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algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, credit crunch, Credit Default Swap, financial innovation, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative ﬁnance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K
., 194 arbitrage, 161-162 arbitrage opportunities, 70 Archipelago, 33 Arnuk, Sal, 11-25, 44-45, 61, 217 Atkins, Paul, 145-146 ATSs (Alternative Trading Systems), 93 regulation of, 139-144 Australia (film), 78 automated liquidity provision, 161 Automatic Trading Systems (ATSs), 93, 139-144 Aykroyd, Dan, 29 B Bachus, Spencer, 187 Baker, Jim, 130 BATS (Better Alternative Trading System) Exchange, 78 Bee, Samantha, 44 Berkeley, Alfred, 173 Biden, Joe, 47-49, 52-53 “Big Picture” blog (Ritholtz), 234 Birk, Roger, 116 Black Monday (October 19, 1987), 125-133, 179 Blair, Bruce, 160 Blankfein, Lloyd, 99 Blodgett, Henry, 189-190 Bloomberg, Michael, 100 Boesky, Ivan, 126 Boggs, Caleb, 48 Bookstaber, Richard, 157 Born, Brooksley, 99 Boston Stock Exchange, 33 BP oil spill (Deepwater Horizon), 67 Brady Commission, 128 Brady, Nicholas, 83, 128 broken trades after Flash Crash, 83, 225 brokerage houses, internal trades, 31-32 Brown, Alistair, 17 Brown, Gordon, 66 Budge, Hamer, 107 Buffett, Warren, 234 Bulgaria Confidential (newspaper), 42 Bush, George H.W., 102 Bush, George W., 50 busted trades after Flash Crash, 88 buttonwood trees, 168 C Cameron, David, 66 Canaday, Ed, 41 capital crisis of 1969-70, 105-111 Casey, William, 120 CBOT (Chicago Board of Trade), 28-30 Cembalest, Michael, 207-208 CFTC (Commodities Futures Trading Commission), 27 Flash Crash report, 213-227 immediate reaction to Flash Crash, 82 investigation of Flash Crash, 183, 187 consolidated tape delays, 202-204 quick fix rules after Flash Crash, 85-87, 90 CFTC-SEC Joint Advisory Committee Accenture testifying before, 85-90 investigation of Flash Crash, 91-95 Chicago Board of Trade (CBOT), 28-30 Chicago Mercantile Exchange (CME), 28-30 Chilton, Bart, 214 Christie, William, 139 circuit breaker rule, 188 circuit breakers, 63-64, 89 Citigroup, 166 Clinton, Bill, 53, 97, 100-102, 143 Clinton, Hillary, 100 Close Encounters of the Third Kind (film), 49 CME (Chicago Mercantile Exchange), 28-30 collocated servers, 17, 22, 34 collocation, origins of, 165-169 commission structure, fixed commissions, 118-120 commodities exchanges correlation with equities exchanges, 94 history in United States, 27-30 unification with equities exchanges, 36, 70 Commodities Futures Trading Commission.
See Ivandjiiski, Dan, 41-42 E E-Mini futures contracts in Flash Crash, 68-69, 215-219 ECNs (electronic communications networks), 94, 142 Edelman, Asher, 136 Engelberg, Jeff, 191 Equinix, 167 equities exchanges correlation with commodities exchanges, 94 Flash Crash, details of, 61-79 Regulation NMS changes to, 21, 31-37 unification with commodities exchanges, 36, 70 erosion of investor confidence, 207-210 ETFs (exchange-traded funds), 185 in Flash Crash, 76-77 mutual funds versus, 232 ethics issues in Flash Crash investigation, 193-198 Eurex, 30 Euronext N.V., 33 European commodities exchanges, modernization of, 29 event trading, 161 exchange-traded funds (ETFs), 185 in Flash Crash, 76-77 mutual funds versus, 232 exchanges collocation, origins of, 165-169 commodities exchanges, history in United States, 27-30 equities exchanges, Regulation NMS changes to, 21, 31-37 Flash Crash, details of, 61-79 individual stock circuit breakers, 89 integration, lack of, 128 intraday moves after Great Recession, 2-3 unification of commodities and equities exchanges, 36, 70 executing brokers, 32 exhaust, 33 F Facciponte, Joseph, 40 failed trades in capital crisis of 1969-70, 106-108 Federal Reserve, regulation of markets through, 129 FINRA (Financial Industry Regulatory Authority), 19, 41 Mary Schapiro’s leadership of, 97 Trillium Brokerage Services LLC, case against, 210-212 fixed commissions, 118-120 Flash Crash Accenture, affect on, 85-87, 90 details of, 6-8, 61-79 immediate Congressional reaction to, 81-84 investigation of, 91-95, 183-185, 189-191 consolidated tape delays, 199-205 SEC ethics issues, 193-198 precursors to, 1, 4-6 SEC and CFTC report on, 213-227 trades busted afterwards, 88 flash orders, 36, 42-43 banning, 47-59 FOIA (Freedom of Information Act), 113-114 Ford, Gerald, 120 Fox, Kevin N., 40 Frank, Barney, 55, 186 Fraud Enforcement and Recovery Act of 2009, 49 Freedom of Information Act (FOIA), 113-114 Freeman, John P., 197 front-running by hedge funds, 140 Futures Industry Association of America, 101 G Galbraith, John Kenneth, 58 Gastineau, Gary, 232 Geithner, Timothy, 81 Gensler, Gary, 86, 97-101, 216 Getco, 157, 196 Gillespie, Ed, 53 Giuliani, Rudolph, 126 Glassman, Cynthia, 146 Goldman Sachs, 39-41, 92, 99 Goldstein, Michael, 68, 186 Gorelick, Richard, 151-152, 155 Graham, Benjamin, 177 Grassley, Charles, 197 Great Britain, elections prior to Flash Crash, 66 Great Depression, volatility after, 179 Great Recession details of, 4 recovery from, 1, 4-6 Greece, debt of, 65-66 Greenspan, Alan, 98 Gulf of Mexico, BP oil spill (Deepwater Horizon), 67 H Hartzell, David, 230-231 Hathaway, Frank, 178 Hawaiian Holdings, Inc., 41 hedge funds, 159-160 front-running by, 140 Hensarling, Jeb, 187 high-frequency trading (HFT) accusation of market manipulation, 39-45 blamed by Congress for Flash Crash, 83-84 collocation, origins of, 165-169 Congressional pressure on SEC to reform, 54-59 eroded market confidence from, 207-210 explanation of, 149-164 Flash Crash consolidated tape delays, 199-205 details of, 61-79 investigation of, 91-92, 183-185, 189-191 in report, 221-227 role in, 7-8 SEC ethics issues, 193-198 origins of, 135-147 public relations efforts of, 150 Quants versus, 157-160 retail trades and, 32 statistics on, 156 strategies employed by, 11-25, 34-37, 161-162 Trillium Brokerage Services LLC, case against, 210-212 volatility reasons for, 175-182 rhythm of, 176-177 history of commodities exchanges in United States, 27-30 Hoffman, Stuart, 234 hot-potato trading, 22 Houtkin, Harvey Ira, 133-139, 161 Hu, Henry, 44, 58, 98 Hunsader, Eric Scott, 199-205, 225-226 Hunt, Ben, 163 hybrid market, 78 I ICE (Intercontinental Exchange), 30 Ichan, Carl, 136 individual stock circuit breakers, 89 initial public offerings (IPOs), 142-144 integration of exchanges, lack of, 128 Intercontinental Exchange (ICE), 30 internal trades, 31-32 internalization during Flash Crash, 73 in Flash Crash report, 222-225 intraday moves after Great Recession, 2-3 investigation of Flash Crash, 91-95, 183-185, 189-191 consolidated tape delays, 199-205 SEC ethics issues, 193-198 investor behavior, overcorrelation of, 177 investor confidence after Flash Crash, 62 erosion of, 207-210 investor recommendations, 229-234 IPOs (initial public offerings), 142-144 Ira Haupt & Co., 108 Island (ECN), 144-145 Ivandjiiski, Krassimir, 42 Ivandjiiski, Dan, 41-42 J–K Johnson, Lyndon, 114 Johnson, Simon, 186 Junger, Sebastian, 67 justifiable trades, 88 Kanjilal, Debases, 189 Kanjorski, Paul, 81-83 Kaufman, Ted, 37, 47-61, 103, 183-187, 193-198, 208-210 Kay, Bradley, 231-232 Kim, Edward, 142 King, Elizabeth, 196 Kirilenko, Andrei, 171, 201 Kotok, David, 234 Kotz, David, 197 L latency, 167 layering, 210-212 Leibowitz, Larry, 42 Lemov, Michael, 114 Levitt, Arthur, 14, 35, 50, 110, 118, 140-144 Lewis, Michael, 191 life-cycle funds, 230 LIFFE (London International Financial Futures and Options Exchange), 30 limit orders, market orders versus, 224 Lincoln, Abraham, 48 liquidity during Flash Crash, 72-73 Liquidity Replenishment Point (LPR), 78 Liquidnet, 173-174 Lo, Andrew W., 160 London International Financial Futures and Options Exchange (LIFFE), 30 Long Term Capital Management, 98, 159 Loomis, Philip A., 121 LPR (Liquidity Replenishment Point), 78 Luddites, 150 Lukken, Walt, 28 M Madoff, Bernie, 56 Malyshev, Misha, 39 market manipulation, HFT (high-frequency traders) accused of, 39-45 market orders, limit orders versus, 224 market volatility.
., 41 hedge funds, 159-160 front-running by, 140 Hensarling, Jeb, 187 high-frequency trading (HFT) accusation of market manipulation, 39-45 blamed by Congress for Flash Crash, 83-84 collocation, origins of, 165-169 Congressional pressure on SEC to reform, 54-59 eroded market confidence from, 207-210 explanation of, 149-164 Flash Crash consolidated tape delays, 199-205 details of, 61-79 investigation of, 91-92, 183-185, 189-191 in report, 221-227 role in, 7-8 SEC ethics issues, 193-198 origins of, 135-147 public relations efforts of, 150 Quants versus, 157-160 retail trades and, 32 statistics on, 156 strategies employed by, 11-25, 34-37, 161-162 Trillium Brokerage Services LLC, case against, 210-212 volatility reasons for, 175-182 rhythm of, 176-177 history of commodities exchanges in United States, 27-30 Hoffman, Stuart, 234 hot-potato trading, 22 Houtkin, Harvey Ira, 133-139, 161 Hu, Henry, 44, 58, 98 Hunsader, Eric Scott, 199-205, 225-226 Hunt, Ben, 163 hybrid market, 78 I ICE (Intercontinental Exchange), 30 Ichan, Carl, 136 individual stock circuit breakers, 89 initial public offerings (IPOs), 142-144 integration of exchanges, lack of, 128 Intercontinental Exchange (ICE), 30 internal trades, 31-32 internalization during Flash Crash, 73 in Flash Crash report, 222-225 intraday moves after Great Recession, 2-3 investigation of Flash Crash, 91-95, 183-185, 189-191 consolidated tape delays, 199-205 SEC ethics issues, 193-198 investor behavior, overcorrelation of, 177 investor confidence after Flash Crash, 62 erosion of, 207-210 investor recommendations, 229-234 IPOs (initial public offerings), 142-144 Ira Haupt & Co., 108 Island (ECN), 144-145 Ivandjiiski, Krassimir, 42 Ivandjiiski, Dan, 41-42 J–K Johnson, Lyndon, 114 Johnson, Simon, 186 Junger, Sebastian, 67 justifiable trades, 88 Kanjilal, Debases, 189 Kanjorski, Paul, 81-83 Kaufman, Ted, 37, 47-61, 103, 183-187, 193-198, 208-210 Kay, Bradley, 231-232 Kim, Edward, 142 King, Elizabeth, 196 Kirilenko, Andrei, 171, 201 Kotok, David, 234 Kotz, David, 197 L latency, 167 layering, 210-212 Leibowitz, Larry, 42 Lemov, Michael, 114 Levitt, Arthur, 14, 35, 50, 110, 118, 140-144 Lewis, Michael, 191 life-cycle funds, 230 LIFFE (London International Financial Futures and Options Exchange), 30 limit orders, market orders versus, 224 Lincoln, Abraham, 48 liquidity during Flash Crash, 72-73 Liquidity Replenishment Point (LPR), 78 Liquidnet, 173-174 Lo, Andrew W., 160 London International Financial Futures and Options Exchange (LIFFE), 30 Long Term Capital Management, 98, 159 Loomis, Philip A., 121 LPR (Liquidity Replenishment Point), 78 Luddites, 150 Lukken, Walt, 28 M Madoff, Bernie, 56 Malyshev, Misha, 39 market manipulation, HFT (high-frequency traders) accused of, 39-45 market orders, limit orders versus, 224 market volatility.
algorithmic trading, automated trading system, Bernie Madoff, buttonwood tree, corporate governance, cuban missile crisis, financial innovation, Flash crash, Gordon Gekko, High speed trading, latency arbitrage, locking in a profit, Mark Zuckerberg, market fragmentation, Ponzi scheme, price discovery process, price mechanism, price stability, Sergey Aleynikov, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, transaction costs, two-sided market
The first BATS trade was at $15.25 a share, but almost immediately market makers on NASDAQ quoted BATS at $10.98 a share, and then at $8.03, and then $4.17, and then ever lower at blazing speed. It took precisely 1.372 seconds for the price to crash all the way from $15.25 to a fraction of a penny a share, a textbook example of a mini flash crash. While BATS was being brutally flayed during its debut, not one of the SEC’s reforms instituted since the Flash Crash had any effect at all. Never before had a stock market’s own shares flash crashed to oblivion. NASDAQ market makers simply withdrew for “safety,” the same reason why Cummings said Tradebot withdrew in the Flash Crash. The episode made headlines around the world and forced BATS to rescind its IPO. Meaningful stock market reform must reverse at least some of the anarchy of the last 10 years. HFT market maker scalpers should take on material market obligations before they’re handed any market privileges.
Although the headlines of the SEC report seemed to exonerate HFT, many market observers believed there were more problems beneath the surface (see Chapters 10 and 11 for more detail on the Flash Crash). One of the biggest questions was why it took the SEC almost five months to produce a report on less than thirty minutes of trading. The SEC explained that most of the delay was because it did not have a consolidated data trail. The Commission needed to piece together what happened on May 6 from many different sources. It was apparent that the SEC was lacking the tools necessary to police the new equity market that it had created. The Band-Aid Fixes Since the Flash Crash, rather than address the entire fragmented equity market, the SEC proposed and approved a number of short-term band-aid fixes. Single Stock Circuit Breakers In June 2010, single stock circuit breakers were approved by the SEC as an initial line of defense against another flash crash. They initiate a trading halt on moves of more than 10% within a five-minute window on Phase One or Phase Two securities (which include Russell 1000 stocks and some ETFs) and a halt on moves of 30% on all other stocks.
As an example of this kind of statistical artifact, fatalities from a plane crash will not move national mortality statistics a jot, but that is no consolation to the many victims. Academic statistical studies to date are simply not sharp enough to detect most of these episodes. If you look at some of these statistics for 2010, the Flash Crash itself isn’t even noticeable. Nanex, the market data company with its own theories about the crash, did an analysis that looked at stocks using finely tuned statistics.5 Nanex discovered hundreds—thousands—of “mini” flash crashes over the years. The worst year for them so far was 2008, but there were still more than a thousand of them in each of 2009 and 2010. There are several ways to define a mini flash crash. Nanex’s analysis looked at the general case where a stock has an extreme short-term price movement and then an immediate rebound, and counted all these. Some would sort these instances into at least two categories: one in which a large order instantaneously clears out an exchange’s order book down to (or up to) nonsense prices, and the other in which HFT market makers empty a book by withdrawing, or by dumping inventory and withdrawing as on May 6, 2010, but the effect on investors is the same—ricocheting stock prices simply because of structural or regulatory defects in the stock market.
bank run, barriers to entry, bash_history, Bernie Madoff, Flash crash, housing crisis, index fund, locking in a profit, London Whale, market microstructure, merger arbitrage, prediction markets, price discovery process, Sergey Aleynikov, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, zero day
For example, the SEC now legally requires brokers to apply risk controls before an order is placed – preventing orders like the one that started the flash crash from crushing the markets. Further, if there is an abrupt drop in a stock, all trading in that stock is paused. If the broader markets fall abruptly, the SEC and CFTC have mandated a uniform market-wide halt to trading. If you’re still curious, please read at least the executive summary of the SEC-CFTC report – it really answers a lot of questions and has a lot of data behind it. If you’re not still curious, just know that (a) the SEC and CFTC gathered and synthesized mountains of data to produce a comprehensive report, and (b) important fixes were made after the flash crash to prevent a recurrence in similar volatile markets, although more work still needs to be done. Does The Data Exist? “No one could say for sure what caused the flash crash – for the same reason no one could prove that high-frequency traders were front-running the orders of ordinary investors.
