Hernando de Soto, housing crisis, Hyman Minsky, illegal immigration, income inequality, incomplete markets, information asymmetry, invisible hand, Jean Tirole, joint-stock company, junk bonds, 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, Phillips curve, Ponzi scheme, Post-Keynesian economics, precariat, prediction markets, price mechanism, profit motive, public intellectual, quantitative easing, race to the bottom, random walk, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, savings glut, school choice, sealed-bid auction, search costs, Silicon Valley, South Sea Bubble, Steven Levy, subprime mortgage crisis, tail risk, technoutopianism, The Chicago School, The Great Moderation, the map is not the territory, The Myth of the Rational Market, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wisdom of Crowds, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, Tobin tax, tontine, too big to fail, transaction costs, Tyler Cowen, vertical integration, 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.

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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.

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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.

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, data science, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, functional programming, Gini coefficient, hype cycle, illegal immigration, iterative process, labor-force participation, loose coupling, machine readable, natural language processing, Netflix Prize, One Laptop per Child (OLPC), power law, quantitative trading / quantitative finance, recommendation engine, selection bias, sentiment analysis, SQL injection, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application

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.

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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.

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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.

Rigged Money: Beating Wall Street at Its Own Game by Lee Munson

affirmative action, Alan Greenspan, asset allocation, backtesting, barriers to entry, Bear Stearns, 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, fear index, fiat currency, financial engineering, financial innovation, fixed income, Flash crash, follow your passion, German hyperinflation, Glass-Steagall Act, global macro, High speed trading, housing crisis, index fund, joint-stock company, junk bonds, managed futures, Market Wizards by Jack D. Schwager, Michael Milken, military-industrial complex, money market fund, moral hazard, Myron Scholes, National best bid and offer, off-the-grid, passive investing, Ponzi scheme, power law, price discovery process, proprietary trading, random walk, Reminiscences of a Stock Operator, risk tolerance, risk-adjusted returns, risk/return, Savings and loan crisis, short squeeze, stocks for the long run, stocks for the long term, too big to fail, trade route, Vanguard fund, walking around money

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.

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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.

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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.

Wait: The Art and Science of Delay by Frank Partnoy

algorithmic trading, Atul Gawande, behavioural economics, Bernie Madoff, Black Swan, blood diamond, Cass Sunstein, Checklist Manifesto, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, cotton gin, 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, MITM: man-in-the-middle, Nick Leeson, paper trading, Paul Graham, payday loans, Pershing Square Capital Management, Ralph Nader, Richard Thaler, risk tolerance, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific management, six sigma, social discount rate, Spread Networks laid a new fibre optics cable between New York and Chicago, Stanford marshmallow experiment, statistical model, Steve Jobs, systems thinking, The Market for Lemons, the scientific method, The Wealth of Nations by Adam Smith, upwardly mobile, Walter Mischel, work culture

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.

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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.

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This section is taken from Frank Partnoy, “Don’t Blink: Snap Decisions and Securities Regulation,” Brooklyn Law Review 77 (2011): 151–179, which I wrote as part of my Abraham L. Pomerantz Lecture on securities regulation, delivered March 15, 2011, at Brooklyn Law School. 6. 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?

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black Swan, blockchain, Boeing 747, Bonfire of the Vanities, Bretton Woods, Brexit referendum, 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, currency risk, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, driverless car, 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, Glass-Steagall Act, global macro, 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, junk bonds, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, low interest rates, machine readable, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, Minsky moment, Money creation, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, operational security, Paul Samuelson, Peace of Westphalia, Phillips curve, Pierre-Simon Laplace, plutocrats, prediction markets, price anchoring, price stability, proprietary trading, public intellectual, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk free rate, risk-adjusted returns, Robert Solow, Ronald Reagan, Savings and loan crisis, Silicon Valley, sovereign wealth fund, special drawing rights, stock buybacks, stocks for the long run, tech billionaire, 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, We are all Keynesians now, Westphalian system

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). Those are highly significant events. 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.

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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. At the open of trading that day, the Swiss National Bank announced it would abandon the €0.8325 peg to the franc the bank had maintained since 2012.

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These intraday yield ranges, between 37 and 47.5 basis points on four occasions, compare with an average intraday yield range of 8 basis points on the approximately four thousand trading days since October 1998. (Interestingly, the degree distribution of all ten-year Treasury note intraday yield ranges since 1998 is not a normal distribution as VaR advocates expect, but a perfect power curve—exactly what complexity theory predicts.) 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.

The Alchemists: Three Central Bankers and a World on Fire by Neil Irwin

"World Economic Forum" Davos, Alan Greenspan, Ayatollah Khomeini, bank run, banking crisis, Bear Stearns, 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 engineering, financial innovation, Flash crash, foreign exchange controls, George Akerlof, German hyperinflation, Google Earth, hiring and firing, inflation targeting, Isaac Newton, Julian Assange, low cost airline, low interest rates, market bubble, market design, middle-income trap, Money creation, money market fund, moral hazard, mortgage debt, new economy, Nixon triggered the end of the Bretton Woods system, Northern Rock, Paul Samuelson, price stability, public intellectual, quantitative easing, rent control, reserve currency, Robert Shiller, Robert Solow, rolodex, Ronald Reagan, Savings and loan crisis, savings glut, Socratic dialogue, sovereign wealth fund, The Great Moderation, too big to fail, union organizing, WikiLeaks, yield curve, Yom Kippur War

., 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.

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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.

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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.

Fed Up: An Insider's Take on Why the Federal Reserve Is Bad for America by Danielle Dimartino Booth

Affordable Care Act / Obamacare, Alan Greenspan, asset-backed security, bank run, barriers to entry, Basel III, Bear Stearns, Bernie Sanders, Black Monday: stock market crash in 1987, 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 engineering, financial innovation, fixed income, Flash crash, forward guidance, full employment, George Akerlof, Glass-Steagall Act, greed is good, Greenspan put, high net worth, housing crisis, income inequality, index fund, inflation targeting, interest rate swap, invisible hand, John Meriwether, Joseph Schumpeter, junk bonds, liquidity trap, London Whale, Long Term Capital Management, low interest rates, margin call, market bubble, Mexican peso crisis / tequila crisis, money market fund, moral hazard, Myron Scholes, natural language processing, Navinder Sarao, negative equity, new economy, Northern Rock, obamacare, Phillips curve, price stability, proprietary trading, pushing on a string, quantitative easing, regulatory arbitrage, Robert Shiller, Ronald Reagan, selection bias, short selling, side project, Silicon Valley, stock buybacks, tail risk, The Great Moderation, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, yield curve

While stock markets do crash, immediate rebounds like that were unprecedented. I started calling all of my contacts on trading desks. They had no answers. 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?

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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. That was hard to square with how one trader described him: “This guy, for want of a better word, had balls. He used to get into big positions, he saw the risk, then saw the reward, and he took the trades.”

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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.

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, Computing Machinery and Intelligence, corporate governance, crowdsourcing, driverless car, drop ship, Easter island, en.wikipedia.org, Erik Brynjolfsson, estate planning, Fairchild Semiconductor, 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, Kiva Systems, Larry Ellison, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Nick Bostrom, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, short squeeze, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Vitalik Buterin, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

“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.

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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.

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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

"Hurricane Katrina" Superdome, algorithmic trading, Alvin Toffler, Andrew Keen, bank run, behavioural economics, 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, Future Shock, 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, James Bridle, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, lateral thinking, Law of Accelerating Returns, Lewis Mumford, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, messenger bag, Milgram experiment, mirror neurons, mutually assured destruction, negative equity, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, off-the-grid, passive investing, pattern recognition, peak oil, Peter Pan Syndrome, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, scientific management, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, SimCity, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, technological determinism, the medium is the message, The Wisdom of Crowds, theory of mind, Tragedy of the Commons, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K, zero-sum game

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.

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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.

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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.