The reader is then led to infer that either the fear of Post-Only orders, or a fiery car crash due to the use of Post-Only orders, has scared everyone away from the stock market: “[T]he investing public had lost faith in the U.S. stock market. Since the flash crash back in May 2010, the S&P index had risen 65 percent, and yet trading volume was down 50 percent: For the first time in history, investors’ desire to trade had not risen with market prices. Before the flash crash, 67 percent of U.S. households owned stocks; by the end of 2013, only 52 percent did: the fantastic post-crisis bull market was noteworthy for how many Americans elected not to participate in it.” This is surprising for a number of reasons. Putting aside the numbers – which are actually wrong – the real surprise is why Lewis thinks people are wary of investing in the stock market today. It apparently is due to the twenty minutes of trading in 2010 known as the flash crash, and has nothing to do with losing $19.2 trillion dollars in the Financial Crisis. Really?
The answer is an unequivocal no. So perhaps a better question is: why, if in 200-plus pages, Lewis is going to put forth only three real-world examples purporting a vast market-wide conspiracy of front-running, would he choose this one? If this was one of the most credible and compelling anecdotes, one wonders what less-believable evidence was edited out. The Flash Crash Conspiracy Theory For stock market pundits, the flash crash of May 6, 2010, when the stock market and stock futures markets plunged 6% and recovered only twenty minutes later, is something of a Rorschach test. For data-driven analysts, this was an opportunity to dive deep into the complexity of the derivatives and equities markets, evaluate how they had failed, and fix it. Another group, however, gleefully rubbed their hands together in anticipation of a witch-hunt indulging their favorite conspiracy theory: high-frequency traders gone wild.
The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini
affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, labour mobility, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative ﬁnance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond
It’s not always easy, however, to regulate these fast-playing traders. The theory is that HFTs may have flooded the system with orders. That created a slowdown in order processing, which led to delayed quotes and a loss in confidence around pricing on the day of the Flash Crash. Jittery Markets On the day of the Flash Crash, world developments made the market nervous. Market makers may have been shy about taking on sell volume on a day that was likely to have many sell orders. What made this jittery day different than other jittery days? The Real Cause of the Flash Crash A detailed investigation into Flash Crash causes showed what might have caused the market price chaos on that day in May.14 The market was already jittery on May 6, reacting to negative developments around the world, including the Greek and European debt crisis.
The Spoils of Having Friends in High Places Chapter 12: The Absurdity of Imbalance The Long-Dated Swap Imbalance The Repo Imbalance The 228 Wasted Resources and the Global Run on Banks Chapter 13: Asleep in Basel Basel I Basel II Basel and the Financial Crisis Chapter 14: Chapter The LTCM Spinoffs JWM Partners LLC Platinum Grove Asset Management The Others The Copycat Funds Chapter 15: The End of LTCM’s Legacy The Bear and the Gorilla Attack November Rain What Went Wrong? Chapter 16: New and Old Lessons from the Financial Crisis Interconnectedness and Crowds Leverage Systemic Risk and Too Big to Fail Derivatives: The Good, the Bad, and the Ugly Conflicts of Interest Policy Lessons Risk Management Counterparty Interaction Hedge Funds The Importance of Arbitrage Part III: The Aftermath Chapter 17: The Flash Crash Background Flash Crash Theories The Real Cause of the Flash Crash The Aftermath Chapter 18: Getting Greeked Members Only The Club’s Early Years Getting Greeked Greek Choices The EU’s Future Chapter 19: The Fairy-Tale Decade I Hate Wall Street The Real Costs of the Financial Crisis An Avatar’s Life Force Economic System Choices The Crisis of Crowds The Wine Arbitrage Appendices Appendix A: The Mathematics of LTCM's Risk-Management Framework A General Framework Measuring Risk Appendix B: The Mechanics of the Swap Spread Trade The Mechanics of the Swap Spread Trade The Long Swap Spread Trade The Short Swap Spread Trade Appendix C: Derivation of Approximate Swap Spread Returns Derivation of Approximate Swap Spread Returns Appendix D: Methodology to Compute Zero-Coupon Daily Returns Methodology to Compute Zero-Coupon Daily Returns Appendix E: Methodology to Compute Swap Spread Returns from Zero-Coupon Returns Appendices Appendix F: The Mechanics of the On-the-Run and Off-the-Run Trade Appendix G: The Correlations between LTCM Strategies Before and During the Crisis Appendix H: The Basics of Creative Mortgage Accounting Appendix I: The Business of an Investment Bank The Business of an Investment Bank Investment Banking Capital Markets Equities Fixed Income Foreign Exchange Global Distribution (Global Sales) Research Client Services Technology Corporate and Risk Management Summary Appendix J: The Calculation of the BIS Capital Adequacy Ratio The Calculation of the BIS Capital Adequacy Ratio The General Calculation An Example Appendix K: The U.S.
Chincarini raises some interesting arguments on crowding and risk models, and practitioners and academics can debate these ideas since risk management is still evolving and the market events of 2008 showed that these models were not fully reliable. There are places where Chincarini applies the crowd idea differently. For example, the author puts “crowd behavior” at the center of the flash crash of May 6, 2010, which is the subject of Chapter 17. The flash crash was, no doubt, a very interesting event. Most accounts blame a large trade in the equity futures market, apparently implemented by a poorly designed algorithm that did not respond appropriately as market impact increased over time. According to this version of events, the trade created a shock that led to a whiplike impact in exchange-traded funds (ETFs) and then even more profound price moves in individual equities as liquidity, today largely provided by high-frequency trading algorithms, dried up.
algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, cloud computing, collapse of Lehman Brothers, Donald Trump, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!
As Mathisson looked out over the audience, he knew Santayana wouldn’t be trolling clubs for bleach-blond babes this year. A freakish stock market crash on May 6, 2010—the so-called Flash Crash—had revealed that the computer-driven market was far more dangerous than anyone had realized. Regulators were angry, fund managers furious. Something had gone dramatically wrong. Senators were banging down Mathisson’s door wanting to know what the hell was going on. A harsh light was shining on an industry that had grown in the shadows. Mathisson was ready to confront the attack. He hit a button on the remote for his PowerPoint presentation. A graph appeared. A jagged line took a cliff-like plunge followed by a sharp vertical leap. It looked like a tilted V, the far right-hand side just lower than the left. “There’s the Flash Crash,” he said. “We all remember that day, of course.” The chart showed the Dow Jones Industrial Average, which took an eight-hundred-point swan dive in a matter of minutes on May 6 due to glitches deep in the plumbing of the nation’s computer-trading systems—the very systems built and run by many of the people sitting in the Glitter Room.
The chart showed the Dow Jones Industrial Average, which took an eight-hundred-point swan dive in a matter of minutes on May 6 due to glitches deep in the plumbing of the nation’s computer-trading systems—the very systems built and run by many of the people sitting in the Glitter Room. The audience stirred. The Flash Crash was a downer, and they were restless. It was going to be a long day full of presentations. Later that night, they’d be treated to a speech by the Right Honorable Gordon Brown, former prime minister of the United Kingdom. Ex–Clinton aide James Carville would address the group the following morning. (It was nothing unusual. Past keynote speakers at the conference had included luminaries such as former Federal Reserve chairman Alan Greenspan, former secretary of state Colin Powell, and the onetime junk-bond king Michael Milken.) Mathisson hit the button, calling up a chart showing that cash had flowed out of mutual funds every single month through 2010, following the Flash Crash. Legions of regular investors had become fed up, convinced the market had become either far too dangerous to entrust with their retirement savings, or just outright rigged to the benefit of an elite technorati.
., headquarters—Niederauer, Greifeld, Joe Ratterman from BATS, Bill O’Brien from Direct Edge—and gave them their marching orders: Implement circuit breakers for individual stocks in order to head off another flash crash. After years of pushing speed like an inner-city drug dealer, the SEC was shifting course. Marketwide circuit breakers, which would trigger a brief stop in trading if the market made a major move in a short period of time, were quickly implemented. They amounted to a reversal of the speed-freak frenzy that had hijacked the financial system in the past decade. It was time to slow things down. Was it enough? No one knew. IN the weeks and months following the events the media dubbed the Flash Crash, the fierce debate over what had become of the U.S. stock market that had erupted after the arrest of Sergey Aleynikov grew even more heated. Angry words were exchanged in the halls of Capitol Hill, on financial television shows, and in the backrooms of giant trading firms in New York and Chicago.
Flash Boys: A Wall Street Revolt by Michael Lewis
automated trading system, bash_history, Berlin Wall, Bernie Madoff, collateralized debt obligation, Fall of the Berlin Wall, financial intermediation, Flash crash, High speed trading, latency arbitrage, pattern recognition, risk tolerance, Sergey Aleynikov, Small Order Execution System, Spread Networks laid a new fibre optics cable between New York and Chicago, too big to fail, trade route, transaction costs, Vanguard fund
He watched the most sophisticated investors respond after Duncan Niederauer, the CEO of the New York Stock Exchange, embarked on a goodwill tour, the purpose of which seemed to be to explain why the New York Stock Exchange had nothing to do with the flash crash. “That’s when a light went off,” said Danny Moses, of Seawolf Capital, a hedge fund that specialized in stock market investments. He had heard Brad and Ronan’s pitch. “Niederauer was saying, ‘Hey, have confidence in us. It wasn’t us.’ Wait a minute: I never thought it was you. Why should I be concerned that it was you? It was like your kid walks into your house and says to you, ‘Dad, I didn’t dent your car.’ Wait, there’s a dent in my car?” After the flash crash, Brad no longer bothered to call investors to set up meetings. His phone rang off the hook. “What the flash crash did,” said Brad, “was it opened the buy side’s willingness to understand what was going on. Because their bosses started asking questions.
That was just a sampling from a single year of what were usually described as “technical glitches” in the new, automated U.S. stock markets: Collectively, they had experienced twice as many outages in the two years after the flash crash as in the previous ten. The technical glitches were accompanied by equally bewildering irregularities in stock prices. In April 2013, the price of Google’s shares fell from $796 to $775 in three-quarters of a second, for instance, and then rebounded to $793 in the next second. In May the U.S. utilities sector experienced a mini–flash crash, with stocks falling by 50 percent or more for a few seconds before bouncing back to their previous prices. These mini–flash crashes in individual stocks that now occurred routinely went largely unnoticed and unremarked upon.* Zoran liked to argue that there were actually fewer, not more, “technical glitches” in 2012 than there had been in 2006—it was only the financial consequences of system breakdowns that had grown.
The first thing Brad noticed as he read the SEC report on the flash crash was its old-fashioned sense of time. “I did a search of the report for the word ‘minute,’ ” said Brad. “I got eighty-seven hits. I then searched for ‘second’ and got sixty-three hits. I then searched for ‘millisecond’ and got four hits—none of them actually relevant. Finally, I searched for ‘microsecond’ and got zero hits.” He read the report once and then never looked at it again. “Once you get a sense of the speed with which things are happening, you realize that explanations like this—someone hitting a button—are not right,” he said. “You want to see a single time-stamped sheet of every trade. To see what followed from what. Not only does it not exist, it can’t exist, as currently configured.” No one could say for sure what caused the flash crash—for the same reason no one could prove that high-frequency traders were front-running the orders of ordinary investors.
The Payoff by Jeff Connaughton
algorithmic trading, bank run, banking crisis, Bernie Madoff, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cuban missile crisis, desegregation, Flash crash, locking in a profit, London Interbank Offered Rate, London Whale, Long Term Capital Management, naked short selling, Plutocrats, plutocrats, Ponzi scheme, risk tolerance, short selling, Silicon Valley, too big to fail, two-sided market, young professional
S. exchange. Thanks to Josh’s intrepid research and synthesis, tutorials from our covert industry insiders, and our own exhaustive (and exhausting) reading, Ted and I became extremely knowledgeable about these practices and how they affect market stability. In fact, Ted even predicted the flash crash—when the market dropped one thousand points in just minutes on May 6, 2010—eight months before it happened. In a speech on September 14, 2009, the anniversary of the collapse of Lehman Brothers, Ted warned of a flash crash and how HFT would fuel it: [U]nlike specialists and traditional market-makers that are regulated, some of these new high-frequency traders are unregulated, though they are acting in a market-maker capacity. They have no requirements to “maintain a fair and orderly” market. They trade when it benefits them.
Ted had become the Oracle of Delaware, the man who’d read the algorithmic auguries of high-frequency trading and foreseen the flash crash. Jim Cramer of Mad Money called Ted the “most sophisticated man in Washington” and someone who was looking out for the average investor. Ted’s clairvoyance gave him instant credibility with many of his colleagues, and we intended to use it. I immediately started drafting a letter from Ted and Warner to Chris Dodd. We delivered it the next day, May 7. The letter asked Dodd to add to the Wall Street reform bill then before the Senate the requirement that the SEC and the Commodity Futures Trading Commission (CFTC) conduct a joint study of what had caused the flash crash and how it should be dealt with. “A temporary $1 trillion drop in market value is an unacceptable consequence of a software glitch,” the letter said.
But it still had no real knowledge about how HFT strategies affected the market or the average investor. As CNBC’s Jim Cramer later said of the SEC, “The lifeguard is off duty. And when you go swimming in this market, you’d better remember there’s nobody out there making sure the water is safe.” The flash crash taught at least three lessons, all of which Ted had identified long before May 6, 2010. First, stock prices don’t always reflect the market’s best estimation of the value of the underlying companies; in mini flash crashes, they can result from the breakdown of algorithmic trading strategies. Second, technology has far outpaced regulation. Regulators’ lack of understanding of HFT strategies and the volatility they create left the markets vulnerable to a nausea-inducing plunge. For example, the SEC took for granted that high-frequency traders were the new market makers without taking into account the ways in which they differed from traditional market makers.
Affordable Care Act / Obamacare, algorithmic trading, Andrei Shleifer, asset-backed security, availability heuristic, bank run, banking crisis, Black-Scholes formula, bonus culture, Bretton Woods, call centre, Carmen Reinhart, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, 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, Eugene Fama: efficient market hypothesis, eurozone crisis, family office, financial deregulation, financial innovation, fixed income, Flash crash, Google Glasses, Gordon Gekko, high net worth, housing crisis, Hyman Minsky, implied volatility, income inequality, index fund, Innovator's Dilemma, interest rate swap, Kenneth Rogoff, Kickstarter, late fees, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, Mark Zuckerberg, McMansion, mortgage debt, mortgage tax deduction, 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 Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, short selling, Silicon Valley, Silicon Valley startup, Skype, South Sea Bubble, sovereign wealth fund, statistical model, transaction costs, Tunguska event, unbanked and underbanked, underbanked, Vanguard fund, web application
If you have a few minutes to spare and want to listen to the sound of chaos, find an audio recording of the futures pits in Chicago during those moments of panic.17 A joint Commodity Futures Trading Commission and Securities and Exchange Commission (SEC) task force that investigated the flash crash found that the primary cause of the volatility was a “fundamental” seller. To be precise, at 2:32 p.m., a mutual fund began to execute an order to sell seventy-five thousand E-Mini S&P 500 futures contracts in order to hedge an existing equity exposure. Whether trying to offload these types of contract or any other asset, a seller does not want the price to go down before its order has gone through. So the sell order uses algorithms that are designed to sell the holding without being spotted, a bit like sneaking out of a party when it’s crowded. Some algorithms slice big orders into a lot of tiny ones; in the flash crash, the computers were programmed to sell more shares when there was more trading going on in the stock.
This can lead to extraordinary numbers of transactions, which is exactly what happened during the flash crash. In a 14-second period between 2:45:13 and 2:45:27, high-frequency traders swapped more than 27,000 E-Mini contracts, which accounted for almost half of the total trading volume, while buying only about two hundred additional contracts.19 Remember, however, that the original algorithm uses the volume of transactions as a prompt to unload shares. The increase in trading brought about by the “hot-potato” effect acted as a signal for the first algorithm to sell more stock, which moved the price down a bit further, sparking more activity by the high-frequency traders. The loop was closed. The flash crash comprised more elements than this volume-driven downward pressure, however. The price declines brought a lot more algorithms, used by both high-frequency traders and institutional investors, into play.