Why I Left Goldman Sachs: A Wall Street Story by Greg Smith

Alan Greenspan, always be closing, asset allocation, Bear Stearns, 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, fear index, financial engineering, fixed income, Flash crash, glass ceiling, Glass-Steagall Act, Goldman Sachs: Vampire Squid, high net worth, information asymmetry, London Interbank Offered Rate, mega-rich, money market fund, new economy, Nick Leeson, proprietary trading, quantitative hedge fund, Renaissance Technologies, short selling, short squeeze, Silicon Valley, Skype, sovereign wealth fund, Stanford marshmallow experiment, statistical model, technology bubble, too big to fail

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. To me, it simply looked scary—one more sign that the global capital markets were officially out of balance. Investors felt the same way. With the flash crash following hard on the heels of the SEC charges, clients were rattled. And being rattled, they stopped trading. They froze. Things turned dead quiet once more; the layoffs recommenced.

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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.

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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.

The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

3D printing, algorithmic bias, algorithmic trading, Alvin Toffler, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, classic study, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Ford Model T, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, Lewis Mumford, lifelogging, machine readable, machine translation, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, Paradox of Choice, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, scientific management, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, stable marriage problem, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, technological determinism, technological solutionism, TED Talk, the long tail, 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

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.

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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.

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(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, Alan Greenspan, altcoin, Apple Newton, bank run, banking crisis, bitcoin, Bitcoin Ponzi scheme, blockchain, Bretton Woods, buy and hold, California gold rush, capital controls, carbon footprint, clean water, Cody Wilson, collaborative economy, collapse of Lehman Brothers, Columbine, Credit Default Swap, cross-border payments, cryptocurrency, David Graeber, decentralized internet, disinformation, disintermediation, Dogecoin, driverless car, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, Firefox, Flash crash, Ford Model T, Fractional reserve banking, Glass-Steagall Act, hacker house, Hacker News, 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 creation, money: store of value / unit of account / medium of exchange, Nelson Mandela, Network effects, new economy, new new economy, Nixon shock, Nixon triggered the end of the Bretton Woods system, off-the-grid, offshore financial centre, payday loans, Pearl River Delta, peer-to-peer, peer-to-peer lending, pets.com, Ponzi scheme, prediction markets, price stability, printed gun, profit motive, QR code, RAND corporation, regulatory arbitrage, rent-seeking, reserve currency, 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, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, underbanked, Vitalik Buterin, WikiLeaks, Y Combinator, Y2K, zero-sum game, Zimmermann PGP

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.

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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.

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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.

This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman

23andMe, adjacent possible, Albert Einstein, Alfred Russel Wallace, Anthropocene, banking crisis, Barry Marshall: ulcers, behavioural economics, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, Bletchley Park, butterfly effect, Cass Sunstein, cloud computing, cognitive load, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Evgeny Morozov, Exxon Valdez, Flash crash, Flynn Effect, Garrett Hardin, Higgs boson, hive mind, impulse control, information retrieval, information security, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jaron Lanier, Johannes Kepler, John von Neumann, Kevin Kelly, Large Hadron Collider, lifelogging, machine translation, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, Nick Bostrom, ocean acidification, open economy, Pierre-Simon Laplace, place-making, placebo effect, power law, 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, scientific management, security theater, selection bias, Silicon Valley, Stanford marshmallow experiment, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, Stuart Kauffman, sugar pill, synthetic biology, the scientific method, Thorstein Veblen, Turing complete, Turing machine, twin studies, Vilfredo Pareto, Walter Mischel, Whole Earth Catalog, WikiLeaks, zero-sum game

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.

…

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 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.

…

., 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, Bletchley Park, British Empire, business process, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, complexity theory, Computing Machinery and Intelligence, continuous integration, Conway's Game of Life, cosmological principle, dark matter, data science, deep learning, DeepMind, dematerialisation, double helix, Douglas Hofstadter, driverless car, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, index card, industrial robot, intentional community, 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, machine translation, millennium bug, mirror neurons, Moravec's paradox, natural language processing, Nick Bostrom, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, Plato's cave, post-industrial society, power law, precautionary principle, prediction markets, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Second Machine Age, self-driving car, seminal paper, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, Strategic Defense Initiative, strong AI, Stuart Kauffman, synthetic biology, systems thinking, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

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.

…

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.

…

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, Alan Greenspan, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, benefit corporation, 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, data science, deep learning, disintermediation, diversified portfolio, Dutch auction, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gamification, Garrett Hardin, gentrification, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, independent contractor, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, Large Hadron Collider, loss aversion, low interest rates, 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, power law, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Russell Brand, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, stock buybacks, TaskRabbit, the Cathedral and the Bazaar, The Future of Employment, the long tail, trade route, Tragedy of the Commons, transportation-network company, Turing test, Uber and Lyft, Uber for X, uber lyft, unpaid internship, Vitalik Buterin, warehouse robotics, Wayback Machine, Y Combinator, young professional, zero-sum game, Zipcar

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.

…

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?

…

., 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, air gap, algorithmic bias, autonomous vehicles, barriers to entry, Big Tech, bitcoin, blockchain, Brian Krebs, business process, Citizen Lab, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, disinformation, Donald Trump, driverless car, drone strike, Edward Snowden, Elon Musk, end-to-end encryption, fault tolerance, Firefox, Flash crash, George Akerlof, incognito mode, industrial robot, information asymmetry, information security, Internet of things, invention of radio, job automation, job satisfaction, John Gilmore, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, national security letter, Network effects, Nick Bostrom, NSO Group, pattern recognition, precautionary principle, printed gun, profit maximization, Ralph Nader, RAND corporation, ransomware, real-name policy, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Seymour Hersh, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, sparse data, Stanislav Petrov, Stephen Hawking, Stuxnet, supply-chain attack, surveillance capitalism, The Market for Lemons, Timothy McVeigh, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, Wayback Machine, web application, WikiLeaks, Yochai Benkler, zero day

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.

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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.

…

, 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, Anthropocene, anti-communist, artificial general intelligence, autism spectrum disorder, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, Computing Machinery and Intelligence, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, Demis Hassabis, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, general purpose technology, Geoffrey Hinton, Gödel, Escher, Bach, hallucination problem, Hans Moravec, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Large Hadron Collider, longitudinal study, machine translation, megaproject, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Nick Bostrom, Norbert Wiener, NP-complete, nuclear winter, operational security, optical character recognition, paperclip maximiser, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, search costs, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, Strategic Defense Initiative, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, time dilation, Tragedy of the Commons, transaction costs, trolley problem, Turing machine, Vernor Vinge, WarGames: Global Thermonuclear War, 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.

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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.

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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, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, 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 Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

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.

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* * * = 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.

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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.

Cogs and Monsters: What Economics Is, and What It Should Be by Diane Coyle

3D printing, additive manufacturing, Airbnb, Al Roth, Alan Greenspan, algorithmic management, Amazon Web Services, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Big bang: deregulation of the City of London, biodiversity loss, bitcoin, Black Lives Matter, Boston Dynamics, Bretton Woods, Brexit referendum, business cycle, call centre, Carmen Reinhart, central bank independence, choice architecture, Chuck Templeton: OpenTable:, cloud computing, complexity theory, computer age, conceptual framework, congestion charging, constrained optimization, coronavirus, COVID-19, creative destruction, credit crunch, data science, DeepMind, deglobalization, deindustrialization, Diane Coyle, discounted cash flows, disintermediation, Donald Trump, Edward Glaeser, en.wikipedia.org, endogenous growth, endowment effect, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, Evgeny Morozov, experimental subject, financial deregulation, financial innovation, financial intermediation, Flash crash, framing effect, general purpose technology, George Akerlof, global supply chain, Goodhart's law, Google bus, haute cuisine, High speed trading, hockey-stick growth, Ida Tarbell, information asymmetry, intangible asset, Internet of things, invisible hand, Jaron Lanier, Jean Tirole, job automation, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, knowledge worker, Les Trente Glorieuses, libertarian paternalism, linear programming, lockdown, Long Term Capital Management, loss aversion, low earth orbit, lump of labour, machine readable, market bubble, market design, Menlo Park, millennium bug, Modern Monetary Theory, Mont Pelerin Society, multi-sided market, Myron Scholes, Nash equilibrium, Nate Silver, Network effects, Occupy movement, Pareto efficiency, payday loans, payment for order flow, Phillips curve, post-industrial society, price mechanism, Productivity paradox, quantitative easing, randomized controlled trial, rent control, rent-seeking, ride hailing / ride sharing, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Robinhood: mobile stock trading app, Ronald Coase, Ronald Reagan, San Francisco homelessness, savings glut, school vouchers, sharing economy, Silicon Valley, software is eating the world, spectrum auction, statistical model, Steven Pinker, tacit knowledge, The Chicago School, The Future of Employment, The Great Moderation, the map is not the territory, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Uber for X, urban planning, winner-take-all economy, Winter of Discontent, women in the workforce, Y2K

With about half the trades on financial markets now computerised HFT, this latest development conjures up the image of an algorithmic web of signals bouncing off low-earth orbit satellites, all for the financial markets to make more money faster off their clients. There is evidence that the so-called flash crash of 6 May 2010, when the Dow Jones share price index fell 600 points in 6 minutes only to recover fully 20 minutes later, was due to automated trading of this kind. In 2015 the US authorities tried to pin the blame on a solo mathematically-gifted day trader, based in suburban West London, but as a recent account details, the crash was not the work of a human mastermind but rather of a complex network of machines and regulation (Vaughan 2020).