Basildon is already connected to markets in Frankfurt via microwave; a journey that takes around 8 milliseconds on fiber-optic cables takes 4.7 milliseconds or less this way. There is talk of a network of microwave towers stretching across the Atlantic to connect London and New York as high-frequency traders strive for the nirvana of zero latency.16 The changing nature of the financial markets became clear to the wider world in an event that has since become known as the “flash crash.” On May 6, 2010, in a 30-minute period between 2:30 p.m. and 3:00 p.m. EDT, a number of equity markets tumbled and rebounded with extraordinary rapidity. The Dow Jones Industrial Average fell by more than 5 percent in 5 minutes, before then recovering much of its losses. Individual share prices exhibited even more bizarre behavior. Shares in Accenture, a consultancy firm with $30 billion in annual revenues, plunged from more than $40 a share to, at one point, a single cent.
Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen
algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, mortgage debt, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond
The most extreme trades were later canceled, however. The role of HFTs in the flash crash was not so much what they did but what they didn’t do, namely provide unlimited liquidity. However, the failure of market makers to provide liquidity in the face of overwhelming one-sided demand pressure, confusion about market prices, and increasing risk has always been a problem. For example, old-fashioned market makers in NASDAQ stocks and in over-the-counter markets have been known to take their phones off the hook when markets have gone off the cliff, e.g., in the 1987 stock market crash. Also, half a century before the flash crash of 2010, a similar event occurred that came to be known as the “Market Break of May 1962.” This event was also investigated by the SEC and, as in the flash crash, the SEC found that the “lateness of the NYSE tape and the size of the price declines on the NYSE prompted some over-the-counter dealers to withdraw as market makers in certain securities.”10 9.4.
Some HFTs may also try to identify and exploit large orders that are broken up into smaller trades and traded over hours or days. For example, if you are seeking to buy a large stock position, try to submit a limit order to buy the same number of shares each minute, right at the minute, and see what happens to your execution (relative to an execution where you split up the order more finely and more randomly and execute at more random times). The Flash Crash of 2010 On May 6, 2010, dramatic market events occurred in the U.S. stock market that came to be known as the flash crash. From the morning, the market was dropping on large trading volume and volatility due to rising fears about the ongoing European debt crisis. At 2:32 p.m., the Standard & Poor’s 500 (S&P 500) stock market index was down 2.8%. The limit order book was thinning due to the heightened volatility and because some exchanges were experiencing data delays and other data problems.
The last time this mutual fund had executed a similar sized order, it had done so over the course of several hours, but on the day of the flash crash, the selling mutual fund decided to have the order executed with an algorithm over just 20 minutes. Over the next 13 minutes, the market dropped 5.2% in value, an enormous move over such a short time period, as seen in figure 9.10. HFTs initially provided liquidity. They were net buyers as the market was dropping, but, at 2:41 p.m., HFTs turned around and became net sellers, perhaps to reduce their inventory risk, but throughout the event, HFTs were mainly buying and selling to each other as documented by the CFTC and SEC: Figure 9.10. The flash crash of May 6, 2010. Still lacking sufficient demand from fundamental buyers or cross-market arbitrageurs, HFTs began to quickly buy and then resell contracts to each other—generating a “hot-potato” volume effect as the same positions were rapidly passed back and forth.
Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman
3D printing, algorithmic trading, Anton Chekhov, Apple II, Benoit Mandelbrot, citation needed, combinatorial explosion, Danny Hillis, David Brooks, discovery of the americas, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, HyperCard, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Netflix Prize, Nicholas Carr, Parkinson's law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, Therac-25, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K
Homer-Dixon based part of his narrative on James Gleick, “A Bug and a Crash: Sometimes a Bug Is More Than a Nuisance,” 1996, http://www.around.com/ariane.html (which originally appeared in The New York Times Magazine, December 1996). For a similar discussion of proximate causes versus the underlying reasons for such sudden system failures, see Chris Clearfield and James Owen Weatherall, “Why the Flash Crash Really Matters,” Nautilus 023, April 23, 2015, http://nautil.us/issue/23/dominoes/why-the-flash-crash-really-matters. Three Mile Island nuclear disaster: Clearfield and Weatherall, “Why the Flash Crash Really Matters.” the system’s massive complexity: Essentially, the failure in each of these cases was due to endogenous complexity—the complexity that evolves within a large system—rather than just to any specific exogenous shock. popular narrative of the Challenger: It must be recognized that the Challenger accident was more complicated than the streamlined story we are often told about its cause.
the writer Quinn Norton has noted: Quinn Norton, “Everything is Broken,” The Message, May 20, 2014, https://medium.com/message/81e5f33a24e1. Langdon Winner notes in his book: Winner, Autonomous Technology, 290–91. computer scientist Danny Hillis argues: Danny Hillis, “The Age of Digital Entanglement,” Scientific American, September 2010, 93. Take the so-called Flash Crash: Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford, UK: Oxford University Press, 2014), 17. It is still not entirely clear, however, what caused the Flash Crash. Understanding something in a “good enough” way: See also César Hidalgo, Why Information Grows: The Evolution of Order, from Atoms to Economies (New York: Basic Books, 2015). CHAPTER 2: THE ORIGINS OF THE KLUGE the Internet first began to be developed: For more, see Barry M. Leiner et al., “Brief History of the Internet,” Internet Society, October 15, 2012, http://www.internetsociety.org/brief-history-internet.
Today’s markets involve not just humans, but large numbers of computer programs trading on a wide variety of information at rates faster than what people could do manually. These programs interlock in complicated ways, making decisions that can cascade through vast trading networks. But how are the decisions made on how to trade? By pouring huge amounts of data into still other programs, ones that fit vast numbers of parameters in an effort to squeeze meaning from incredible complexity. The result can be extreme. Take the so-called Flash Crash, when, on May 6, 2010, the global financial market experienced a massive but extremely rapid fluctuation in the stock market, as large numbers of companies lost huge amounts of value, only to regain them instants later. This crash seems to have involved a series of algorithms and their specific rules for trading all interacting in unexpected ways, causing a trillion dollars in lost value for a short period of time.
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
Admiral Zheng, banking crisis, Basel III, Bernie Madoff, Black Swan, centralized clearinghouse, clean water, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crowdsourcing, David Brooks, Dissolution of the Soviet Union, diversification, diversified portfolio, en.wikipedia.org, financial innovation, financial intermediation, Flash crash, income inequality, index fund, invisible hand, London Whale, Long Term Capital Management, moral hazard, Northern Rock, passive investing, performance metric, Ponzi scheme, principal–agent problem, rent-seeking, Ronald Coase, shareholder value, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, Steve Jobs, the market place, The Wealth of Nations by Adam Smith, transaction costs, Upton Sinclair, value at risk, WikiLeaks
Soon the crisis spilled over to individual stocks. According to a joint report from the US Commodity Futures Trading Commission and the SEC, some twenty thousand individual trades on three hundred different securities were made at prices at least 60 percent different from the prices that existed before the flash crash started—even though the market had recovered in twenty minutes and had returned to normal by 3:00. Some stocks were traded at “irrational prices as low as one penny or as high as $100,000.”48 So the flash crash was warning no. 1, the false tweet warning no. 2. Barry Schwartz, a Canadian portfolio manager, draws a simple conclusion: “Don’t let computers rule your investments.”49 But few are listening. High-frequency computer-driven trading is estimated to account for 61 percent of all stock market trades in the United States.50 If such trading adds value, it does so at a cost to other investors.
“No human believed the story,” said Rick Fier, Director of Equity Trading at Conifer Securities. “Only the computers react to something that serious disseminated in such a way. I bought some stock well and did not sell into it. Humans win.”44 Others also blamed the computers but noted how scary that thought is.45 It was not the first time Wall Street had dodged a bullet. Nearly three years before, on May 6, 2010, there was the “flash crash.” At about 2:32 p.m., a mutual fund entered a sell order for a large amount—$4.1 billion’s worth—of a futures contract designed to mimic the performance of the S&P 500 index. The computer program that entered the trade was created to take into account the volume of trading, but not the time frame or the price.46 It turned out there weren’t enough buyers for such a large sale. Within seconds, high-frequency trading computers went into an electronic frenzy, creating what the Wall Street Journal called a “hot potato effect”: computers sold to other computers, which then tried to sell to other computers to hedge their positions.47 In just three minutes, from 2:41 to 2:44, the price of the futures contract and the actual S&P trading basket dropped 3 percent; then, in just fifteen more seconds, it dropped another 1.7 percent.
Lu Wang, Whitney Kisling, and Eric Lam, “Fake Post Erasing $136 Billion Shows Market Needs Humans” (Bloomberg News Service, April 24, 2013). 44. Ibid. 45. Ibid. 46. “Findings Regarding the Market Events of May 6, 2010: Report of the Staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues” (US Commodity Futures Trading Commission and US Securities and Exchange Commission, September 30, 2010). 47. Tom Lauricella, Kara Scannell, and Jenny Strasburg, “How a Trading Algorithm Went Awry: Flash-Crash Report Finds a ‘Hot Potato’ Volume Effect from Same Positions Passed Back and Forth,” Wall Street Journal, October 2, 2010. 48. Ibid. 49. Wang, Kisling, and Lam, “Fake Post Erasing $136 Billion.” 50. Larry Tabb, Tabb Group, “High Frequency Trading: What Is It and Should I Be Worried?,” presentation to the World Federation of Exchanges, Cambridge, MA, November 2009. 51. Chris Sier, interview for this book, April 25, 2015. 52.
3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, 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. We know that a lot of algorithms: “The Market’s Black Box: Engine for Efficiency or Ever-Growing Monster?” Paris Tech Review, August 25, 2010, http://www.paristechreview.com/2010/08/25/market-black-box-efficiency-growing-monster/ (accessed July 2, 2011).
They’d detect cascading algorithm interactions like the 2010 Flash Crash and unplug the machines. The Large Trader Rule requires detailed registration of AIs, along with full human organization charts. If this sounds like a prelude to large government intervention, it is. Why not? Wall Street has proven again and again that as a culture it cannot behave responsibly without strenuous regulation. Is that also true of AGI developers? Without a doubt. There’s no moral merit badge required for studying AGI. Pre-trade testing of algorithms could simulate algorithms’ behavior in a virtual environment before they were let loose on the market. AI Source Code audits and Centralized AI Activity Recording aim to anticipate errors, and facilitate after-game analysis following an accident, like the 2010 Flash Crash. But look back at the four levels of opacity mentioned earlier, and see if these defenses, even if they were fully implemented, sound anything like foolproof to you
According to Wissner-Gross, market observers have suggested that some seem to be signaling each other across Wall Street with millisecond trades that occur at a pace no human can track (these are HFTs or high-frequency trades, discussed in chapter 6). Wouldn’t the next logical step be to make your hedge fund reflective? That is, perhaps your algorithm shouldn’t automatically trigger sell orders based on another fund’s massive sell-off (which is what happened in the flash crash of May 2010). Instead it would perceive the sell-off and see how it was impacting other funds, and the market as a whole, before making its move. It might make a different, better move. Or maybe it could do one better, and simultaneously run a very large number of hypothetical markets, and be prepared to execute one of many strategies in response to the right conditions. In other words, there are huge financial incentives for your algorithm to be self-aware—to know exactly what it is and model the world around it.
Rigged Money: Beating Wall Street at Its Own Game by Lee Munson
affirmative action, asset allocation, backtesting, barriers to entry, Bernie Madoff, Bretton Woods, buy low sell high, California gold rush, call centre, Credit Default Swap, diversification, diversified portfolio, estate planning, fiat currency, financial innovation, fixed income, Flash crash, follow your passion, German hyperinflation, High speed trading, housing crisis, index fund, joint-stock company, moral hazard, passive investing, Ponzi scheme, price discovery process, random walk, risk tolerance, risk-adjusted returns, risk/return, too big to fail, trade route, Vanguard fund, walking around money
Short term, if you only care about generating commissions, HFTs will work until routers stop hitting your ECN. Thus, it is the ECN that wants to be careful of the bad HFTs, since they are the only people that can root them out. Figure 6.1 100,000? A Lie! When the Lights Go Out It seems that after the Flash Crash we learned that HFTs provided a false sense of security. Sure, under normal circumstances HFTs are the new market makers, buying and selling all day long to bring liquidity to the market. However, when things go south, the operators just shut off the machines and the liquidity disappears. According to the SEC’s report on the Flash Crash, “Still lacking sufficient demand from fundamental buyers or cross-market arbitrageurs, HFTs began to quickly buy and then resell contracts to each other—generating a ‘hot-potato’ volume effect as the same positions were rapidly passed back and forth.
The knee-jerk reaction to the economic impact was like slow motion compared to the crash of 1929, which had the benefit of radio to broadcast the panic on a daily basis. You can understand how things have changed since then. Back then you couldn’t use your smart phone to take a picture of your house burning down or tweet about the destruction of San Francisco to your friends. Today, the world knew immediately about the 20-minute long Flash Crash of May 6, 2010, and was able to watch the market recover in real time. Is it progress that we are now at the point of 20-minute long stock market crashes? Technology has created a false veneer of total information awareness. I’m here to expose how a few specific events occurred that contributed to the delinquency of the long-term investor. Many market prognosticators like to spit out factoids of how the average holding period of stocks back in the 1950s was around eight years.
Say you want to sell your house today for $100 (yes, you live in Detroit). If only one person walks by and offers $50, that’s the price you can sell for. If two people will pay $100, the price might rise until one backs down. In each case, the transaction price is based on what the other person is willing to pay; either a limit they set, or the market price you accept. When the market declines quickly, as we saw in the Flash Crash of May 6, 2010, many orders were placed to sell at the market price. Because there were not enough orders to buy at the market price, stocks fell until the market found a price that buyers were willing to pay. That is why the market didn’t go to zero that day. At some point orders came in, or limits were hit to buy shares at the specified prices. So, the next time someone tells you there were more sellers than buyers, you can tell him there were equal amount of both, but more liquidity takers on the buy side.
Automate This: How Algorithms Came to Rule Our World by Christopher Steiner
23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Mark Zuckerberg, market bubble, medical residency, Narrative Science, PageRank, pattern recognition, Paul Graham, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator
Algorithms normally behave as they’re designed, quietly trading stocks or, in the case of Amazon, pricing books according to supply and demand. But left unsupervised, algorithms can and will do strange things. As we put more and more of our world under the control of algorithms, we can lose track of who—or what—is pulling the strings. This is a fact that had sneaked up on the world until the Flash Crash shook us awake. Algorithms entered evening newscasts through the door of the Flash Crash, but they didn’t leave. They soon showed up in stories about dating, shopping, entertainment, medicine—everything imaginable. The Flash Crash had merely been an augur for a bigger trend: algorithms are taking over everything. When a process on the Web or inside a machine happens automatically, a pithy explanation often comes with it: “It’s an algorithm.” The classical definition of an algorithm says the device is a list of instructions that leads its user to a particular answer or output based on the information at hand.
Burnett’s journalistic fiber had her more excited. “But the fact that after all this, that that could have just happened, is an absolutely stupendous story,” she exclaimed. “I think it’s a great story,” Cramer said flatly. “It’s the greatest story never told. You’ll never know what happened here.” Cramer wasn’t wrong. As of this writing, there is still no consensus on the exact root of what became known as the Flash Crash. Some of the blame was directed at a Kansas City money manager whose algorithm sold off $4 billion worth of stock futures too quickly, sparking other algorithms to do the same. Some blame an unknown group of traders who conspired to send things down all at once through the use of coordinated algorithms. Some believe it was simply an old-fashioned panic, not unlike what the world witnessed in 1929.
Wall Street fortunes have been made betting on Gaussian distributions—and just as many have been lost on algorithms that embrace Gaussian outcomes but don’t account for fat tails. It’s easier to write algorithms to fit normal distributions. And despite history showing us repeatedly that human behavior is anything but normal, some hackers choose to account for only normal distribution. Using this assumption can make money 100 out of 100 days. But it’s day 101, the Black Monday of 1987, the Russian debt default of 1998, the Flash Crash of 2010, that can ruin those banking on algorithms designed purely around Gaussian distributions. Even Gauss, more than two hundred years ago, warned that errors of any magnitude are possible within a normal distribution.26 The introduction of normal distributions changed humankind and ushered in the modern field of statistics, which allows for the easy purchase of things like life insurance, the building of better bridges, and even, though not as important, betting on basketball games.
Bad Data Handbook by Q. Ethan McCallum
Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, cloud computing, cognitive dissonance, combinatorial explosion, conceptual framework, database schema, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative ﬁnance, recommendation engine, sentiment analysis, statistical model, supply-chain management, text mining, too big to fail, web application
Those trades officially never happened, and in the end no money changed hands. Most stock data sources have already had the canceled trades removed, so it is difficult to even see the evidence of the flash crash in the historic record. In that sense, the flash crash contained bad data, not bad reality. Yet, the only difference between the flash crash and the United Airlines example is that human intervention undid the flash crash after it happened; it was “too big to fail.” To further cloud the issue, people believed that those trades were real when they happened. Stock trading is state-dependent; how you behave depends on the previous trades that happened. To properly model trading during the flash crash, you would have to simulate making trades, updating your portfolio, and then having them canceled afterwards. In reality, you would just shrug your shoulders and hope that it is okay to ignore this sort of crash.
United Airlines (UAUA) Stock Price on 2008-09-08, Volume-Weighted Average over One-Minute Intervals Many data-cleaning algorithms would throw out these few minutes of obviously wrong prices. Yet, these prices did happen; real money was exchanged. Within ten minutes, 15% of UAUA’s shares and $156 million had changed hands. This is correct data that should be kept for any honest analysis. Other times, the answer is not so clear cut. In the May 6th, 2010, “flash crash,” many stocks suddenly dropped in price by more than half for no readily apparent reason before going back again when sanity prevailed. There is still no consensus on exactly what happened. After the crash had been resolved, the various stock exchanges retroactively canceled all trades that were more than 60% different from the pre-crash price. This caused an odd situation where people who got a really good bargain buying shares for half the correct price got to keep their gains, while those who had an even better deal at 60% off had to give the profits back.
Situations like the United Airlines example are more or less just outlier detection; a bad price that persists for one second should probably be thrown out while one that keeps going for a few minutes probably should not. It is a matter of judgment to decide what kind of thresholds to set and what error rates are acceptable, but that is a tractable data problem. But there is little that can be done to deal with situations like the flash crash, except to know that they happened and work around them. There is no systematic way to model extraordinary events which have been shaped by human judgment and politics. Conclusion The theme of these examples is that clean-looking data often has additional complexity lurking under the surface. If you do not understand the data, where it comes from, and what it represents, then your conclusions may carry a bias.
Andrei Shleifer, asset-backed security, bank run, barriers to entry, Basel III, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, blue-collar work, Bretton Woods, Brownian motion, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, constrained optimization, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, dark matter, David Brooks, David Graeber, debt deflation, deindustrialization, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, facts on the ground, Fall of the Berlin Wall, financial deregulation, financial innovation, Flash crash, full employment, George Akerlof, Goldman Sachs: Vampire Squid, Hernando de Soto, housing crisis, Hyman Minsky, illegal immigration, income inequality, incomplete markets, invisible hand, Jean Tirole, joint-stock company, Kenneth Rogoff, knowledge economy, l'esprit de l'escalier, labor-force participation, liquidity trap, loose coupling, manufacturing employment, market clearing, market design, market fundamentalism, Martin Wolf, Mont Pelerin Society, moral hazard, mortgage debt, Naomi Klein, Nash equilibrium, night-watchman state, Northern Rock, Occupy movement, offshore financial centre, oil shock, payday loans, Ponzi scheme, precariat, prediction markets, price mechanism, profit motive, quantitative easing, race to the bottom, random walk, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, savings glut, school choice, sealed-bid auction, Silicon Valley, South Sea Bubble, Steven Levy, technoutopianism, The Chicago School, The Great Moderation, the map is not the territory, The Myth of the Rational Market, the scientific method, The Wisdom of Crowds, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, War on Poverty, Washington Consensus, We are the 99%, working poor
It then broke out in the theoretical area concerning whereof the vast majority of neoclassical economists were most proud: the microeconomics of a fully competitive market.89 One of the most worrying heralds of fresh mortification was the so-called flash crash that occurred in New York share markets on the afternoon of May 6, 2010. For twenty minutes, starting at 2:30 p.m., trading volume spiked dramatically as a wide range of shares fell more than 5 percent in a matter of minutes, only to recover equally sharply (Figure 4.7). The same also happened on a number of exchange-traded indexes. * * * Figure 4.7: The “Flash Crash” of May 6, 2010 * * * * * * Source: Bloomberg Some individual share prices dropped to mere pennies in price, forcing the various exchanges to impose “broken” or canceled trades on something like 27 percent of all transactions.
Perhaps more distressing, a month later, the government investigators and financial economists were no more the wiser as to the real causes of the whipsaw movement.90 What was more disconcerting was that a brawl subsequently broke out among economists over which of the myriad “innovations” in trading may have been the culprit: high-frequency automated trading, the dispersal of trades among numerous for-profit exchanges, robo-trading in general, the practice of “stub quotes,” the role of exchange-traded funds, and so forth. Other, smaller flash crashes have occurred in financial markets since then, among which may be counted the BATS IPO fiasco and the Knight Capital spasm. Distressingly, no consensus interpretation of the flash crashes has taken hold among economists. While this should give market participants pause, you might have thought it would frighten economists even more, since this phenomenon potentially contradicts everything their core models postulate about market behavior. Neoliberals should equally take umbrage, since the wonderful information processing capacities of the market seem impugned by such events.
OK, maybe I can let microeconomists off the hook” (Krugman, “The Profession and the Crisis,” p. 307). 90 The preliminary SEC report can be consulted at www.sec.gov/sec-cftc-prelimreport.pdf. The final attempt at imposing consensus came out five months later as U.S. SEC, Findings Regarding the Market Events of May 6, 2010, but the dispute nevertheless continues. See Kirilenko et al., “The Flash Crash”; Easley et al., “The Microstructure of the Flash Crash”. 91 This attitude is almost too ubiquitous to document properly. For selected examples, see Coyle, “The Public Responsibilities of the Economist”; Krugman, End This Depression Now!; and Quiggin, Zombie Economics. 92 One rather unapologetic example is Eric Maskin at http://thebrowser.com/interviews/eric-maskin-on-economic-theory-and-financial-crisis. Maskin is domiciled at the Institute for Advanced Study, and is a specialist in game theory, which may go some distance in explaining the intellectual bubble he inhabits.
Wait: The Art and Science of Delay by Frank Partnoy
algorithmic trading, Atul Gawande, Bernie Madoff, Black Swan, blood diamonds, Cass Sunstein, Checklist Manifesto, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, Daniel Kahneman / Amos Tversky, delayed gratification, Flash crash, Frederick Winslow Taylor, George Akerlof, Google Earth, Hernando de Soto, High speed trading, impulse control, income inequality, Isaac Newton, Long Term Capital Management, Menlo Park, mental accounting, meta analysis, meta-analysis, Nick Leeson, paper trading, Paul Graham, payday loans, Ralph Nader, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, six sigma, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical model, Steve Jobs, The Market for Lemons, the scientific method, The Wealth of Nations by Adam Smith, upwardly mobile, Walter Mischel
Although some politicians have argued for regulators to police high-frequency trading, it is unlikely that regulators would have much of a chance against computer trading algorithms, any more than they would be able to beat a computer at chess or Jeopardy. By the time the federal government’s report on the flash crash was published on September 30, 2010, market participants already had switched to new strategies. No one would use Waddell & Reed’s trading program today. Although regulators won’t have much of a chance battling high-frequency traders directly, there is one policy they might implement to help protect against future flash crashes: instead of trying to keep up with the markets, regulators could help slow them down by introducing explicit pauses. Stock exchanges already use circuit breakers to force markets to shut down when they have declined by certain amounts.11 So here is one concrete proposal to help traders slow down: force them to take a lunch break.
The details of the “flash crash” were reported in “Findings Regarding the Market Events of May 6, 2010,” report of the staffs of the Commodities Futures Trading Commission (CFTC) and the Securities and Exchange Commission (SEC) to the Joint CFTC-SEC Advisory Committee on Emerging Regulatory Issues, September 30, 2010. 7. Joel Hasbrouck and Gideon Saar, “Low-Latency Trading,” Johnson School Research Paper Series 35-2010, September 1, 2011, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1695460; see also Jonathan A. Brogaard, “High-Frequency Trading and Market Quality,” Working Paper Series, July 17, 2010, available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1970072; Andrei Kirilenko, Albert S. Kyle, Mehrdad Samadi, and Tugkan Tuzun, “The Flash Crash: The Impact of High-Frequency Trading on an Electronic Market,” Working Paper Series, May 26, 2011, available at: http://papers.ssrn.com/sol3/papers.cfm?
The E-Mini contract and all of these stocks recovered. By 3:08 PM, the market had settled and prices were about the same as they were before Waddell & Reed had started its trading program. When the firm’s computers finished selling 75,000 E-Mini contracts, they ran out of instructions and shut down. The entire ride, the bust and the boom, had taken just thirty-six minutes. It became known as the “flash crash.”6 Numerous studies show that under normal conditions high-frequency traders are a powerful positive force in the markets. They improve liquidity, making it easier and cheaper for us to buy and sell stocks. They reduce volatility, particularly in the short term, so that stock prices remain relatively stable.7 However, other studies show that during periods of high uncertainty, such as May 6, 2010, high-frequency trading is associated with increased volatility and sudden, abrupt swings in the prices of stocks.8 Overall, the evidence is mixed.
Present Shock: When Everything Happens Now by Douglas Rushkoff
algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K
Algorithms determine the songs playing on Clear Channel stations, the ideal partners on dating websites, the best driving routes, and even the plot twists for Hollywood screenplays—all by compressing the data of experience along with the permutations of possibility. But the results aren’t always smooth and predictable. A stock market driven by algorithms is all fine and well until the market inexplicably loses 1,000 points in a minute thanks to what is now called a flash crash. The algorithms all feeding back to and off one another get caught in a loop, and all of a sudden Accenture is trading at $100,000 a share or Proctor & Gamble goes down to a penny.30 Ironically, and in a perfect expression of present shock, the leading high-frequency trading exchange had a high-profile flash crash on the same day it was attempting to conduct its own initial public offering—and on the same day I was finishing this section of the book. The company, BATS Global Markets, runs a stock exchange called Better Alternative Trading System, which was built specifically to accommodate high-frequency trading and handles over 11 percent of US equities transactions.
BATS issued 6 million shares for about $17 each, and then something went terribly wrong: their system suddenly began executing trades of BATS stock at three and four cents per share.31 Then shares of Apple trading on the BATS exchange suddenly dropped 10 percent, at which point the company halted trading of both ticker symbols. Embarrassed, and incapable of figuring out quite what happened, BATS took the extremely unusual step of canceling its own IPO and giving everyone back their money. What will be the equivalent of a flash crash in other highly compressed arenas where algorithms rule? What does a flash crash in online dating or Facebook friendships look like? What about in criminal enforcement and deterrence, particularly when no one knows how the algorithms have chosen to accomplish their tasks? Algorithmic present shock is instantaneous. Its results impact us before it is even noticed. At least on the stock market, participation in this pressure cooker is optional.
See gaming and Internet analyst Kevin Slavin’s terrific presentation on this history to the Lift11 Conference at www.livestream.com/liftconference/video?clipId=pla_08a3016b-47e9-4e4f-8ef7-ce71c168a5a8. 29. Kevin Slavin, “How Algorithms Shape Our World,” TedTalks, July 2011, www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world.html. 30. Nina Mehta, “Automatic Futures Trade Drove May Stock Crash, Report Says,” Bloomberg Businessweek, October 4, 2010. See also Graham Bowley. “Lone $4.1 Billion Sale Led to ‘Flash Crash’ in May,” New York Times, October 1, 2010. 31. Brian Bremner, “The Bats Affair: When Machines Humiliate their Masters,” Bloomberg Businessweek, March 23, 1012, www.businessweek.com/articles/2012-03-23/the-bats-affair-when-machines-humiliate-their-masters. 32. For the basics, see Alexandra Zendrian, “Don’t Be Afraid of the Dark Pools,” Forbes, May 18, 2009. 33. John Henley, “Greece on the Breadline: Cashless Currency Takes Off,” Guardian, March 16, 2012. 34.
23andMe, Albert Einstein, Alfred Russel Wallace, banking crisis, Barry Marshall: ulcers, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, butterfly effect, Cass Sunstein, cloud computing, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Exxon Valdez, Flash crash, Flynn Effect, hive mind, impulse control, information retrieval, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Schrödinger's Cat, security theater, Silicon Valley, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, Walter Mischel, Whole Earth Catalog
Between 2:42 P.M. and 2:50 P.M. on May 6, 2010, the Dow-Jones Industrial Average experienced a rapid decline and subsequent rebound of nearly six hundred points, an event of unprecedented magnitude and brevity. This disruption occurred as part of a tumultuous event on that day now known as the Flash Crash, which affected numerous market indices and individual stocks, even causing some stocks to be priced at unbelievable levels (e.g., Accenture was at one point priced at $.01). With tick-by-tick data available for every trade, we can watch the crash unfold in slow motion, a film of a financial calamity. But the cause of the crash itself remains a mystery. The U.S. Securities & Exchange Commission report on the Flash Crash was able to identify the trigger event (a $4 billion sale by a mutual fund) but could provide no detailed understanding of why this event caused the crash. The conditions that precipitated the crash were already embedded in the market’s web of causation, a self-organized, rapidly evolving structure created by the interplay of high-frequency trading algorithms.
The conditions that precipitated the crash were already embedded in the market’s web of causation, a self-organized, rapidly evolving structure created by the interplay of high-frequency trading algorithms. The Flash Crash was the birth cry of a network coming to life, eerily reminiscent of Arthur C. Clarke’s science fiction story “Dial F for Frankenstein,” which begins “At 0150 GMT on December 1, 1975, every telephone in the world started to ring.” I’m excited by the scientific challenge of understanding all this in detail, because . . . well, never mind. I guess I don’t really know. The Name Game Stuart Firestein Neuroscientist, chair of the Department of Biological Sciences, Columbia University Too often in science we operate under the principle that “to name it is to tame it,” or so we think. One of the easiest mistakes, even among working scientists, is to believe that labeling something has somehow or other added to an explanation or understanding of it.
., 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.
Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Brian Krebs, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration
“Hedge Funder Spends $75M on Eastchester Manse,” Real Deal, August 1, 2012, http://therealdeal.com/blog/2012/08/01/hedge-funder-spend-75m-on-westchester-manse/. 12. http://www.deshawresearch.com, accessed November 26, 2014. 4. THE GODS ARE ANGRY 1. “Automated Trading: What Percent of Trades Are Automated?” Too Big Has Failed: Let’s Reform Wall Street for Good, April 3, 2013, http://www.toobighasfailed.org/2013/03/04/automated-trading/. 2. Marcy Gordon and Daniel Wagner, “‘Flash Crash’ Report: Waddell & Reed’s $4.1 Billion Trade Blamed for Market Plunge,” Huffington Post, December 1, 2010, http://www.huffingtonpost.com/2010/10/01/flash-crash-report-one-41_n_747215.html. 3. http://rocketfuel.com. 4. Steve Omohundro, “Autonomous Technology and the Greater Human Good,” Journal of Experimental and Theoretical Artificial Intelligence 26, no. 3 (2014): 303–15. 5. CAPTCHA stands for “Completely Automated Public Turing Test to tell Computers and Humans Apart.”
But the root cause is much more sinister—the emergence of invisible electronic agents empowered to take actions on behalf of the narrow self-interests of their owners, without regard to the consequences for the rest of the world. Because these agents are stealthy and incorporeal, we can’t perceive their presence or comprehend their capabilities. We’d be better off with robotic muggers —at least we could see them coming and run away. The “Flash Crash” of 2010 may have caught regulators’ attention, but it did nothing to slow the application of similar techniques to a wide variety of other domains. Any time you buy something, visit a website, or post a comment online, a hidden army of electronic agents, working for someone else, is watching you. Whole industries have sprung up that do nothing but sell weapons in the form of programs and data to companies brave enough to wade into these never-ending melees.
See government specific departments and agencies Federal Housing Administration (FHA), insurance program, 223n10 Federal Reserve Board, 171–72, 174 financial system, 45, 51–53, 57–58, 102 government programs, 168–69, 178 reinvestment vs. spending and, 117. See also assets ownership; stock markets fines, 88, 90 firefighters, 44, 46 First Amendment, 90 Fitch Ratings, 178 Fitzgerald, F. Scott, “The Rich Boy,” 109 “Flash Crash of 2010” (stock market plunge), 8–9, 61–63 Forbes magazine, 109, 113 forged laborers (robots), 3, 5, 35–48 advanced technologies and, 38–48 anthropomorphic bias and, 11, 24, 36–37, 87 capabilities of, 6, 38–48, 84–86, 90, 143 corporate investments in, 177 criminal acts and, 40, 85–86, 90 flexible systems of, 46–48 future uses of, 47–48 as independent agents, 91–92 labor market effects from, 10–12, 13, 134, 143–45 legal responsibility for actions of, 84–85 moral discretion and, 81 need for controls for, 203–4 paired with synthetic intellects, 6, 135 potential ownership of other robots by, 200 primitive precursors to, 39 sensor network for, 42–43, 44 slave status parallel with, 86 401K plans, 182 free market.