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Robinhood, the retail trading platform driving much of the Gamestop phenomenon in early 2021, was running orders through Citadel, an HFT trader that paid for those retail orders so it could glean intelligence about market conditions to give its own algorithms an advantage (Van Doren 2021). The Gamestop rise and fall was all too obvious, but there is some evidence that there are very many flash crashes—more than 18,500 in the five years to 2011—too fast for humans to notice them (Johnson et al. 2012). In a report on this research, John Cartlidge of the University of Bristol, was quoted as saying: ‘Economic theory has always lagged behind economic reality, but now the speed of technological change is widening that gap at an exponential rate.

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., 2021, ‘GameStop, Payments for Order Flow, and High Frequency Trading’, Cato Institute, 1 February, https://www.cato.org/blog/gamestop-payments-order-flow-high-frequency-trading, accessed 6 February 2021. Van Reenen, J., 2018, ‘Increasing Differences Between Firms: Market Power and the Macro-Economy,’ CEP Discussion Papers 1576, Centre for Economic Performance, London School of Economics. Vaughan, N., 2020, The Flash Crash, New York: Penguin Random House. Von Mises, L., 1920, ‘Die Wirtschaftsrechnung im sozialistischen Gemeinwesen’, Archiv für Sozialwissenschaften, 47, 186–121; published in English as “Economic Calculation in the Socialist Commonwealth”, trans. S. Adler, in F. A. Hayek (ed.), Collectivist Economic Planning: Critical Studies on the Possibilities of Socialism, London: Routledge & Kegan Paul Ltd., 1935, ch. 3, 87–130.

Planet Ponzi by Mitch Feierstein

Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Asian financial crisis, asset-backed security, bank run, banking crisis, barriers to entry, Bear Stearns, Bernie Madoff, book value, 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, Future Shock, Glass-Steagall Act, government statistician, high net worth, High speed trading, illegal immigration, income inequality, interest rate swap, invention of agriculture, junk bonds, light touch regulation, Long Term Capital Management, low earth orbit, low interest rates, mega-rich, money market fund, moral hazard, mortgage debt, negative equity, Neil Armstrong, Northern Rock, obamacare, offshore financial centre, oil shock, pensions crisis, plutocrats, Ponzi scheme, price anchoring, price stability, proprietary trading, purchasing power parity, quantitative easing, risk tolerance, Robert Shiller, Ronald Reagan, tail risk, 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.

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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. 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.

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, behavioural economics, Berlin Wall, bitcoin, Build a better mousetrap, centralized clearinghouse, Chuck Templeton: OpenTable:, commoditize, computer age, computerized markets, crowdsourcing, deferred acceptance, desegregation, Dutch auction, 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, PalmPilot, 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

The wider the spreads the liquidity providers quote, the further prices have to jump before they can be exploited on both sides of the trade this way, and the more they pass on the cost of protecting themselves to ordinary investors. Very high speed trading can also contribute to instability in the market. 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.

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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. Of course, fast cables and computers aren’t the only ways to get information about the market and to act on it before others do.

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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.

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, fake news, Flash crash, forensic accounting, game design, High speed trading, Julian Assange, millennium bug, Minecraft, Neil Armstrong, null island, obamacare, off-by-one error, orbital mechanics / astrodynamics, publication bias, Richard Feynman, Richard Feynman: Challenger O-ring, selection bias, SQL injection, subprime mortgage crisis, Tacoma Narrows Bridge, Therac-25, value at risk, WikiLeaks, Y2K

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.

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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.

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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.

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, 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, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

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.  

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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.

Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky

algorithmic trading, asset allocation, automated trading system, backpropagation, backtesting, barriers to entry, behavioural economics, book value, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, currency risk, data science, deep learning, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, financial intermediation, Flash crash, Geoffrey Hinton, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, low interest rates, machine readable, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, Performance of Mutual Funds in the Period, popular capitalism, prediction markets, price discovery process, profit motive, proprietary trading, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, 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 bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

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.

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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.”

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back by Bruce Schneier

4chan, Airbnb, airport security, algorithmic trading, Alignment Problem, AlphaGo, Automated Insights, banking crisis, Big Tech, bitcoin, blockchain, Boeing 737 MAX, Brian Krebs, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computerized trading, coronavirus, corporate personhood, COVID-19, cryptocurrency, dark pattern, deepfake, defense in depth, disinformation, Donald Trump, Double Irish / Dutch Sandwich, driverless car, Edward Thorp, Elon Musk, fake news, financial innovation, Financial Instability Hypothesis, first-past-the-post, Flash crash, full employment, gig economy, global pandemic, Goodhart's law, GPT-3, Greensill Capital, high net worth, Hyman Minsky, income inequality, independent contractor, index fund, information security, intangible asset, Internet of things, Isaac Newton, Jeff Bezos, job automation, late capitalism, lockdown, Lyft, Mark Zuckerberg, money market fund, moral hazard, move fast and break things, Nate Silver, offshore financial centre, OpenAI, payday loans, Peter Thiel, precautionary principle, Ralph Nader, recommendation engine, ride hailing / ride sharing, self-driving car, sentiment analysis, Skype, smart cities, SoftBank, supply chain finance, supply-chain attack, surveillance capitalism, systems thinking, TaskRabbit, technological determinism, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, TikTok, too big to fail, Turing test, Uber and Lyft, uber lyft, ubercab, UNCLOS, union organizing, web application, WeWork, When a measure becomes a target, WikiLeaks, zero day

COMPUTERS AND AI ARE ACCELERATING SOCIETAL HACKING 224computers scale rote tasks: Karlheinz Meier (31 May 2017), “The brain as computer: Bad at math, good at everything else,” IEEE Spectrum, https://spectrum.ieee.org/the-brain-as-computer-bad-at-math-good-at-everything-else. 225Donotpay.com automates the process: Samuel Gibbs (28 Jun 2016), “Chatbot lawyer overturns 160,000 parking tickets in London and New York,” Guardian, https://www.theguardian.com/technology/2016/jun/28/chatbot-ai-lawyer-donotpay-parking-tickets-london-new-york. 225Automated A/B testing: Amy Gallo (28 Jun 2017), “A refresher on A/B testing,” Harvard Business Review, https://hbr.org/2017/06/a-refresher-on-ab-testing. 225they have the potential to overwhelm: California has a law requiring bots to identify themselves. Renee DiResta (24 Jul 2019), “A new law makes bots identify themselves—that’s the problem,” Wired, https://www.wired.com/story/law-makes-bots-identify-themselves. 226“flash crashes” of the stock market: Laim Vaughan (2020), Flash Crash: A Trading Savant, a Global Manhunt, and the Most Mysterious Market Crash in History, Doubleday. 226these systems are vulnerable to hacking: Shafi Goldwasser et al. (14 Apr 2022), “Planting undetectable backdoors in machine learning models,” arXiv, https://arxiv.org/abs/2204.06974. 56.

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In the future, AIs might recommend politicians for a wealthy political donor to support. They might decide who is eligible to vote. They might translate desired social outcomes into tax policies or tweak the details of entitlement programs. Hacks of these increasingly critical systems will become more damaging. (We’ve seen early examples of this with “flash crashes” of the stock market.) And for the most part, we have little insight into how the systems are designed, made, or used. Finally, the sophistication of AIs means that they will increasingly replace humans, since computers can often execute more complex and unanticipated strategies than humans can.