3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, computer age, death of newspapers, deferred acceptance, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Kodak vs Instagram, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator
If this is the case, then how many shipwrecks do we need before we stop building ships? Those looking for stories of algorithms run amok can certainly find them with relative ease. On May 6, 2010, the Dow Jones Industrial Average plunged 1,000 points in just 300 seconds—effectively wiping out close to $1 trillion of wealth in a stock market debacle that became known as the Flash Crash. Unexplained to this day, the Flash Crash has been pinned on everything from the impact of high-speed trading to a technical glitch.15 Yet few people would seriously put forward the view that algorithms are, in themselves, bad. Indeed, it’s not simply a matter of algorithms doing the jobs that were once carried out manually; in many cases algorithms perform tasks that would be impossible for a human to perform.
As technologies are invented and prove not to be the end of humanity, they recede into background noise, where they become fodder for further generations of disruptive technology, just as the strongest lions eventually weaken and are overtaken by younger, fitter ones. Confusing the matter further is the complex relationship we enjoy with technology on a daily basis. Like David Ecker, the Columbia Spectator journalist I quoted in the last chapter, most of us hold concerns over “bad” uses of technology, while enjoying everything good technology makes possible. To put it another way, how did I find out the exact details of the Flash Crash I mentioned above? I Googled it. Objectivity in the Post-mechanical Age One topic I continued to butt up against during the writing of this book (and in my other tech writing for publications like Fast Company and Wired) is the subject of objectivity. In each chapter of this book, the subject of objectivity never strayed far from either my own mind or the various conversations I enjoyed with the technologists I had the opportunity to interview.
(Winner) 134 Dodds, Peter 172–76 Dominguez, Jade 25 Dostoyevsky, Fyodor 118 Dourish, Paul 231 Dow Jones 219 drunk driving 142–44 Eagle, Nathan 85 Ecker, David 206–7, 219 eHarmony 71, 74–77, 88 see also Internet; love and sex; Warren, Neil Clark Eisenstein, Sergei 178 Electric Dreams 103 Ellul, Jacques 5, 56 EMD Serono 58 emotion sniffing 51–52 Emotional Optimisation 200–201 Enchanted Loom, The (Jastrow) 96 entertainment, see art and entertainment Epagogix 165–68, 170–72, 176, 179, 191, 203, 205 Eric Berne Memorial Scientific Award 23 Essay on the Moral Statistics of France (Guerry) 117 “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market” 173 Facebook 232, 241 and Facedeals 20 and facial recognition 215 how algorithms work with 2 jobs at 27 profiles, and people’s success 30–31 profiles, traits inferred from 37–38 Timeline feature on 38–39 and YouAreWhatYouLike 37 Facedeals 20 facial recognition and analysis 20, 33, 91, 146, 151, 193, 215 and Internet dating 78 Failing Law Schools (Tamanha) 216 Family Guy 196 Farewell to the Working Class (Gorz) 217–18 Fast Company 3, 35, 128, 220 on Amazon 44–5 Faster Than Thought (Bowden) 184 Faulkner, William 187 Feldman, Konrad 18–19 films, see art and entertainment Filter Bubble, The (Pariser) 47 Fincher, David 189 Find the Love of Your Life (Warren) 73 FindYourFaceMate 78 Fitbit 13 FitnessSingles 78 Flash Crash 219 flexitime 43 Food Stamp Act (US) 154–55 Ford, Henry 44 Foucault, Michel 101 Fourastie, Jean 219 Freud, Sigmund 11 Friedman, Milton 218 Galbraith, Robert 187 Gale, David 62–63, 66 Galton, Francis 31–32 gaming technology 32–33 Gass, John 148 Gates, Bill 182 Geek Logik (Sundem) 67–68 gender reassignment 26 GenePartner 77–78 Generation X (Coupland) 16 Gibson, William 194n Gild 25–26, 29–30 Gillespie, Tarleton 233 Gladwell, Malcolm 211 Goldman, William 161, 173 Good Morning America 67 Google 201–2 and auto-complete 225–27 claimed objectivity of 220–21 differentiated results from 46–48 dynamic-pricing patent granted to 50; see also differential pricing employment practices of 41–42 and facial recognition 215 Flu Trends algorithm of 238–39 how algorithms work with 2 and inadvertent racism 151 and Lake Wobegone Strategy 27–29 Levy’s study of 41 and news-outlet decline 225–27 People Analytics Group within 41–42; see also web analytics and self-driving cars 143, 213 Slate article on 41 and UAL 229 Google Earth 135 Google Glass 14, 26 Google Maps 16, 134–35 Google Street View 227 Google Translate 215, 221 Gorz, André 217 Gottschall, Jonathan 186 Gould, Stephen Jay 33–34 Graf, Daniel 135 graph theory 182 Grindr 89, 152 Guardian 84 Guattari, Félix 48, 54 Guerry, André-Michel 114–18 Gusfield, Joseph 142–43 Halfteck, Guy 32–34 Hansen, Mark 53 Hanson, Curtis 167 Heaven’s Gate 167 Henry VI (Shakespeare) 125–26 Her 103 Hitchcock, Alfred 17 Hogge, Becky 44 Holmes, Katie 68–69 Holmes, Oliver Wendell Jr. 158 Horkheimer, Max 179, 205 House of Cards 188–89 House of Commons, rebuilding of 45 How the Mind Works (Pinker) 80 Human Dynamics (at MIT) 85 Hume, David 199–200 Hunch 37, 234 Hunger Games, The 169 Hutcheson, Joseph C.
Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John von Neumann, knowledge worker, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey
Other systems specialize in finding arbitrage opportunities within or between markets, or in high-frequency trading that seeks to profit from minute price movements that occur over the course of milliseconds (a timescale at which communication latencies even for speed-of-light signals in optical fiber cable become significant, making it advantageous to locate computers near the exchange). Algorithmic high-frequency traders account for more than half of equity shares traded on US markets.69 Algorithmic trading has been implicated in the 2010 Flash Crash (see Box 2). * * * Box 2 The 2010 Flash Crash By the afternoon of May, 6, 2010, US equity markets were already down 4% on worries about the European debt crisis. At 2:32 p.m., a large seller (a mutual fund complex) initiated a sell algorithm to dispose of a large number of the E-Mini S&P 500 futures contracts to be sold off at a sell rate linked to a measure of minute-to-minute liquidity on the exchange. These contracts were bought by algorithmic high-frequency traders, which were programmed to quickly eliminate their temporary long positions by selling the contracts on to other traders.
Possible impacts from genetic selection in different scenarios 7. Some strategically significant technology races 8. Superpowers: some strategically relevant tasks and corresponding skill sets 9. Different kinds of tripwires 10. Control methods 11. Features of different system castes 12. Summary of value-loading techniques 13. Component list List of Boxes 1. An optimal Bayesian agent 2. The 2010 Flash Crash 3. What would it take to recapitulate evolution? 4. On the kinetics of an intelligence explosion 5. Technology races: some historical examples 6. The mail-ordered DNA scenario 7. How big is the cosmic endowment? 8. Anthropic capture 9. Strange solutions from blind search 10. Formalizing value learning 11. An AI that wants to be friendly 12. Two recent (half-baked) ideas 13. A risk-race to the bottom CHAPTER 1 Past developments and present capabilities We begin by looking back.
After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to post facto cancellation under existing trade rules).70 The retelling here of this episode is a digression because the computer programs involved in the Flash Crash were not particularly intelligent or sophisticated, and the kind of threat they created is fundamentally different from the concerns we shall raise later in this book in relation to the prospect of machine superintelligence. Nevertheless, these events illustrate several useful lessons. One is the reminder that interactions between individually simple components (such as the sell algorithm and the high-frequency algorithmic trading programs) can produce complicated and unexpected effects.
The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order by Paul Vigna, Michael J. Casey
3D printing, Airbnb, altcoin, bank run, banking crisis, bitcoin, blockchain, Bretton Woods, California gold rush, capital controls, carbon footprint, clean water, collaborative economy, collapse of Lehman Brothers, Columbine, Credit Default Swap, cryptocurrency, David Graeber, disintermediation, Edward Snowden, Elon Musk, ethereum blockchain, fiat currency, financial innovation, Firefox, Flash crash, Fractional reserve banking, hacker house, Hernando de Soto, high net worth, informal economy, Internet of things, inventory management, Julian Assange, Kickstarter, Kuwabatake Sanjuro: assassination market, litecoin, Long Term Capital Management, Lyft, M-Pesa, Mark Zuckerberg, McMansion, means of production, Menlo Park, mobile money, money: store of value / unit of account / medium of exchange, Network effects, new economy, new new economy, Nixon shock, offshore financial centre, payday loans, peer-to-peer lending, pets.com, Ponzi scheme, prediction markets, price stability, profit motive, RAND corporation, regulatory arbitrage, rent-seeking, reserve currency, Robert Shiller, Robert Shiller, Satoshi Nakamoto, seigniorage, shareholder value, sharing economy, short selling, Silicon Valley, Silicon Valley startup, Skype, smart contracts, special drawing rights, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, supply-chain management, Ted Nelson, The Great Moderation, the market place, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, Turing complete, Tyler Cowen: Great Stagnation, Uber and Lyft, underbanked, WikiLeaks, Y Combinator, Y2K, Zimmermann PGP
-wide gasoline prices from the Energy Information Administration, http://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_nus_w.htm; and bitcoin prices from the CoinDesk Bitcoin Price Index, http://www.coindesk.com/price. New York University professor David Yermack concluded that bitcoin: David Yermack, “Is Bitcoin a Real Currency?,” NBER Working Paper No. 19747, December 2013. You need look no further: CoinDesk Bitcoin Price Index. This included a harrowing “flash crash”: Paul Vigna, “BitBeat: A Bitcoin ‘Flash Crash’ as Volume Spike Briefly Takes Price to $309,” Wall Street Journal, MoneyBeat blog, August 18, 2014, http://blogs.wsj.com/moneybeat/2014/08/18/bitbeat-a-bitcoin-flash-crash-as-volume-spike-briefly-takes-price-to-309/. In a scathing presentation to the New York: Mark T. Williams, “Testimony of Mark T. Williams,” New York State Department of Financial Services, January 28–29, 2014, http://www.dfs.ny.gov/about/hearings/vc_01282014/williams.pdf. “I wouldn’t say hoarding is a bad thing”: Bobby Lee, interviewed by Michael J.
reports put the amount at anywhere from two thousand to half a million coins: For the low-end two thousand: Marc Bevand, “Major Attack on the World’s Largest Bitcoin Exchange,” Zorinaq, June 19, 2011, http://blog.zorinaq.com/?e=55; for the high-end half a million: Jason Mick, “Inside the Mega-Hack of Bitcoin: The Full Story,” DailyTech, June 19, 2011, http://www.dailytech.com/Inside+the+MegaHack+of+Bitcoin+the+Full+Story/article21942.htm. Bitcoin’s prices plunged to meet it: Jack Hough, “Bitcoin’s Flash Crash,” MarketWatch, June 22, 2011, http://blogs.marketwatch.com/paydirt/2011/06/22/bitcoin%E2%80%99s-flash-crash/; also, Tyler Cowan, “The Bitcoin Crash,” Marginal Revolution, http://marginalrevolution.com/marginalrevolution/2011/06/the-bitcoin-crash.html. The fraudulent trades would later be unwound and do not show up in historical price charts, although a chart at Bitcoin Charts, http://bitcoincharts.com/charts/mtgoxUSD#tgCzm1g10zm2g25zv, does show a “double float” of 1.7e+308 in the price columns, for six days after the nineteenth, the time the trades were being unwound.
In any case, a little more than four months after that November peak, the price was plumbing the depths of $344.24 following the collapse of Mt. Gox and amid news in early April of a crackdown by Chinese authorities. Things stabilized somewhat over the summer, but with frequent bouts of what would still be regarded as extreme volatility in any other currency market. This included a harrowing “flash crash” that occurred in mid-August solely on the Bulgaria-based exchange, BTC-e, where the price plunged from $500 to $309 in three minutes before bouncing most of the way back. CoinDesk’s Bitcoin Price Index (Courtesy of CoinDesk) A case can be made that bitcoin’s volatility is unavoidable for the time being. Earning respect and widespread adoption as a currency is a process; it can’t be achieved overnight.
Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity by Douglas Rushkoff
3D printing, Airbnb, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, bank run, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business process, 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, crowdsourcing, cryptocurrency, disintermediation, diversified portfolio, Elon Musk, Erik Brynjolfsson, ethereum blockchain, fiat currency, Firefox, Flash crash, full employment, future of work, gig economy, Gini coefficient, global supply chain, global village, Google bus, Howard Rheingold, IBM and the Holocaust, impulse control, income inequality, index fund, iterative process, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joseph Schumpeter, Kickstarter, loss aversion, Lyft, Mark Zuckerberg, market bubble, market fundamentalism, Marshall McLuhan, means of production, medical bankruptcy, minimum viable product, Naomi Klein, Network effects, new economy, Norbert Wiener, Oculus Rift, passive investing, payday loans, peer-to-peer lending, Peter Thiel, post-industrial society, profit motive, quantitative easing, race to the bottom, recommendation engine, reserve currency, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Snapchat, social graph, software patent, Steve Jobs, TaskRabbit, trade route, transportation-network company, Turing test, Uber and Lyft, Uber for X, unpaid internship, Y Combinator, young professional, Zipcar
For instance, one typical HFT strategy is to provide liquidity in a particular stock until the market shows signs of instability. The algorithm then suddenly withdraws all its bids and offers, leading to an immediate dearth of demand and a precipitous price drop. The smart algorithm, knowing it can make this happen, has already bet against the stock with derivative options. When the other algorithms realize what’s happening, they freeze up, too, leading to a “flash crash.” The stock goes down, but for no real-world reason. It’s just collateral damage from the game itself.31 Another common algorithm strategy is to flood the quote and order systems with fake trades—orders of intent but not full executions—to convince human traders (or other algorithms) that the market is moving in a particular direction. More than 90 percent of all quotes are fake gestures of this sort, generated by computers.32 Algorithms run on ultrafast computers connected as physically close to the stock exchange computers as possible.
It’s an iterative process, in which the algorithms adjust themselves and their activity on every loop, responding less to the news on the ground than to one another. Such systems go out of control because the feedback of their own activity has become louder than the original signal. It’s like when a performer puts a microphone too close to an amplified speaker. It picks up its own feedback, sends it to the speaker, picks it up again, and sends it through again, ad infinitum. The resulting screech is equivalent to the sudden market spike or flash crash created by algorithms iterating their own feedback. Traditional market players scratch their heads at these outlier events because they can’t be explained in terms of trading activity between humans. What made that bubble burst? Was it market sentiment, a piece of news, or something being overbought? None of the usual suspects indicated trouble. That’s why it has become popular to label these gaps in rationality “black swans”—as if they are utterly unpredictable anomalies.