The Death of Money: The Coming Collapse of the International Monetary System by James Rickards

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Alan Greenspan, Asian financial crisis, asset allocation, Ayatollah Khomeini, bank run, banking crisis, Bear Stearns, Ben Bernanke: helicopter money, bitcoin, Black Monday: stock market crash in 1987, Black Swan, Boeing 747, 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, Dr. Strangelove, Edward Snowden, eurozone crisis, fiat currency, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, G4S, George Akerlof, global macro, global reserve currency, global supply chain, Goodhart's law, Growth in a Time of Debt, guns versus butter model, Herman Kahn, high-speed rail, income inequality, inflation targeting, information asymmetry, invisible hand, jitney, John Meriwether, junk bonds, Kenneth Rogoff, labor-force participation, Lao Tzu, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, market design, megaproject, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, mutually assured destruction, Nixon triggered the end of the Bretton Woods system, obamacare, offshore financial centre, oil shale / tar sands, open economy, operational security, plutocrats, Ponzi scheme, power law, price stability, public intellectual, 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, Solyndra, 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

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.

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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).

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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.

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, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business process, buy and hold, continuous integration, corporate governance, discrete time, distributed generation, fear index, financial engineering, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, machine readable, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, power law, principal–agent problem, random walk, risk free rate, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process

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.

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. © 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.

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., 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

"World Economic Forum" Davos, 23andMe, 4chan, A Declaration of the Independence of Cyberspace, Aaron Swartz, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Big Tech, Brian Krebs, California gold rush, Californian Ideology, call centre, cloud computing, cognitive dissonance, commoditize, company town, context collapse, correlation does not imply causation, Credit Default Swap, crowdsourcing, data science, deep learning, digital capitalism, disinformation, don't be evil, driverless car, drone strike, Edward Snowden, Evgeny Morozov, fake it until you make it, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, Higgs boson, hive mind, Ian Bogost, income inequality, independent contractor, informal economy, information retrieval, Internet of things, Jacob Silverman, Jaron Lanier, jimmy wales, John Perry Barlow, Kevin Kelly, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, Larry Ellison, late capitalism, Laura Poitras, license plate recognition, life extension, lifelogging, lock screen, Lyft, machine readable, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, off-the-grid, optical character recognition, payday loans, Peter Thiel, planned obsolescence, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, real-name policy, recommendation engine, rent control, rent stabilization, RFID, ride hailing / ride sharing, Salesforce, self-driving car, sentiment analysis, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Snapchat, social bookmarking, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, systems thinking, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, 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, yottabyte, you are the product, Zipcar

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.

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(Free Art and Technology), 359–62 Favstar, 196 FBI, 41, 130 feedback loop for journalism on social media, 97 feelings and emoticons, 30, 31 emotional states, 304–5 fame-related suffering, 71, 72–74, 75–82 mood graph, 41–43 read by camera in smartphone or tablet, 41–42 sharing negative sentiments, 24, 31, 203–4, 305 sharing positive sentiments, 24–26, 27, 203–4, 305 tagging, 30–31 tracking individuals by emotional states, 304–5 See also sentiment analysis; shaming Filter Bubble, The (Pariser), 122 financial crisis of 2008, 212 financial services companies, 309, 324 Fitbit, 139 fitness monitors, 305–6, 315 Five Eyes, 130 Fiverr, 89, 89n flâneur fantasy, 267–70 flash crashes, 39–40 followers fake followers, 38, 85–87, 88–89 importance of, 87–88, 198 Kutcher and CNN race, 146 metrics, 53 Forbes magazine, 107 Fort Hood shooting, 116 4chan, 74, 162 Foursquare, 140–41, 358 fractional work overview, 227–28, 236–37 college graduate’s experience, 222–26, 245–48 content moderators, 230–31, 244 sorting through queries on Twitter, 229–30 TaskRabbit, 222–26, 236–37, 242, 245 France, 267, 268 Free Art and Technology (F.A.T.), 359–62 frictionless sharing, 12–13, 60, 151, 268–69, 293 fusion centers, 367, 368 “Future of the Future, The” (Ballard), 21 Game of Thrones, 246 Gartner study of rating sites, 189 Gawker, 75, 96, 111 Gehl, Robert, 24 Genesys software, 196 Geo Listening, 133 Gigwalk, 232 Gingrich, Newt, 87 Girls Around Me app, 140–41 Gleick, James, 110 Glendale school district, California, 133 Glitchr, 353–54 Global Associated News, 348 global communication, 3–4 global communication, instantaneous, 5 globalization and privacy, 285 Gmail, 156, 359 Gnip, 37 “Going Public” (F.A.T.), 360 Goldberg, Bryan, 128 Golumbia, David, xiv Google overview, 274 content moderators, 230 data uses, 13, 300–301, 302, 303 fake e-mails, 92 fines for Street View, 295 goal of organizing the world’s information, 5, 18, 323–24 pay-per-gaze advertising patent, 302 and photographs, 309–10 privacy policy across products, 18, 311–12 staff, 5 unique visitors to, 96 and Waze, 140 Google tools Chrome Web browser, 262, 329–30 Gmail, 156, 359 Google Alerts, 214 Google Glass, 41, 143, 191, 302 Google Maps, 302–3 Google Now, 6–7, 300–301 Google Play, 311–12 Google Search, 198, 201, 212–13 Google+ social network, 13–16 reCAPTCHA, 260 ScreenWise, 329–30 Search, plus Your World, 14–15 Shared Endorsements, 33 YouTube, 13, 15, 70–71, 84, 361 GPS, 131–32, 140, 141 Great Recession, 220–27 Gregory, Alice, 118–19 Gregory Brothers, 71 Grosser, Ben, 358–59 hacks and hacking of AP Twitter account, 39 Facebook hackers, 166n, 354–55 by Five Eyes governments, 130 by Glitchr, 353–54 Uber customer rating system, 187–88 Happier social media site, 27 “Harlem Shake” video, 88 Hartzog, Woodrow, 216 Harvard Business School, 183, 189 Harvey, Adam, 356 hashtags, 94–95 hate speech, exposing, 170–72 Hayden, Michael, 144–45 Hayes, Danny, 105–6 headlines, 123–24, 125, 127 health insurance rate and biometric or genomic testing, 282 Hell Is Other People antisocial app, 358 Henderson the Rain King (Bellow), 59 Herrman, John, 110–11 Herzog, Werner, 184–85 hierarchies in social networks, 53–55 Hilton, Paris, 67 Homeland Security Department, 139 Honestly.com, 190 Horning, Rob, 48, 149 hotels, 181–84, 240 Houston Astros, 107 Huffington, Arianna, 179 Huffington Post, 115, 179 human beings as cells of a single organism, 12, 376n commoditizing ourselves, 60–61 confessionalism or over-sharing, 22–23, 23n continuous partial attention, 51–52 data dread, 50 data trail, viii, ix–x effect of social web, 22–23 as element in semiautomated system of communication and data, 42–43 and Like response, 24–26 prismatic identity, 163 range of behaviors, 177–78 as rentable commodities, 234–35 unique skills as improvement on algorithms, 228, 229–30 urge to share, 47–49, 61–62 videos of daily activities for nightly playback, 21 See also digital serfdom; feelings; human nature human-computation system on Twitter, 89–91 human genomics marketplace, 328 human nature desire to feel famous, 145–46 dream of rising above the crowd, 257 and “fail” memes, 69–70 informational addiction, 110 narcissism, 119–20 and privacy settings, 310, 311 and surveillance, 134–36 tribalism, 63 See also informational appetite hyperdata, 228 IBM’s Social Sentiment Index, 38–39 Identifier for Advertisers, Apple’s, 99 identity overview, 155 in college, 155–56 companies’ capability to define you, 308 Facebook as determinant of your worthiness, 308–9 growing up with an online identity, 158 Internet as arena for, 163–64 log-ins, 160, 165–66, 182 merging with others to be one voice, 8–9 and online persona, 344–45, 346 ownership of, 256–57, 273–74, 275–77, 311, 360 prismatic, 163 retaining as multifaceted, 164–66 static nature of Facebook page, 156 and surveillance, 156–57, 280 See also authentic identity identity obfuscation blurring faces in photographs, 357 Crypstagram, 358 with Glamouflage T-shirts, 356–57 with makeup, hair styling, and other tools, 356 with prosthetic masks, 357 Vaughan’s comic book on, 355–56 identity-theft services, 215 Illich, Ivan, 270–71 Image Atlas, 362 immaterial labor.

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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

Alan Greenspan, Andrei Shleifer, asset allocation, automated trading system, backtesting, bank run, banking crisis, Bear Stearns, Bernie Madoff, buy and hold, capital controls, correlation does not imply causation, Credit Default Swap, currency risk, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, high net worth, High speed trading, index fund, inflation targeting, junk bonds, low interest rates, 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

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.

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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

"there is no alternative" (TINA), Affordable Care Act / Obamacare, Alan Greenspan, asset-backed security, bank run, banking crisis, battle of ideas, Bear Stearns, Ben Bernanke: helicopter money, Berlin Wall, Bretton Woods, Brexit referendum, 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, cross-border payments, currency peg, currency risk, 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, Future Shock, German hyperinflation, global reserve currency, income inequality, inflation targeting, information asymmetry, Irish property bubble, Jean Tirole, Kenneth Rogoff, Les Trente Glorieuses, low interest rates, Martin Wolf, mittelstand, Money creation, 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, Phillips curve, Post-Keynesian economics, price stability, principal–agent problem, quantitative easing, race to the bottom, random walk, regulatory arbitrage, rent-seeking, reserve currency, risk free rate, road to serfdom, secular stagnation, short selling, Silicon Valley, South China Sea, special drawing rights, tail risk, the payments system, too big to fail, Tyler Cowen, union organizing, unorthodox policies, Washington Consensus, WikiLeaks, yield curve

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.

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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.

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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).

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah

"World Economic Forum" Davos, accounting loophole / creative accounting, Ada Lovelace, Adam Curtis, Airbnb, Alan Greenspan, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, behavioural economics, Ben Bernanke: helicopter money, bitcoin, Bletchley Park, blockchain, Bretton Woods, Brexit referendum, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, crowdsourcing, cryptocurrency, data science, David Graeber, deep learning, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, Glass-Steagall Act, Higgs boson, 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, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, Large Hadron Collider, Lewis Mumford, liquidity trap, London Whale, low interest rates, low skilled workers, M-Pesa, machine readable, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, Michael Milken, MITM: man-in-the-middle, Money creation, 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, power law, 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, robo advisor, Satoshi Nakamoto, Satyajit Das, Savings and loan crisis, savings glut, seigniorage, seminal paper, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, 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, Vitalik Buterin, 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.

Moneyland: Why Thieves and Crooks Now Rule the World and How to Take It Back by Oliver Bullough

Alan Greenspan, banking crisis, Bernie Madoff, bitcoin, blood diamond, Bretton Woods, Brexit referendum, BRICs, British Empire, capital controls, central bank independence, corporate governance, cryptocurrency, cuban missile crisis, dark matter, diversification, Donald Trump, energy security, failed state, financial engineering, Flash crash, Francis Fukuyama: the end of history, full employment, Global Witness, high net worth, if you see hoof prints, think horses—not zebras, income inequality, joint-stock company, land bank, liberal capitalism, liberal world order, mass immigration, medical malpractice, Navinder Sarao, offshore financial centre, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, rent-seeking, Richard Feynman, risk tolerance, Sloane Ranger, sovereign wealth fund, Suez crisis 1956, WikiLeaks

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.

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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

artificial general intelligence, brain emulation, effective altruism, Flash crash, friendly AI, machine translation, Nick Bostrom, shareholder value, Turing test

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.

Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

"Margaret Hamilton" Apollo, Abraham Maslow, Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business logic, business process, Checklist Manifesto, cloud computing, cognitive load, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, exponential backoff, fail fast, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, if you see hoof prints, think horses—not zebras, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, machine readable, meta-analysis, microservices, minimum viable product, MVC pattern, no silver bullet, OSI model, performance metric, platform as a service, proprietary trading, reproducible builds, revision control, risk tolerance, side project, six sigma, the long tail, the scientific method, Toyota Production System, trickle-down economics, warehouse automation, 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.

…

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, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, 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, workplace surveillance , zero-sum game, Zipcar

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

Alan Greenspan, 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, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, bitcoin, Bob Litterman, Bonfire of the Vanities, bonus culture, break the buck, Brexit referendum, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, carbon tax, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, confounding variable, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, democratizing finance, Diane Coyle, diversification, diversified portfolio, do well by doing good, double helix, easy for humans, difficult for computers, equity risk premium, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, information security, interest rate derivative, invention of the telegraph, Isaac Newton, it's over 9,000, James Watt: steam engine, Jeff Hawkins, Jim Simons, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, language acquisition, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, megaproject, merger arbitrage, meta-analysis, Milgram experiment, mirror neurons, money market fund, moral hazard, Myron Scholes, Neil Armstrong, Nick Leeson, old-boy network, One Laptop per Child (OLPC), out of africa, p-value, PalmPilot, paper trading, passive investing, Paul Lévy, Paul Samuelson, Paul Volcker talking about ATMs, Phillips curve, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, proprietary trading, public intellectual, 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 Solow, Sam Peltzman, Savings and loan crisis, seminal paper, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, subprime mortgage crisis, survivorship bias, systematic 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, uptick rule, 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).

…

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.” Figure 10.8. Financial Moore’s Law: raw and natural logarithm of average daily trading volume by year of exchange-listed options and futures from the Options Clearing Corporation, 1973–2014, and linear regression estimate of geometric growth rate, which implies a doubling every (log 2)/0.14 = 4.95 years.

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See also “Quant Meltdown” (August 2007) financial engineering, 212, 415 financial sector, 330–332 finches, 226–227, 240, 244 FINRA (Financial Industry Regulatory Authority), 360 financial technology, 248, 361, 399, 405 first-order false belief, 111 Fisher, Larry, 23 Fisher, Ronald Aylmer, 216–217 fixed-income arbitrage, 243, 293 fixed-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 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.

Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, air gap, Albert Michelson, AlphaGo, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Swan, Bletchley Park, blockchain, Burning Man, business logic, buy low sell high, Claude Shannon: information theory, cloud computing, commons-based peer production, Computer Numeric Control, conceptual framework, CRISPR, crowdsourcing, cryptocurrency, data acquisition, deep learning, DeepMind, Demis Hassabis, digital rights, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, Ford Model T, frictionless, game design, Gerolamo Cardano, informal economy, information security, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, move 37, Nate Silver, Network effects, neurotypical, Oculus Rift, off-the-grid, One Laptop per Child (OLPC), PalmPilot, pattern recognition, peer-to-peer, pirate software, power law, pre–internet, prisoner's dilemma, Productivity paradox, quantum cryptography, 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, synthetic biology, technological singularity, technoutopianism, TED Talk, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Two Sigma, universal basic income, unpaid internship, uranium enrichment, urban planning, warehouse automation, warehouse robotics, Wayback Machine, WikiLeaks, Yochai Benkler

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

"World Economic Forum" Davos, 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, electricity market, eurozone crisis, Fall of the Berlin Wall, financial engineering, fixed income, Flash crash, illegal immigration, labour market flexibility, labour mobility, light touch regulation, low interest rates, market fundamentalism, Money creation, 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

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?

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., 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.

The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser

A Declaration of the Independence of Cyberspace, A Pattern Language, adjacent possible, Amazon Web Services, An Inconvenient Truth, Apple Newton, 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, Gabriella Coleman, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, John Perry Barlow, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Netflix Prize, new economy, PageRank, Paradox of Choice, Patri Friedman, paypal mafia, Peter Thiel, power law, recommendation engine, RFID, Robert Metcalfe, sentiment analysis, shareholder value, Sheryl Sandberg, 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, Ted Nordhaus, The future is already here, the scientific method, urban planning, We are as Gods, Whole Earth Catalog, WikiLeaks, Y Combinator, Yochai Benkler

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?

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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?