., 229 Circuit City, 90 Citizens United case, 72 Claritas, 32 click workers, 50 climate change, 135, 227–28, 237 coin of the realm, 128–29 collaboration as corporate strategy, 106–7 colonialism, 71–72 commons, 215–23 co-owned networks and, 220–23 history of, 215–16 projects inspired by, 217–18 successful, elements of, 216–17 tragedy of, 215–16 worker-owned collectives and, 219–20 competencies, of corporations, 79–80 Connect+Develop, 107 Consumer Electronics Show, 19 Consumer Reports,33 contracting with small and medium-sized enterprises, 112 cooperative currencies, 160–65 favor banks, 161 LETS (Local Exchange Trading System), 163–65 time dollar systems, 161–63 co-owned networks, 220–23 corporations, 68–82 acquisition of startups, growth through, 78 amplifying effect of, 70, 73 Big Shift and, 76 cash holdings of, 76, 77–78 competency of, 79–80 cost reduction, growth through, 79–80 decentralized autonomous corporations (DACs), 149–50 Deloitte’s study of return on assets (ROA) of, 76–77 distributive alternative to platform monopolies, 93–97 evaluation of, 69–74 extractive nature of, 71–72, 73, 74, 75, 80–82 growth targets, meeting, 68–69 income inequality and, 81–82 limits to corporate model, 75–76, 80–82 managerial and financial methods to deliver growth by, 77–79 monopolies (See monopolies) obsolescence created by, 70–71, 73 offshoring and, 78–79 personhood of, 72, 73–74, 90, 91 recoding of, 93–97, 125–26 repatriation and, 80 retrieval of values of empire and, 71–72, 73 as steady-state enterprises, 97–123 Costco, 74 cost reduction, and corporate growth, 79–80 Couchsurfing.com, 46 crashes of 1929, 99 of 2007, 133–34 biotech crash, of 1987, 6 flash crash, 180 Creative Commons, 215 creative destruction, 83–87 credit, 132–33 credit-card companies, 143–44 crowdfunding, 38–39, 198–201 crowdsharing apps, 45–49 crowdsourcing platforms, 49–50 Crusades, 16 Cumbrian Pounds, 156 Curitiba, Brazil modified LETS program, 164–65 Daly, Herman, 184 data big, 39–44 getting paid for our own, 44–45 “likes” economy and, 32, 34–36 in pre-digital era, 40 Datalogix, 32 da Vinci, Leonardo, 236 debt, 152–54 decentralized autonomous corporations (DACs), 149–50 deflation, 169 Dell, 115–16 Dell, Michael, 115–16 Deloitte Center for the Edge, 76–77 destructive destruction, 100 Detroit Dollars, 156 digital distributism, 224–39 artisanal era mechanisms and values retrieved by, 233–34 developing distributive businesses, 237–38 digital industrialism compared, 226 digital technology and, 230–31 historical ideals of distributism, 228–30 leftism, distinguished, 231 Pope Francis’s encyclical espousing distributed approach to land, labor and capital, 227–28 Renaissance era values, rebirth of, 235–37 subsidiarity and, 231–32 sustainable prosperity as goal of, 226–27 digital economy, 7–11 big data and, 39–44 destabilizing form of digitally accelerated capitalism, creation of, 9–10 digital marketplace, development of, 24–30 digital transaction networks and, 140–51 disproportionate relationship between capital and value in, 9 distributism and, 224–39 externalizing cost of replacing employees in, 14–15 industrialism and, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 industrial society, distinguished, 11 “likes” and similar metrics, economy of, 30–39 platform monopolies and, 82–93, 101 digital industrialism, 13–16, 23–24, 101–2, 201 digital distributism compared, 226 diminishing returns of, 93 externalizing costs and, 14–15 growth agenda and, 14–15, 23–24 human data as commodity under, 44 income disparity and, 53–54 labor and land pushed to unbound extremes by, 214 “likes” economy and, 33 reducing bottom line as means of creating illusion of growth and, 14 digital marketplace, 24–30 early stages of e-commerce, 25–26 highly centralized sales platforms of, 29 initial treatment of Internet as commons, 25 “long tail” of widespread digital access and, 26 positive reinforcement feedback loop and, 28 power-law dynamics and, 26–29 removal of humans from selection process in, 28 digital transaction networks, 140–51 Bitcoin, 143–49, 150–51, 152 blockchains and, 144–51 central authorities, dependence on, 142 decentralized autonomous corporations (DACs) and, 149–50 PayPal, 140–41 theft and, 142 direct public offerings (DPOs), 205–6 discount brokerages, 176–78 diversification, 208, 211 dividends, 113–14, 208–10 dividend traps, 113 Dorsey, Jack, 191–92 Draw Something, 192, 193 Drexler, Mickey, 116 dual transformation, 108–9 dumbwaiter effect, 19 Dutch East India Company, 71, 89, 131 eBay, 16, 26, 29, 45, 140 education industry, 95–97 Eisenhower administration, 52–53, 63, 75 Elberse, Anita, 28 employee-owned companies, 116–18 Enron, 133, 171n Eroski, 220 eSignal, 178 EthicalBay, 221 E*Trade, 176, 177 Etsy, 16, 26, 30 expense reduction, and corporate growth, 78–79 Facebook, 4, 31, 83, 93, 96, 201 data gathering and sales by, 41, 44 innovation by acquisition of startups, 78 IPO of, 192–93, 195 psychological experiments conducted on users by, 32–33 factors of production, 212–14 Fairmondo, 221 Family Assistance Plan, 63 family businesses, 103–4, 231–32 FarmVille, 192 favor banks, 161 Febreze Set & Refresh, 108 Federal Reserve, 137–38 feedback loop, and positive reinforcement, 28 Ferriss, Tim, 201 feudalism, 17 financial services industry, 131–33, 171–73, 175 Fisher, Irving, 158 flash crash, 180 flexible purpose corporations, 119–20 flow, investing in, 208–10 Forbes,88, 173, 174 40-hour workweek, reduction of, 58–60 401(k) plans, 171–74 Francis, Pope, 227, 228, 234 Free, Libre, Open Knowledge (FLOK) program, 217–18 Free (Anderson), 33 free money theory, local currencies based on, 156–59 barter exchanges, 159 during Great Depression, 158–59 self-help cooperatives, 159 stamp scrip, 158–59 tax anticipation scrip, 159 Wörgls, 157–58 frenzy, 98–99 Fried, Jason, 59 Friedman, Milton, 64 Friendster, 31 Frito-Lay, 80 front running, 180–81 Fulfillment by Amazon, 89 Fureai Kippu (Caring Relationship Tickets), 162 Future of Work initiative, 56n Gallo, Riso, 103–4 Gap, 116 Gates, Bill, 186 General Electric, 132 General Public License (GPL) for software, 216 Gesell, Silvio, 157 GI Bill, 99 Gimein, Mark, 147 Gini coefficient of income inequality, 81–82, 92 global warming, 135, 227–28, 237 GM, 80 Goldman Sachs, 133, 195 gold standard, 139 Google, 8, 48, 78, 83, 90–91, 93, 141, 218 acquisitions by, 191 business model of, 37 data sales by, 37, 44 innovation by acquisition of startups, 78 IPO of, 194–95 protests against, 1–3, 5, 98–99 grain receipts, 128 great decoupling, 53 Great Depression, 137, 158–59 Great Exhibition, 1851, 19 Greenspan, Alan, 132–33 growth, 1–11 bazaars, and economic expansion in late Middle Ages, 16–18 central currency and, 126, 129–31, 133–36 digital industrialism, growth agenda of, 14–15, 23–24 highly centralized e-commerce platforms and, 29 startups, hypergrowth expected of, 187–91 as trap (See growth trap) growth trap, 4–5, 68–123 central currency as core mechanism of, 133–34 corporations as program and, 68–82 platform monopolies and, 82–93, 101 recoding corporate model and, 93–97 steady-state enterprises and, 98–123 guaranteed minimum income programs, 62–65 guaranteed minimum wage public jobs, 65–66 guilds, 17 Hagel, John, 76–77 Hardin, Garrett, 215–16 Harvard Business Review,108–9 Heiferman, Scott, 196–97 Henry VIII, King, 215, 229 Hewlett-Packard UK, 112 high-frequency trading (HFT), 179–80 Hilton, 115 Hobby Lobby case, 72 Hoffman, Reid, 61 Holland, Addie Rose, 205–6 holograms, 235 Homeport New Orleans, 121 housing industry, 135 Huffington, Arianna, 34, 35, 201 Huffington Post, 34, 201 human role in economy, 13–67 aristocracy’s efforts to control peasant economy, 17–18 bazaars and, 16–18 big data and, 39–44 chartered monopolies and, 18 decreasing employment and, 30–39 digital marketplace, impact of, 24–30 industrialism and, 13–16, 18–24, 44 “likes” economy and, 30–39 reevaluation of employment and adopting policies to decrease it and, 54–67 sharing economy and, 44–54 Hurwitz, Charles, 117 IBM, 90–91, 112 inclusive capitalism, 111–12 income disparity corporate model and, 81–82 digital technology as accelerating, 53–54 Gini coefficient of, 81–82, 92 growth trap and, 4 power-law dynamics and, 27–28, 30 public service options for reducing, 65–66 IndieGogo, 30, 199 individual retirement accounts (IRAs), 171 industrial farming, 134–35 industrialism, 18–24 branding and, 20 digital, 13–16, 23–24, 44, 53–54, 93, 101–2, 201, 214, 226 disempowerment of workers and, 18–19 human connection between producer and consumer, loss of, 19–20 isolation of human consumers from one another and, 20–21 mass marketing and, 19–20 mass media and, 20–21 purpose of, 18–19, 22 value system of, 18–19 inflation, 169 Instagram, 31 Intercontinental Exchange, 182 interest, 129–31 investors/investing, 70, 72, 168–223 algorithmic trading and, 179–84 bounded, 210–15 commons model for running businesses and, 215–23 crowdfunding and, 198–201 derivative finance, volume of, 182 digital technology and, 169–70, 175–84 direct public offerings (DPOs) and, 205–6 discount brokerages and, 176–78 diversification and, 208, 211 dividends and, 208–10 flow, investing in, 208–10 high-frequency trading (HFT) and, 179–80 in low-interest rate environment, 169–70 microfinancing platforms and, 202–4 platform cooperatives and, 220–23 poor performance of do-it-yourself traders and, 177–78 retirement savings and, 170–75 startups and, 184–205 ventureless capital and, 196–205 irruption, 98 i-traffic, 196 iTunes, 27, 29, 34, 89 J.
3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, millennium bug, natural language processing, Norbert Wiener, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K
Early in the afternoon of Thursday, 6 May 2010 the Dow Jones Industrial Average fell by 6 per cent in a matter of minutes. Not only that, but all kinds of crazy things started happening with stock prices: some fell as low as one cent and others shot through the roof at US$100,000 apiece with no obvious cause. In fifteen nail-biting minutes almost US$1 trillion of market capitalisation was wiped out. Yet five minutes later the Dow was back to normal, as if nothing had happened. The incident became known as the ‘Flash Crash’.28 The causes of it are still highly contested. The official explanation by the Securities and Exchange Commission blames a single badly timed and overly large stock sale. But this is disputed by many experts, who instead point at a set of financial computer technologies called ‘high-frequency trading’ as the true culprit. Basically, what these technologies do is exploit tiny, nanosecond-scale intervals between the placement of an order to buy or sell stock and the actual transaction.
Our only other option is therefore adding yet another layer of complexity, of a non-human kind. What if we had super machines that could watch over us? Machines that monitored other machines and ensured no one spied on our data, machines that defended our vital computer systems and corrected the instabilities whenever they might occur, that guaranteed there could be no more ‘millennium bugs’ or ‘flash crashes’ or a digital apocalypse? What if we had machines that were truly intelligent and that would be our guardians? 15 MACHINES THAT THINK We have come a long way since Aristotle had the insight that logic follows rules. We saw how Boole and Frege pushed this insight further by codifying logic, thus enabling the development of computer languages that code logical rules. In the fullness of time, a torrent of inventions and innovations – such as the electric bulb, electromechanical relays, the transistor and miniaturisation – facilitated the development of advanced electronics.
In this chapter I shall explore what all this means. How close are we to truly intelligent machines – complete with self-awareness? What will the repercussions be for our economy and society as thinking machines begin to replace us in the workplace? Are we in danger of extinction from thinking machines that will one day become self-aware and take over the world – making the millennium bug and the flash crash incidents seem like child’s play? How close are we to the notorious ‘AI Singularity’? The wise men of Dartmouth Artificial Intelligence, as a distinct scientific discipline, was born in the summer of 1956 during a conference on the campus of Dartmouth College in New Hampshire. It was a truly historical event, and those who attended would go on to contribute major innovations in the field of AI in the years to come.
Affordable Care Act / Obamacare, Airbnb, algorithmic trading, barriers to entry, Berlin Wall, bitcoin, Build a better mousetrap, centralized clearinghouse, computer age, crowdsourcing, deferred acceptance, desegregation, experimental economics, first-price auction, Flash crash, High speed trading, income inequality, Internet of things, invention of agriculture, invisible hand, Jean Tirole, law of one price, Lyft, market clearing, market design, medical residency, obamacare, proxy bid, road to serfdom, school choice, sealed-bid auction, second-price auction, second-price sealed-bid, Silicon Valley, spectrum auction, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, The Wealth of Nations by Adam Smith, two-sided market
A famous example, in which high-speed trading of ES futures and SPY exchange-traded funds was implicated, is the “flash crash” of 2010. In just four minutes, the prices of futures and of the related SPY exchange-traded funds (as well as many of the stocks in the index) were driven down by several percentage points—a very big move, in the absence of earth-shattering news—and then recovered almost as fast. A subsequent investigation by the Securities and Exchange Commission and the Commodity Futures Trading Commission suggested that this brief distortion resulted from high-speed computer algorithms trading with one another, at a speed that eluded human supervision, and briefly spun out of control before anyone could react. In the aftermath of this flash crash, there was added confusion involving order backlogs and incorrect time stamps that made it difficult to determine which trades had actually gone through, since even some of the market computers had been left behind by the high-speed traders.
See also college admissions; residency programs for doctors; school matching in democracy, 166 early admissions in, 73–74 exploding offers in, 98–99 marriage age and, 72 Ph.D. offers in, 77–78 public value of, 125 Edwards, Valerie, 130 electronic order books, 83–84 Elias, Julio, 245 email, 169, 175–77 E-mini S&P 500 futures (ES), 82–89 equilibrium, 77 Ethiopia Commodity Exchange, 17–18 experimental economics, 77, 127–28, 176, 209, 244 experiments, 209, 213, 241 expert guides, 147–48 exploding offers, 9–10, 67, 98–99 empowerment of candidates and, 76–80 in gastroenterology fellowships, 76–78 for judicial clerkships, 91–99 in law firm recruiting, 67, 68 to medical residents, 136 for orthopedic surgeon fellows, 78–80 to Ph.D. candidates, 77–78 in school admissions, 73–74 exploitation, 203 failures, market abandonment of, 167 causes vs. symptoms of, 90–98 child marriage and, 70–74 from congestion, 92–93 cultural change and, 78–80 difficulty of limiting, 67–68, 74, 79–80, 90–98 from early transactions, 57–80 exploding offers and, 67–68 finding solutions for, 133–34 in gastroenterology fellowships, 75–78 in judicial clerkships, 69–70, 79, 90–98 in law firm recruiting, 65–68 in orthopedic surgeon hiring, 78–80 prevalence of, 73 safety, trust, and simplicity and, 113–30 self-control and, 67–68, 74–78 from speed, 81–99 fairness, 25 Falke, Roberta, 38–39 Farmer City, Illinois, 115 farmers’ markets, 20, 74 farming, 198 Federal Communications Commission, 186–89 Federal Law Clerk Hiring Plan, 93–98 fellowships, gastroenterology, 75–78 fertility tourism, 201–2 Fiesta Bowl, 61 financial markets, 82–89 flash crash of 2010, 84–85 Fleming, Alexander, 133–34 Florey, Howard, 134 Food Facility Inspection Report, 220–21 Football Bowl Association, 62–63 football bowl games, 59–65 Franklin, Benjamin, 200–201 Fréchette, Guillaume, 64, 237 free markets, 7, 12–13, 217, 226–28. See also markets and marketplaces FreeMarkets, 121–22 futures markets, 16–17, 82–89 Gale, David, 141–43, 158 game theory, 10–11 thought experiments in, 32–33 on trading cycles, 32–41 gaming the system, 10–11 banning markets and, 213–14 in Boston school choice, 126–30 in early transactions, 57–80 in New York City school system, 109–10, 153–55 in the Oklahoma Land Rush, 58–60 gastroenterology fellowships, 75–78 Google, 190–91 Android, 21–22 Great Recession (2008), 66 Green, Jerry, 3–4, 8 Green, Pamela, 3–4 Greiner, Ben, 118 gun ownership, 198 Hamlet (Shakespeare), 200 Hayek, Friedrich, 226–27 health care reimbursement, 206–7, 223–24 for kidney transplants, 51, 206–7, 208–10 health codes, 220–21 Hendren, Hardy, 138, 141 Hil, Garet, 45–46, 49 Hopwood, Shon, 97, 239 horsemeat, 195–97 Hoxby, Caroline, 126 human dignity, 207 IBM, 19 identity theft, 116 immune systems, 133–34 indentured servitude, 199–200 India, 201–2 industry standards, 22 information early transactions and missing, 60 importance of sharing all, 153–61 privacy and, 119–22 on qualifications and interest (See signals and signaling) reliable, 118–19 safety of sharing in Boston Public Schools, 122–28 in clearinghouses, 112 for kidney exchanges, 34, 36, 37, 47–49 market efficiency and, 119–21 for medical residencies, 137–43, 150–51 in New York City school system, 109–10, 112, 153–61 speed of, cotton market and, 89–90 in-kind exchanges, 202–5 Inquiry into the Nature and Causes of the Wealth of Nations, An (Smith), 206–7 insider trading, 48, 85 Institute for Innovation in Public School Choice, 165 interest charges, 200–201, 202, 205 Internet marketplaces, 7, 20–26 Airbnb, 99–103 congestion in, 99–106 dating sites, 72, 169, 175–77 eBay, 104–5, 116–21 payment systems in, 23–26 privacy and, 119–22 real estate, 224–25 reputation in, 115–16, 117–19 safety of, 105 signaling in, 169 targeted ads in, 189–92 thickness of, 105 trust in, 105 Uber, 103–4 Internet of Things, 101 iPhone, 21–22, 24 Iran, Islamic Republic of, 205–6 Iron Law of Marriage, 145 Islam, 200, 201, 205 iStopOver, 102 Japan college applications in, 171 exploding job offers in, 98–99 Jevons, William Stanley, 32 job markets.
Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu
algorithmic trading, asset allocation, automated trading system, backtesting, Black-Scholes formula, Brownian motion, business process, continuous integration, corporate governance, discrete time, distributed generation, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, principal–agent problem, random walk, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process
Recipe for disaster? Albuquerque J 2010. Demsetz H. The cost of transacting. Q J Econ 1968;82:33–53. Easley D, Lopez de Prado MM, O’Hara M. The microstructure of the ‘ﬂash crash:’ ﬂow toxicity, liquidity crashes and the probability of informed trading. J Portfolio Management 2011;37:118–128. Engle RE. The econometrics of ultra-high frequency data. Econometrica 2000;1:1–22. Garman M. Market microstructure. J Financ Econ 1976;3:257–275. Iati R. High frequency trading technology. TABB Group; 2009. Johnson J. Probability and statistics for computer science. John Wiley & Sons Inc.; Hoboken, NJ; 2003. Kirilenko A, Kyle A, Samadi M, Tuzun T. The ﬂash crash: the impact of high frequency trading on an electronic market. Working Paper 2011. Available at http://ssrn.com/abstract=1686004. Mandelbrot B, Hudson RL. The (mis) behavior of markets.
In either case, the high UHFT volume is an economic consequence of trader-agents attempting to exploit arbitrage opportunities in Handbook of Modeling High-Frequency Data in Finance, First Edition. Edited by Frederi G. Viens, Maria C. Mariani, and Ionuţ Florescu. © 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 235 236 CHAPTER 9 A Market Microstructure Model milliseconds—akin to gathering pennies at the rate of some 1000 times per second (1000 ms) or more. UHFT market activities have received critical attention in the analysis of the ‘‘ﬂash-crash’’ of May 6th, 2010, when the Dow Jones fell 573 points in several minutes only to recover 543 points in an even shorter time interval. Market regulators conﬁrmed the event initiated from the automated execution of a single large sell order at the Chicago Mercantile Exchange (CME): the sale of 75,000 E-mini S&P 500 futures contracts.1 The price drop gained momentum as a result of a colossal imbalance in buy/sell orders.
., xiii, 3 Finance, volatility and covolatility measurement/forecasting in, 243 Finance problems, methods used for, 68 Financial Accounting Standards Board (FASB), 53 Financial analysis, using boosting for, 47–74 Financial asset returns, computing covariance of, 263–264 Financial data, 176 behavior of, 202 GH distributions for describing, 165 Financial databases, 62 Financial events observations centered on, 107 probability curves for, 108 Financial market behavior, correlations in, 120 Financial mathematics model, 348 Financial mathematics, Black–Scholes model in, 352 Financial models, with transaction costs and stochastic volatility, 383–419 Financial perspective, 51, 55 Financial returns, 164, 216 Financial sector estimates, 150 Financial time series, 176 long-term memory effects in, 119 Finite-sample performance, via simulations, 14–17 Finite value function, 315, 322 Finite variance, 123 Fitted Gaussian distributions, 22 Fixed frequency, vs. high-low frequency, 208–212 Fixed-frequency approach, drawback of, 183–185 Fixed-frequency density, 210 Index Fixed-frequency method, 200 Fixed-point theorem, 391 applying, 398–399 existence based on, 397 Fixed portfolio/consumption processes, 308 Fixed rare event, favorable price movement for, 32 Fixed stopping time, 307, 308 Fixed time interval, 9 Fixed timescale, risk forecasts on, 176–185 ‘‘Flash-crash’’ of 2010, 236 Flat-top realized kernels, 261 Florescu, Ionu, xiii, 27, 97 Fluctuating memory effect, 145 Forecast horizon, monthly, 196–199 Forecasting of covariance, 280–285 of Fourier estimator properties, 272–285 of volatility, 273–275 Forecast pdfs, 209–210 Forecasts, conﬁdence intervals for, 187–188 Foreign stocks index, 128 Forward index level, calculating, 111–112 Fourier coefﬁcients, 247, 251–252 Fourier covariance estimator, ﬁnite sample properties of, 264 Fourier cutting frequency, 274 Fourier estimator(s), 244–245 asymptotic properties of, 248–250 cutting frequency and, 259–260 forecasting performance of, 245 forecasting properties of, 272–285 gains offered by, 245, 286 of integrated covariance, 263–272 of integrated volatility, 254, 252–263 microstructure noise and, 260–261, 274 of multivariate spot volatility, 246–252 of multivariate volatility, 266 performance of, 273 results of, 276–279 robustness of, 252–253 of volatility of variance and leverage, 250–252 427 Fourier estimator MSE (MSEF ), microstructure noise and, 256.
Planet Ponzi by Mitch Feierstein
Affordable Care Act / Obamacare, Albert Einstein, Asian financial crisis, asset-backed security, bank run, banking crisis, barriers to entry, Bernie Madoff, centre right, collapse of Lehman Brothers, collateralized debt obligation, 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, Flash crash, floating exchange rates, frictionless, frictionless market, high net worth, High speed trading, illegal immigration, income inequality, interest rate swap, invention of agriculture, Long Term Capital Management, moral hazard, mortgage debt, Northern Rock, obamacare, offshore financial centre, oil shock, pensions crisis, Plutocrats, plutocrats, Ponzi scheme, price anchoring, price stability, purchasing power parity, quantitative easing, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, too big to fail, trickle-down economics, value at risk, yield curve
Our weak-willed regulators know these things but aren’t taking the forceful steps that would be needed to control them.28 In short, if that ‘flash crash’ could happen in 2010, it could happen again now: the markets of 2011 and 2012 remain highly vulnerable. As a matter of fact, the same thing already has happened again, and worse. In November 2010, the sugar market saw a 20% collapse in prices over two days. Cotton prices are also exceptionally volatile.29 The same has been true of cocoa futures.30 By good fortune, none of these flash crashes have yet caused much damage, but poorly maintained levees didn’t do much harm to New Orleans until 2005. The mortgage market looked to be working fine, until it came close to destroying the international financial system. In 2010 we were fortunate that the flash crash happened when the markets were still being buoyed up by ultra-low interest rates, by quantitative easing, by massive fiscal stimulus, and by a broad sense of returning security in the financial markets.
In 2010 we were fortunate that the flash crash happened when the markets were still being buoyed up by ultra-low interest rates, by quantitative easing, by massive fiscal stimulus, and by a broad sense of returning security in the financial markets. Those props (disastrous as they’re proving in the longer run) were enough to stop the meltdown. But just suppose the next crash happens when another major financial institution is on the brink. When nerves are shredded. When panic is only half a rumor away. Under these circumstances, a flash crash could easily precipitate failure on a Lehman-like scale. And, with some minor exceptions, all the circumstances which allowed the market to fail in 2010 are still in place today, some of them to an even greater extent. Disaster lies just round the corner. It’s extraordinary that regulators do not pursue these matters more aggressively. Take the relatively simple matter of the $128 billion worth of failed trades each day.
Affordable Care Act / Obamacare, Asian financial crisis, asset allocation, Ayatollah Khomeini, bank run, banking crisis, Ben Bernanke: helicopter money, bitcoin, Black Swan, Bretton Woods, BRICs, business climate, capital controls, Carmen Reinhart, central bank independence, centre right, collateralized debt obligation, collective bargaining, complexity theory, computer age, credit crunch, currency peg, David Graeber, debt deflation, Deng Xiaoping, diversification, Edward Snowden, eurozone crisis, fiat currency, financial innovation, financial intermediation, financial repression, Flash crash, floating exchange rates, forward guidance, George Akerlof, global reserve currency, global supply chain, Growth in a Time of Debt, income inequality, inflation targeting, invisible hand, jitney, Kenneth Rogoff, labor-force participation, labour mobility, Lao Tzu, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market design, money: store of value / unit of account / medium of exchange, mutually assured destruction, obamacare, offshore financial centre, oil shale / tar sands, open economy, Plutocrats, plutocrats, Ponzi scheme, price stability, quantitative easing, RAND corporation, reserve currency, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Satoshi Nakamoto, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, special drawing rights, Stuxnet, The Market for Lemons, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, trade route, uranium enrichment, Washington Consensus, working-age population, yield curve
Strangelove, dealt with nuclear-war-fighting scenarios between the United States and the Soviet Union. As portrayed in these films, neither side wanted war, but it was launched nonetheless due to computer glitches and actions of rogue officers. Capital markets today are anything but fail-safe. In fact, they are increasingly failure-prone, as the Knight Capital incident and the curious May 6, 2010, flash crash demonstrate. A financial attack may be launched by accident during a routine software upgrade or drill. Capital markets almost collapsed in 1998 and 2008 without help from malicious actors, and the risk of a similar collapse in coming years, accidental or malicious, is distressingly high. In 2011 the National Journal published an article called “The Day After” that described in detail the highly classified plans for continuity of U.S. government operations in the face of invasion, infrastructure collapse, or extreme natural disaster.
They morph in the same way a caterpillar turns into a butterfly, a process physicists call “self-organized criticality.” Social systems including capital markets are characterized by such self-organized criticality. One day the stock market behaves well, and the next day it unexpectedly collapses. The 22.6 percent one-day stock market crash on Black Monday, October 19, 1987, and the 7 percent fifteen-minute “flash crash” on May 6, 2010, are both examples of the financial system self-organizing into the critical state; at that point, it takes one snowflake or one sell order to start the collapse. Of course, it is possible to go back after the fact and find a particular sell order that, supposedly, started the market crash (an example of hunting for snowflakes). But the sell order is irrelevant. What matters is the system state
The result will be another systemic and unanticipated failure, larger than the Fed’s capacity to contain it. The panic’s immediate impact will be highly deflationary as assets, including gold, are dumped wholesale to raise cash. This deflationary bout will be followed quickly by inflation, as the IMF pumps out SDRs to reliquefy the system. System crashes. A fifth sign will be more frequent episodes like the May 6, 2010, flash crash in which the Dow Jones Index fell 1,000 points in minutes; the August 1, 2012, Knight Trading computer debacle, which wiped out Knight’s capital; and the August 22, 2013, closure of the NASDAQ Stock Market. From a systems analysis perspective, these events are best understood as emergent properties of complex systems. These debacles are not the direct result of banker greed, but they are the maligned ghost in the machine of high-speed, highly automated, high-volume trading.
Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman
23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, meta analysis, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar
The AP tweet was revealed to be the result of a hack (a pro-Assad group called the Syrian Electronic Army claimed responsibility), and the news organization soon regained control over its account. The incident though showed how closely linked systems—Twitter and stock markets, realtime sentiment analysis and automated trading—can be easily gamed, especially when someone in control of a heavily followed Twitter account clicks on a suspicious link, giving control to an unscrupulous hacker. It wasn’t the first flash crash linked to automatic trading—that honor goes to the May 2010 Flash Crash, in which the Dow lost 1,000 points and swung back to equilibrium a few minutes later—but it was the first in which social media has played such an obvious role. Both Twitter and the AP were criticized for their lax security, and a few months later, Twitter introduced two-factor authentication, a security measure that should make such incidents less likely in the future.
See also hacks and hacking social factories, 264 social graph overview, 10–12 advertisers’ use of, 157 and algorithmic experimentation, 204–6 ambient awareness of others in, 50 and personal scrapbooking, 46 and reputation services, 194 social listening, 35–36, 216–17 social media overview, viii–x, 22, 160–61 and advertising, 23–24, 31–35, 148 and bots, 38–39 as community of choice, 257–58 and context collapse, 290–92 and flash crashes, 39–40 interrelating sites, 161, 246 intolerance of fakery and obfuscation, 74 and journalists, 97, 108, 148 labor markets compared to, 227 and oppressed classes, 5 and prosumption, 270–73 redesigning, 274–77 secondary orality, 63 surveillance on, 129, 133–34, 145–46 utopia predictions, 4–6, 7 See also digital serfdom; metrics; opting out; reputation; sharing economy social media outrage, 108 social media rebellion overview, 345–47, 361–62 devaluing your data, 349–55 digital bill of rights, 296, 365 by F.A.T., 359–62 links with limits on number of views, 358 OCR-proof typeface, 358 regulations allowing the right to be forgotten, 364 removing numbers from Facebook, 358–59 ScareMail browser extension, 359 taking time off, 336–41, 343–44, 347 willful acts of sabotage, 347–49 See also Crockford, Kade; identity obfuscation social media users behavior suggestions from virtual assistant, 42–43 commoditizing ourselves, 60–61 control of your content, 256–57, 258–59, 339 hierarchies in social networks, 53–55 media choices for consumption, 202 narcissism, 61–62 and photography, 55–60 politicians, 149 rating people, 190–92 recognizing when you’re done, 258 and reputation damage, 173 self-awareness, 136 stalkers, 78–82, 142 transparency of, 310 See also sharing social news overview, 101–2 advertorials, 116–17 churnalism, 103–7 curiosity gap headlines, 123–24 hyperbolic headlines, 127 and journalists, 127–28 and narcissism, 119–20 Upworthy, 102, 121–22, 125 See also BuzzFeed Social Roulette (F.A.T.), 360 Social Security number, 283 Social Sentiment Index, IBM, 38–39 social surveillance, 129, 133–34, 145–46 social value of privacy, 286 Soghoian, Christopher, 369 Solove, Daniel J., 286 Sontag, Susan, 55 Sony Entertainment Network, 221 sousveillance, 136–37 speech analytics, 40–43 sponsored content, 28, 31–32, 116–18.
Smarter Than Us: The Rise of Machine Intelligence by Stuart Armstrong
It turns out that it’s incredibly difficult to explain to a computer exactly what we want it to do in ways that allow us to express the full complexity and subtlety of what we want. Computers do exactly what we program them to do, which isn’t always what we want them to do. For instance, when a programmer accidentally entered “/” into Google’s list of malware sites, this caused Google’s warning system to block off the entire Internet!1 Automated trading algorithms caused the May 6, 2010 Flash Crash, wiping out 9% of the value of the Dow Jones within minutes2—the algorithms were certainly doing exactly what they were programmed to do, though the algorithms are so complex that nobody quite understands what that was. The Mars Climate Orbiter crashed into the Red Planet in 1999 because the system had accidentally been programmed to mix up imperial and metric units.3 These mistakes are the flip side of the computer’s relentless focus: it will do what it is programmed to do again and again and again, and if this causes an unexpected disaster, then it still will not halt.
The Filter Bubble: What the Internet Is Hiding From You by Eli Pariser
A Declaration of the Independence of Cyberspace, A Pattern Language, Amazon Web Services, augmented reality, back-to-the-land, Black Swan, borderless world, Build a better mousetrap, Cass Sunstein, citizen journalism, cloud computing, cognitive dissonance, crowdsourcing, Danny Hillis, data acquisition, disintermediation, don't be evil, Filter Bubble, Flash crash, fundamental attribution error, global village, Haight Ashbury, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, jimmy wales, Kevin Kelly, knowledge worker, Mark Zuckerberg, Marshall McLuhan, megacity, Netflix Prize, new economy, PageRank, paypal mafia, Peter Thiel, recommendation engine, RFID, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, social graph, social software, social web, speech recognition, Startup school, statistical model, stem cell, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, the scientific method, urban planning, Whole Earth Catalog, WikiLeaks, Y Combinator
But the more complex and “intelligent” the system gets, the harder it’ll be to tell. Pinpointing where bias or error exists in a human brain is difficult or impossible—there are just too many neurons and connections to narrow it down to a single malfunctioning chunk of tissue. And as we rely on intelligent systems like Google’s more, their opacity could cause real problems—like the still-mysterious machine-driven “flash crash” that caused the Dow to drop 600 points in a few minutes on May 6, 2010. In a provocative article in Wired, editor-in-chief Chris Anderson argued that huge databases render scientific theory itself obsolete. Why spend time formulating human-language hypotheses, after all, when you can quickly analyze trillions of bits of data and find the clusters and correlations? He quotes Peter Norvig, Google’s research director: “All models are wrong, and increasingly you can succeed without them.”