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, Alan Greenspan, Asian financial crisis, asset-backed security, backtesting, bank run, Bear Stearns, 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, currency risk, debt deflation, disinformation, distributed ledger, diversification, financial engineering, financial intermediation, Flash crash, global reserve currency, implied volatility, income inequality, inflation targeting, junk bonds, labor-force participation, Long Term Capital Management, low interest rates, Lyft, margin call, market bubble, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, negative equity, Network effects, Ponzi scheme, proprietary trading, public intellectual, purchasing power parity, quantitative easing, random walk, rent-seeking, reserve currency, rising living standards, risk free rate, risk/return, sharing economy, short selling, short squeeze, sovereign wealth fund, stock buybacks, tail risk, TikTok, 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.

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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.

Fed Up!: Success, Excess and Crisis Through the Eyes of a Hedge Fund Macro Trader by Colin Lancaster

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, Alan Greenspan, always be closing, asset-backed security, beat the dealer, Ben Bernanke: helicopter money, Bernie Sanders, Big Tech, Black Monday: stock market crash in 1987, bond market vigilante , Bonfire of the Vanities, Boris Johnson, Bretton Woods, business cycle, buy the rumour, sell the news, Carmen Reinhart, Chuck Templeton: OpenTable:, collateralized debt obligation, coronavirus, COVID-19, creative destruction, credit crunch, currency manipulation / currency intervention, deal flow, Donald Trump, Edward Thorp, family office, fear index, fiat currency, fixed income, Flash crash, George Floyd, global macro, global pandemic, global supply chain, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, Growth in a Time of Debt, housing crisis, index arbitrage, inverted yield curve, Jeff Bezos, Jim Simons, junk bonds, Kenneth Rogoff, liquidity trap, lockdown, Long Term Capital Management, low interest rates, low skilled workers, margin call, market bubble, Masayoshi Son, Michael Milken, Mikhail Gorbachev, Minsky moment, Modern Monetary Theory, moral hazard, National Debt Clock, Nixon triggered the end of the Bretton Woods system, Northern Rock, oil shock, pets.com, Ponzi scheme, price stability, proprietary trading, quantitative easing, Reminiscences of a Stock Operator, reserve currency, Ronald Reagan, Ronald Reagan: Tear down this wall, Sharpe ratio, short selling, short squeeze, social distancing, SoftBank, statistical arbitrage, stock buybacks, The Great Moderation, TikTok, too big to fail, trickle-down economics, two and twenty, value at risk, Vision Fund, WeWork, yield curve, zero-sum game

* The environment of 2019 has been tough. Up to this point in the year, it has been a big yo-yo. Stocks are up small, year-to-date, but over a twelve-month period, they’ve flatlined. Bonds rally as the data weakens and then sputter. Stocks go up on trade optimism and then fall. And as usual, we are always watching out for the flash crashes. They appear with no warning, like black ice on a frozen highway. You’re long $750,000 per basis point in some EM curve steepener, and all of a sudden, it’s moving massively against you with zero liquidity.2 The car hits the ice, and you end up in a wreck. At the end of the day, we macro traders have unique jobs.

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Oil is indicated to open below $32 a barrel tomorrow, a fucking 30% move down from where it closed on Friday. It’s full-on ape-shit bad in the markets. People are losing their fucking shirts. Everything is blowing out. US stock futures are limit down, high-yield bonds are gapping wider, ten-year Treasury yields dipped below 0.5%. We had flash crashes in currencies overnight as the oil market crash sent shockwaves through the FX markets. CAD, MXN, RUB, NOK, and ZAR are all getting killed. Adding a bit of credit risk to the overall picture is the worst thing possible. Bankruptcy filings are inevitable. XOM, CVX, and BP are dripping like rocks.

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, asset allocation, Basel III, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamond, 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, fear index, financial engineering, financial innovation, Flash crash, forward guidance, Garrett Hardin, Gini coefficient, Glass-Steagall Act, global reserve currency, high net worth, High speed trading, hindsight bias, hype cycle, income inequality, inflation targeting, interest rate swap, inverted yield curve, Isaac Newton, Jaron Lanier, John Perry Barlow, joint-stock company, joint-stock limited liability company, junk bonds, Kodak vs Instagram, Kondratiev cycle, Large Hadron Collider, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, low interest rates, 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, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Ponzi scheme, precautionary principle, proprietary trading, 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, Tragedy of the Commons, trickle-down economics, two and twenty, Two Sigma, Tyler Cowen, Washington Consensus, wealth creators, working poor, yield curve

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 bias, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, behavioural economics, Berlin Wall, Big Tech, Bill Duvall, bitcoin, Boeing 747, Charles Babbage, cognitive load, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, David Sedaris, delayed gratification, dematerialisation, diversification, Donald Knuth, Donald Shoup, double helix, Dutch auction, Elon Musk, exponential backoff, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, fulfillment center, Garrett Hardin, Geoffrey Hinton, George Akerlof, global supply chain, Google Chrome, heat death of the universe, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, Johannes Kepler, John Nash: game theory, John von Neumann, Kickstarter, knapsack problem, Lao Tzu, Leonard Kleinrock, level 1 cache, linear programming, martingale, multi-armed bandit, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, power law, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, Sam Altman, scientific management, 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, Tragedy of the Commons, 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.

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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.

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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

Airbnb, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Alvin Roth, assortative mating, behavioural economics, 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, data science, deep learning, deliberate practice, discrete time, distributed ledger, Easter island, en.wikipedia.org, Estimating the Reproducibility of Psychological Science, Everything should be made as simple as possible, experimental economics, first-price auction, Flash crash, Ford Model T, Geoffrey West, Santa Fe Institute, germ theory of disease, Gini coefficient, Higgs boson, 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, money market fund, multi-armed bandit, Nash equilibrium, natural language processing, Network effects, opioid epidemic / opioid crisis, p-value, Pareto efficiency, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, Phillips curve, power law, pre–internet, prisoner's dilemma, race to the bottom, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Solow, school choice, scientific management, sealed-bid auction, second-price auction, selection bias, six sigma, social graph, spectrum auction, statistical model, Stephen Hawking, Supply of New York City Cabdrivers, systems thinking, tacit knowledge, The Bell Curve by Richard Herrnstein and Charles Murray, The Great Moderation, the long tail, The Rise and Fall of American Growth, the rule of 72, the scientific method, The Spirit Level, the strength of weak ties, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, urban sprawl, value at risk, web application, winner-take-all economy, zero-sum game

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.

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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

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, 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 Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, 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, Rutger Bregman, Salesforce, 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, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, 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, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, 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

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.

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., 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, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, 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, stable marriage problem, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

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?

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., 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.

Easy Money: Cryptocurrency, Casino Capitalism, and the Golden Age of Fraud by Ben McKenzie, Jacob Silverman

algorithmic trading, asset allocation, bank run, barriers to entry, Ben McKenzie, Bernie Madoff, Big Tech, bitcoin, Bitcoin "FTX", blockchain, capital controls, citizen journalism, cognitive dissonance, collateralized debt obligation, COVID-19, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, cryptocurrency, data science, distributed ledger, Dogecoin, Donald Trump, effective altruism, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, experimental economics, financial deregulation, financial engineering, financial innovation, Flash crash, Glass-Steagall Act, high net worth, housing crisis, information asymmetry, initial coin offering, Jacob Silverman, Jane Street, low interest rates, Lyft, margin call, meme stock, money market fund, money: store of value / unit of account / medium of exchange, Network effects, offshore financial centre, operational security, payday loans, Peter Thiel, Ponzi scheme, Potemkin village, prediction markets, proprietary trading, pushing on a string, QR code, quantitative easing, race to the bottom, ransomware, regulatory arbitrage, reserve currency, risk tolerance, Robert Shiller, Robinhood: mobile stock trading app, Ross Ulbricht, Sam Bankman-Fried, Satoshi Nakamoto, Saturday Night Live, short selling, short squeeze, Silicon Valley, Skype, smart contracts, Steve Bannon, systems thinking, TikTok, too big to fail, transaction costs, tulip mania, uber lyft, underbanked, vertical integration, zero-sum game

Experienced in online trading, Kim switched to the two other internet connections he had installed as backups in his house. None of them got the app to work. “So I’m just going there, going crazy, going click click click, you know, trying to close out of that position, to lock in the profits,” he told us. “And you know, I jump on Twitter. Other people are having similar issues.” Flash crashes in crypto markets tend to be accompanied by technical snafus or unexplained outages, including an inability to withdraw funds. On September 7, 2021, for example, when El Salvador introduced Bitcoin as a form of legal tender, a market-wide slide led to a number of exchanges reporting transaction delays and other problems.

…

Some customers found their accounts put automatically in what Celsius called HODL mode, preventing them from cashing out. In a practical sense, this was a Ponzi artist trying to prevent his victims from withdrawing their money, which would make the entire scheme collapse. During a period of market volatility in September 2021, Mashinsky tweeted, “Don’t let these flash crash low volume swings sway you. Others may need cash and so sell their coins at a discount, you are going after financial freedom so #HODL on. ETH is a great buy at these levels.” Besides outsized rewards and financial freedom, Mashinsky promised his customers “community,” that empty, horribly overused word that seems to be deployed even more often after a company tells its customers that they can’t return their money.

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, AlphaGo, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, DeepMind, European colonialism, Flash crash, Ford Model T, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, low interest rates, Nick Bostrom, pattern recognition, peak-end rule, Ponzi scheme, self-driving car, Suez canal 1869, telemarketer, The future is already here, 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

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

Apollo 11, Ayatollah Khomeini, Boeing 747, British Empire, creative destruction, data acquisition, data science, Dava Sobel, different worldview, digital map, Easter island, Edmond Halley, Eratosthenes, experimental subject, Eyjafjallajökull, Flash crash, friendly fire, GPS: selective availability, Hedy Lamarr / George Antheil, Ian Bogost, Internet of things, Isaac Newton, John Harrison: Longitude, Kevin Kelly, Kwajalein Atoll, 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, Skinner box, skunkworks, smart grid, systems thinking, the map is not the territory, vertical integration

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.

…

., 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.

The Price of Time: The Real Story of Interest by Edward Chancellor

"World Economic Forum" Davos, 3D printing, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, asset allocation, asset-backed security, assortative mating, autonomous vehicles, balance sheet recession, bank run, banking crisis, barriers to entry, Basel III, Bear Stearns, Ben Bernanke: helicopter money, Bernie Sanders, Big Tech, bitcoin, blockchain, bond market vigilante , bonus culture, book value, Bretton Woods, BRICs, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, cashless society, cloud computing, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, commodity super cycle, computer age, coronavirus, corporate governance, COVID-19, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cryptocurrency, currency peg, currency risk, David Graeber, debt deflation, deglobalization, delayed gratification, Deng Xiaoping, Detroit bankruptcy, distributed ledger, diversified portfolio, Dogecoin, Donald Trump, double entry bookkeeping, Elon Musk, equity risk premium, Ethereum, ethereum blockchain, eurozone crisis, everywhere but in the productivity statistics, Extinction Rebellion, fiat currency, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, full employment, gig economy, Gini coefficient, Glass-Steagall Act, global reserve currency, global supply chain, Goodhart's law, Great Leap Forward, green new deal, Greenspan put, high net worth, high-speed rail, housing crisis, Hyman Minsky, implied volatility, income inequality, income per capita, inflation targeting, initial coin offering, intangible asset, Internet of things, inventory management, invisible hand, Japanese asset price bubble, Jean Tirole, Jeff Bezos, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Rogoff, land bank, large denomination, Les Trente Glorieuses, liquidity trap, lockdown, Long Term Capital Management, low interest rates, Lyft, manufacturing employment, margin call, Mark Spitznagel, market bubble, market clearing, market fundamentalism, Martin Wolf, mega-rich, megaproject, meme stock, Michael Milken, Minsky moment, Modern Monetary Theory, Mohammed Bouazizi, Money creation, money market fund, moral hazard, mortgage debt, negative equity, new economy, Northern Rock, offshore financial centre, operational security, Panopticon Jeremy Bentham, Paul Samuelson, payday loans, peer-to-peer lending, pensions crisis, Peter Thiel, Philip Mirowski, plutocrats, Ponzi scheme, price mechanism, price stability, quantitative easing, railway mania, reality distortion field, regulatory arbitrage, rent-seeking, reserve currency, ride hailing / ride sharing, risk free rate, risk tolerance, risk/return, road to serfdom, Robert Gordon, Robinhood: mobile stock trading app, Satoshi Nakamoto, Satyajit Das, Savings and loan crisis, savings glut, Second Machine Age, secular stagnation, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, stock buybacks, subprime mortgage crisis, Suez canal 1869, tech billionaire, The Great Moderation, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thorstein Veblen, Tim Haywood, time value of money, too big to fail, total factor productivity, trickle-down economics, tulip mania, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, Walter Mischel, WeWork, When a measure becomes a target, yield curve

The easy money regime threatened to end in what he called an ‘epoch-defining seismic rupture’, heralding the end of globalization, the return of capital controls and rising inflation.20 A few months after Lehman’s bankruptcy, Borio was warning about the dangers of keeping extreme monetary policies in place for too long.21 Over the following years, he stood apart from other monetary economists in alerting the world to the unintended consequences of the zero interest rate policy (ZIRP), the negative interest rate policy (NIRP) and other monetary innovations. ‘The highly abnormal is becoming uncomfortably normal,’ Borio bemoaned after the October 2014 bond market ‘flash crash’. As one year followed another, unconventional monetary policies became more unconventional. Abnormality became the new normal. Monetary policy is path dependent, said Borio. Once the central bankers have taken a wrong turning, the financial system and economy are driven off course: economic growth falters, financial imbalances accumulate, and capital is misallocated.

…

Extreme measures are adopted: negative interest rates, limitless amounts of quantitative easing and, in response to British’s voters’ wishes to leave the European Union, even a ‘Project Fear’. Too much is at stake to let the bubble burst. Yet unwelcome reality occasionally intrudes in the form of financial crises (of the Dotcom, subprime and Eurozone variety), ‘flash crashes’ and abnormal economic outcomes – plunging productivity, rising inequality and extreme profitability. The conventional explanations that are provided – secular stagnation, demographic headwinds and technological change – are unconvincing. Like Truman, we feel that things are not right. In an unscripted moment in the show, Truman falls in love with a girl who tries to warn him that everything is fake.

…

., 181 Egypt, 77, 78, 255, 262 Einstein, Albert, 8 Elizabeth II, Queen, 114 Ellington Capital Management, 223 Emden, Paul, 80* emerging markets: Brazilian crash (2012–13), 257–8; BRICs, 254–5, 257–8; capital controls return after 2008, 262, 291; capital flight from (starting 2015), 262, 285–6; demand for industrial commodities, 128; epic corruption scandals, 258; and extended supply chains, 261; flooding across South East Asia (2010), 255; ‘Fragile Five’, 258–9; growth of foreign exchange reserves, 252, 253, 254–5, 256; impact of ultra-low interest rates on, xxiii, 253–60, 262–3; international carry Trade, 137, 237–8; overheating during 2010, 255, 256; post-crisis capital flows into, xxiii, 253–9, 262–3; and recent phase of globalization, 260–61; recovery from 2008 crisis, 124; and savings glut hypothesis, 129, 268–9; ‘second phase of global liquidity’ after 2008 crisis, 253–9, 262–3; and taper tantrum (June 2013), xxiii, 137, 239, 256–7, 259, 263; Turkish debt, 258–60; vulnerability to US monetary policy, 137, 262–3, 267–8 see also China employment/labour markets, xx, 151–2, 240, 260–61, 260*, 296; after 2008 crisis, 210, 211; new insecurity, 211, 298 Erdogan, Recep Tyyip, 259 European Central Bank (ECB), 144, 145, 147, 239, 240, 293; inflation targeting, 119, 120, 122–3; and quantitative easing, 146, 241, 242; sets negative rate, 147, 192–3, 244, 299 European Union, 187, 241, 262 Eurozone, 124, 150–51, 226; and political sovereignty, 293, 293†; sovereign debt crisis (from 2010), 144–8, 226, 238, 239, 241, 273, 293 Evans, David Morier, 73 Evelyn, John, 36, 45 Evergrande (Chinese developer), 279, 288, 310 executive compensation schemes, 152, 162, 163–4, 170, 204, 206, 207 Extinction Rebellion, 201 ExxonMobil, 166 Fang’s Money House, Wenzhou, 281–2 farming: agricultural cycle, 11, 14, 88; and ‘Bank of John Deere’, 167; barley loans in ancient Mesopotamia, 5–6, 6*, 7, 8, 10, 11, 14; bubbles in post-crisis decade, 173; in China, 283; and language of interest, 4–5; loans related to consumption, 6, 25; US deflation of 1890s, 99 Federal Reserve, US: asymmetrical approach to rates, 136–7; as carry trader, 222; cognitive dissonance in, 118–19; Federal Reserve Act (1914), 83; ‘forgotten depression’ (1921), 84, 86, 100, 143; forward guidance policy, 131*, 133, 238, 239, 240, 241; and Gold Exchange Standard, 85, 87, 90*; the ‘Greenspan put’, 111, 186; impact on foreign countries, 137, 239, 240–41, 255–6, 259, 262–3, 267–8, 285; inflation targeting, 119, 120, 241; Long Island meeting (1927), 82–3, 88, 92; mandates of, 240, 262; and March 2020 crash, 305–6; Objectives of Monetary Policy (1937), 97; Open Market Committee (FOMC), 109, 112–13, 115†, 120, 164, 228, 238, 239, 240; Operation Twist (2011), 131*, 238; parallel with US Forest Service, 154–5; and post-Great War inflation, 84; as the ‘price of leverage’, xxi–xxii; quantitative easing by, 12*, 76, 131*, 137, 175, 215, 228, 236, 238, 239–40, 241; raised rates announcement (2015), 138, 239; reaches ‘zero lower bound’ (2008), 243–4; response to 1929 Crash, 98, 100, 101, 108; suggested as responsible for 2008 crisis, 116–17, 118–19, 155, 204, 226–7; TALF fund, 175; taper tantrum (June 2013), xxiii, 137, 239, 256–7, 259, 263; ultra-easy money after 2008 crisis, xxi, 60, 124, 131–8, 146, 149, 152–5, 181–3, 206–17, 221–4, 230, 235–41, 243–4, 262, 291–2; Paul Volcker runs, 108–9, 145; Janet Yellen runs, 120 see also Bernanke, Ben; Greenspan, Alan Feldstein, Martin, 119 Ferri, Giovanni, 277* Fetter, Frank, 30 Field, Alexander, The Great Leap Forward, 142–3 financial crisis (2008): accelerates financialization, 182–3; and complex debt securities, 116, 117–18, 231; ‘crunch porn’ on causes of, 114; economists who anticipated crisis, 113–14, 132; failure of unconventional monetary policies after, xxi, xxii, 43–4, 291–4, 298–9, 301–3; Fed’s monetary policy as suggested cause, 116–17, 118–19, 155, 204, 226–7; generational impact of, 211–12, 213; as ‘giant carry trade gone wrong’, 253–5; global causes of, 117–18; Icelandic recovery from, 301–2; and inequality, 204, 205–17, 299; interest at lowest level in five millennia during, xxi, 243–4, 247; Law’s System compared to, 49, 60–61; low/stable inflation at time of, 134, 135; monetary policy’s role in run-up downplayed, 115–16, 115*, 115†; and quoting of Bagehot, 76; recovery of lost industrial output after, 124; regulatory interpretation of, 114–15, 117; and return of the state, 292–5, 297, 298; return to ‘yield-chasing’ after, 221–6, 230–31, 233–4, 237–8; the rich as chief beneficiaries of, 206–10; savings glut hypothesis, 115–16, 117, 126, 128–9, 132, 191, 252, 268–9; ‘second phase of global liquidity’ after, 253–9, 262–3; unwinding of carry trades during, 221, 227; warnings from BIS economists before, 113–14, 131–4, 135–9 see also Great Recession financial derivatives market, 225–6 financial engineering: buybacks, 53, 152, 163–6, 167, 169, 170–71, 183, 224; crowding out of real economy by, 158–9, 160, 166–71, 182–3, 185, 237; ‘funding gap’ as impetus, 164, 176–7, 291; merger ‘tsunami’ after 2008 crisis, 160–63, 161*, 168–70, 237, 298; ‘promoter’s profit’ concept, 158–9, 160, 161, 164; and ‘shareholder value’, 163–6, 167, 170–71; Truman Show as allegory for bubble economy, 185–7; use of leverage, 111, 116, 149, 155, 158–71, 204, 207, 223, 237, 291; zaitech in Japan, 106, 182, 185 financial repression: in China, 264–5, 265*, 266–81, 268*, 283, 286–9, 292; and inequality, 287–8; McKinnon coins term, 264; political aspects, 265, 265*, 286–9, 292; returns to West after 2008 crisis, 291–3; after Second World War, 290–91, 302 financial sector: bond markets as ‘broken (2014), 227; complex securitizations, 116, 117–18, 221, 227, 231; decades-long bull market from early 1980s, 203–4; economics as fundamentally monetary, 132, 138–9; Edmunds’ ‘New World Wealth Machine’, 181–2; expansion in 1920s USA, 203; finance as leading growth, 266; financial mania of 1860s, 72–4, 75–6; fixed-income bonds, 68–9, 193, 219, 222, 225, 226; foreign securities/loans, 66, 77–8, 91; investment trusts appear (1880s), 79; liquidity traps, 114; mighty borrowers within, 202; profits bubble in post-crisis USA, 183, 183†, 185, 211; robber baron era in USA, 156–9, 203; stability as destabilizing, 82, 143, 233, 263, 285; stock market bubble in post-crisis decade, 175–7, 176*; trust companies in US, 83–4, 84*; US bond market ‘flash crash’ (2014), 138; and volatility, 153, 228–30, 233, 234, 254, 304, 305; volatility as asset class, 229–30, 229*, 233, 234, 304, 305; ‘Volmageddon’ (5 February 2018), 229–30, 234 see also banking and entries for individual institutions/events financial system, international: Asian crisis, 114, 252, 278; Basel banking rules, 232; Borio on ‘persistent expansionary bias’, 262–3; complex mortgage securities, 116, 117–18; crash (12 March 2020), 304–6; ‘excess elasticity’ of, 137; global financial imbalances, 137, 138; Louvre Accord (1987), 105–6; stock market crash (October 1987), 106, 110–11, 229 financialization, 162–71, 182–3, 185, 203–8, 237 Fink, Larry, 209, 246 Finley, Sir Moses, Economy and Society in Ancient Greece (1981), 18* fire-fighting services, 154–5 First World War, 84, 85 Fisher, Irving: and debt-deflation, 98–9, 100, 119, 280; first to refer to ‘real’ interest rate, 88–9, 219*; founds Stable Money League (1921), 87, 96; and Gesell’s rusting money, 243, 246; on interest, 29–30, 82, 189, 189*, 201; losses in 1929 crash, 94; monetarist view of 1929 Crash, 98–9, 100, 101, 108; ‘money illusion’ concept, 87*; on nature’s production, 4–5; on negative interest, 246; The Theory of Interest, xxiv, xxv, xxvi*, 16, 173 Fisher, Peter, 194 Fisher, Richard, 164 Fitzgerald, F.

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

3D printing, Ada Lovelace, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, Bletchley Park, blockchain, brain emulation, Buckminster Fuller, Charles Babbage, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, deep learning, DeepMind, dematerialisation, Demis Hassabis, discovery of the americas, disintermediation, don't be evil, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Geoffrey Hinton, Google Glasses, hedonic treadmill, hype cycle, 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, machine translation, Mahatma Gandhi, means of production, mutually assured destruction, Neil Armstrong, Nicholas Carr, Nick Bostrom, paperclip maximiser, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, radical life extension, Ray Kurzweil, Robert Solow, 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, TED Talk, The future is already here, 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 Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Adam Curtis, Affordable Care Act / Obamacare, Alan Greenspan, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Bear Stearns, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, business logic, 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, data science, Debian, digital rights, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, financial thriller, fixed income, Flash crash, folksonomy, full employment, Gabriella Coleman, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, Ian Bogost, informal economy, information asymmetry, information retrieval, information security, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Bogle, Julian Assange, Kevin Kelly, Kevin Roose, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, machine readable, Marc Andreessen, Mark Zuckerberg, Michael Milken, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, public intellectual, quantitative easing, race to the bottom, reality distortion field, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, Savings and loan crisis, 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, technological solutionism, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, vertical integration, WikiLeaks, Yochai Benkler, zero-sum game