“Digitized Book Project Unveils a Quantitative ‘Cultural Genome,’ ” accessed Feb. 8, 2011, http://www.seas.harvard.edu/news-events/news-archive/2010/digitized-books. 200 “censorship and propaganda”: Ibid. 200 nearly sixty languages: Google Translate Help Page, accessed Feb. 8, 2011, http://translate.google.com/support/?hl=en. 201 better and better: Nikki Tait, “Google to translate European patent claims,” Financial Times, Nov. 29, 2010, accessed Feb. 9, 2010, www.ft.com/cms/s/0/02f71b76-fbce-11df-b79a-00144feab49a.html. 202 “what to do with them”: Danny Sullivan, phone interview with author, Sept. 10, 2010. 202 “flash crash”: Graham Bowley, “Stock Swing Still Baffles, with an Ominous Tone,” New York Times, Aug. 22, 2010, accessed Feb. 8, 2010, www.nytimes.com/2010/08/23/business/23flash.html. 202 provocative article in Wired: Chris Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Wired, June 23, 2008, accessed Feb. 10, 2010, http://www.wired.com/science/discoveries/magazine/16-07/pb_theory. 203 greatest achievement of human technology: Hillis quoted in Jennifer Riskin, Genesis Redux: Essays in the History and Philosophy of Artificial Life (Chicago: University of Chicago Press, 2007), 200. 204 “advertiser-funded media”: Marisol LeBron, “ ‘Migracorridos’: Another Failed Anti-immigration Campaign,” North American Congress of Latin America, Mar. 17, 2009, accessed Dec. 17, 2010, https://nacla.org/node/5625. 205 characters using the companies’ products throughout: Mary McNamara, “Television Review: ‘The Jensen Project,’ ” Los Angeles Times, July 16, 2010, accessed Dec. 17, 2010, http://articles.latimes. com/2010/jul/16/entertainment/la-et-jensen-project-20100716. 205 product-placement hooks throughout: Jenni Miller, “Hansel and Gretel in 3D?
Unhappy Union by The Economist, La Guardia, Anton, Peet, John
bank run, banking crisis, Berlin Wall, Bretton Woods, capital controls, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, credit crunch, Credit Default Swap, debt deflation, Doha Development Round, eurozone crisis, Fall of the Berlin Wall, Flash crash, illegal immigration, labour market flexibility, labour mobility, market fundamentalism, moral hazard, Northern Rock, oil shock, open economy, pension reform, price stability, quantitative easing, special drawing rights, supply-chain management, The Great Moderation, too big to fail, transaction costs, éminence grise
As Greek bonds rose beyond 12%, contagion pushed Irish yields close to 6% and Portuguese ones up above 7%. Stockmarkets around the world slumped as investors fretted about the financial and political stability of a block that made up around a quarter of global output. Save the euro After months of indecision and half measures, the euro was now in mortal danger. The mood of foreboding grew darker still on May 6th 2010, the day of a strange “flash-crash” on Wall Street, in which the Dow Jones Industrial Average collapsed by about 1,000 points before recovering within minutes, perhaps because of a technical glitch. The ECB’s governing council, in Lisbon that day for its monthly meeting, faced a momentous decision: should it start buying sovereign bonds to stop the panic? The Federal Reserve and the Bank of England had been doing so under their policy of quantitative easing to bring down long-term borrowing costs.
., The Passage to Europe, Yale University Press, 2013 Appendix 4 How The Economist saw it at the time May 1st–7th 2010 July 10th–16th 2010 November 20th–26th 2010 December 4th–10th 2010 January 15th–21st 2011 March 12th-18th 2011 June 11th-17th 2011 June 25th-July 1st 2011 October 29th-November 4th 2011 November 5th-11th 2011 November 12th-18th 2011 November 26th-December 2nd 2011 February 18th–24th 2012 March 31st–April 6th 2012 May 19th–25th 2012 May 26th-June 1st 2012 July 28th-August 3rd 2012 August 11th-17th 2012 November 17th-23rd 2012 March 23rd-29th 2013 May 25th–31st 2013 September 14th–20th 2013 October 26th-November 1st 2013 January 4th-10th 2014 Index 1974–75 global recession 10 A accession treaties 112 accountability 125–129, 162 Alliance of Liberals and Democrats for Europe (ALDE) 130–131 Alogoskoufis, George 42 Amsterdam treaty 111–112, 193 Anastasiades, Nicos 2, 86–88 Anglo Irish Bank 53 Ansip, Anders 104 Arab spring 145–146 Argentina 5, 50 Armenia 149 Ashton, Catherine 28, 43, 144 Asmussen, Jörg 51, 82 Austria 111, 127 influence 108 interest rates 93 Azerbaijan 149 Aznar, José Maria 17 B Bagehot, Walter 9 bail-in rules 83, 90–91, 165 see also Cyprus bail-outs national approval requirement 127 no-bail-out rule 45, 162, 163–165 Balkans war 143 Bank of Cyprus 86–87 Bank of England 47, 157 bank recapitalisation 58–59, 74–77, 84 Bankia 72 banking sector characteristics 35 banking supervision see financial supervision banking union 23, 74–75, 77, 83–85, 90–92, 106, 165, 195 see also deposit guarantees; financial supervision Barnier, Michel 41, 138 Barroso, José Manuel early days of crisis 41 European Commission 97, 98, 141, 172 Greece 3, 78 Italy 63 Batista, Paulo Nogueira 46 Belarus 149 Belgium 17, 100, 127 Berlusconi, Silvio euro currency view 151 Italy’s failure to reform 59, 60, 62–63 People of Freedom party (PdL) 107 resignation 64 Black Wednesday 16–17 Blair, Tony 28, 112 BNP Paribas 40 Bolkestein directive 137 bond yields 37, 38, 61, 70, 89 bond spreads 37, 42, 70, 80, 88 Bootle, Roger 1 Bowles, Sharon 98, 129 Brandt, Willy 10 Bretton Woods 9–10 Brown, Gordon 24, 41, 48, 102, 112, 144 Bruegel think-tank 35, 74, 163, 166 budget deficits Maastricht ceiling 15 timescales for meeting targets 88–89 see also stability and growth pact budgets annual, European 21, 27, 118 central 13, 168–170 federal 164, 168 fiscal capacity 84 Bulgaria 108, 113, 124, 126, 147 Bundesbank 16, 23, 157 C Cameron, David 14, 17, 64–65, 117–119, 132, 140 Cannes G20 summit (2011) 62–64 Capital Economics 1 Cassis de Dijon judgment 21 Catalonia 178 CEBS (Committee of European Banking Supervisors) 35 central banks, national 22–23 Centre for European Policy Studies 34 Centre for European Reform 34 CFSP (Common Foreign and Security Policy) 142, 144 China 33, 139, 167 Chirac, Jacques 18, 23, 100, 127 Christofias, Demetris 86 Churchill, Winston 7, 115, 161 Clark, Christopher 178 climate change 135–136 Clinton, Hillary 144 Cockfield, Arthur 13 Committee of European Banking Supervisors (CEBS) 35 Committee of Permanent Representatives (COREPER) 20 Committee of Regions 21 common fisheries policy 100, 138 Common Foreign and Security Policy (CFSP) 142, 144 community method 19, 21–22 Competitiveness Pact see Euro Plus Pact complacency pre-crisis 36–37 Constâncio, Vítor 34 constitution proposals 26–27 convergence criteria 14–16, 41, 112, 193 COREPER (Committee of Permanent Representatives) 20 COSAC (Conference of Community and European Affairs Committees of Parliaments of the European Union) 133 Council of Ministers 20, 121, 130 Council of the European Union see Council of Ministers Court of Auditors 21 Court of First Instance 21 Crafts, Nicholas 9 credit ratings (countries) 69, 77–78, 108 Crimea 150 Croatia 113, 143, 147 current-account (im)balances 25, 31, 88–89, 167–168 customs union, German 9 Cyprus accession 147 bail-out 2, 85–88 entry to euro 112 finances pre-crisis 30 Cyprus Popular Bank (Laiki) 86–88 Czech Republic 113, 118 D Dayton agreement 143 de Gaulle, Charles 9, 22, 96 de Larosière, Jacques 41, 74 Deauville meeting between Sarkozy and Merkel 51–52, 102 debt mutualisation 74, 103, 166–167 defence and security 8, 143, 145 deflation 92 Delors, Jacques 11, 37, 97 Delpla, Jacques 167 democratic accountability 125–129, 162 democratic deficit 121, 129–132, 162–163, 171–172 Denmark European participation 112 justice and home affairs (JHA) 111, 139 ministerial accountability 133 opt-outs 139 referendums 16, 27, 132 shadowing of euro 113 single currency opt-out 110, 115 UK sympathies 119 deposit guarantees 5, 40–41, 74, 77, 91 Deutschmark 10, 12, 16 devaluation, internal 31, 65–66 Dexia 72 Dijsselbloem, Jeroen 24, 87 double majority voting 20, 114 Draghi, Mario 156 appointment as ECB president 23, 68 crisis-management team 2 demand for fiscal compact 64 Long Term Refinancing Operations (LTRO) 68–70 outright monetary transactions (OMT) 78–81 pressure on Berlusconi 59 “whatever it takes” London speech 79 Duisenberg, Wim 23 E e-commerce 137 east–west divide 108 ECB (European Central Bank) bond-buying 47–49, 59–60 crisis-management planning 2, 4 delays 156 European System of Central Banks 22 liquidity provision 40–42, 68–70 outright monetary transactions (OMT) 79–81, 164, 175–176 role and function 22–24, 39–40, 170–171 supervision 6, 99, 175, 195 troika membership 160–161 EcoFin meetings 20, 114 Economic and Financial Committee 20 economic and monetary union (EMU) 11, 112 Economic and Social Committee 21 economic imbalances 30–34 The Economist on ECB responsibilities 15 fictitious memorandum to Angela Merkel 1 ECSC (European Coal and Steel Community) 7–8 EEAS (European External Action Service) 142, 144 EEC (European Economic Community) 8 EFSF (European Financial Stability Facility) 26, 48, 55, 60–61, 81, 194 see also ESM (European Stability Mechanism) EFSM (European Financial Stabilisation Mechanism) 48 Eiffel group 120, 129, 164 elections, European 121, 129–130 Elysée treaty 100 emissions-trading scheme (ETS) 135–136 EMS (European Monetary System) creation of 11 exchange-rate mechanism 16 membership 15 EMU (economic and monetary union) 11, 112 EMU@10 36 energy policies 136 enhanced co-operation 111 enlargement 33, 146–147 environment summits 135 Erdogan, Recep Tayyip 148 ESM (European Stability Mechanism) 194 establishment 26, 55, 80–81 operations 58, 75, 76, 91 Estonia 65, 108 ETS (emissions-trading scheme) 135–136 EU 2020 strategy 137 euro break-up contingency plans 2–3 convergence criteria 14–16, 41, 112, 193 crash danger 47–48 introduction of 4, 18 notes and coins 18 special circumstances 3–4 euro crisis effect on world influence 143–146 errors 155–161 focus of attention 135–141 Euro Plus Pact 55, 195 euro zone 4 economic dangers 175–178 increasing significance of institutions 113–114, 120 performance compared with US 154–155 political dangers 175–178 political integration 125 trust 173 Eurobonds 54, 59, 74, 166–167 Eurogroup of finance ministers 24, 114 European Banking Authority 114, 195 European Central Bank (ECB) bond-buying 47–49, 59–60 crisis-management planning 2, 4 delays 156 European System of Central Banks 22 liquidity provision 40–42, 68–70 outright monetary transactions (OMT) 79–81, 164, 175–176 role and function 22–24, 39–40, 170–171 supervision 6, 99, 175, 195 troika membership 160–161 European Coal and Steel Community (ECSC) 7–8 European Commission commissioners 19, 172 errors 160 future direction 171–172 influence and power 96–97, 99, 119, 125 intrusiveness 127, 140–141 organisation 19 presidency 131, 144 proposals for economic governance 50 European Community 12 European Council 20, 98–99 European Court of Human Rights 21 European Court of Justice 21 European Defence Community 8 European Economic Community (EEC) 8 European External Action Service (EEAS) 142, 144 European Financial Stabilisation Mechanism (EFSM) 48 European Financial Stability Facility (EFSF) 26, 48, 55, 60–61, 81, 194 see also European Stability Mechanism (ESM) European Financial Stability Mechanism 26 see also European Stability Mechanism (ESM) European Investment Bank 21 European Monetary Institute 22 European Monetary System (EMS) creation of 11 exchange-rate mechanism 16 membership 15 European Parliament 20–21, 97–98, 99, 100, 119, 121, 129–132, 171 European People’s Party 117, 127, 130–131 European Political Co-operation 142 European semester 25, 195 European Stability Mechanism (ESM) 194 establishment 26, 55, 80–81 operations 58, 75, 76, 91 European Systemic Risk Board 41 European Union driving forces for monetary union 12–13 expansion 26 historical background 7–12 treaty making 26–28 world influence 140, 142–150 European Union Act (2011) 117, 132 Eurosceptics 13, 123 Finns Party 124 Jobbik 125 League of Catholic Families 125 National Front 124 Party of Freedom (PdL) 124 UK Independence Party (UKIP) 118, 125, 140 excessive deficit procedure 24, 88–89, 194, 195 exchange-rate systems 3, 9–11 exchange rates 164 F Farage, Nigel 98, 118 Federal Deposit Insurance Corporation (FDIC) 77 Federal Reserve (US) 23, 47, 48, 157 federalism 19, 110, 116, 161–165, 168–170, 177–178 financial integration 35–36 financial supervision 195 ECB 6, 99, 175, 195 Jacques de Larosière proposals 41 national 23, 35 single supervisor 76–77, 83–84, 90 Finland accession 26, 111 Finns Party 124 influence 108 ministerial accountability 133 fiscal capacity 84 fiscal compact treaty 25–26, 64–65, 118, 194–195 fiscal policy, focus on 30–31 Five Star Movement 124, 126 fixed exchange-rate systems 3, 9–10 Foot, Michael 116 forecasts, growth 92 foreign policy 142–143 Fouchet plan 22 France credit rating 69, 103 current-account balance 168 EMS exchange-rate mechanism 16 excessive deficit procedure 89 GDP growth 32 and Greece 44 influence 100–104, 142–143 Maastricht deal 12, 16 public debt 159 public opinion of EU 123, 124 single currency views 16–17 unemployment 159 veto of UK entry 115 vote to block European Defence Community 8 freedoms of movement 8, 13 G Gaulle, Charles de 9, 22, 96 Gazprom 136 GDP growth 32 Georgia 149 Germany 2013 elections 90, 106, 125 bond yields 37, 89 Bundesbank 16, 23, 157 constitutional (Karlsruhe) court 45, 95, 128, 158 credit rating 69, 77–78 crisis management errors 155–156 current-account surplus 89, 105, 167–168 demands post Greek bail-out 50–51 economic strengths and weaknesses 14 GDP growth 32 and Greece 44 influence 100–106 Maastricht deal 12, 15–16 national control and accountability 128, 133 parliamentary seats 100 political parties 93, 125 public debt 159 public opinion of EU 123 unemployment 159 unification 16 Zollverein 9 Giscard d’Estaing, Valéry 11, 18, 26, 100 Glienicker group 163, 170 gold standard 9–10 Golden Dawn 124 government spending (worldwide) 4 governments, insolvency of 50 great moderation 31 Greece 2012 election 73, 126 bail-out deal 45–47, 56–58, 65–67, 70, 158 bond yields 37, 61–62 current-account balance 168 debt crisis 42–45 euro membership 18, 112, 115 finances post bail-out 93–94 finances pre-crisis 30, 71 GDP growth 32 potential euro exit 1–5, 81–83 public debt 159, 166 public opinion of EU and euro 113, 123, 124 referendum on bail-out 2, 61–62 unemployment 159 Gros, Daniel 34 H Hague, William 151 Haider, Jörg 127 Hamilton, Alexander 162, 167 Heath, Edward 10, 116 Heisbourg, François 104 Hollande, François 73–74, 89, 103–104, 127 proposed reforms 177 Hungary 41, 113, 126, 147 Hypo Real Estate 41 I Iceland 53, 147 ideological differences 114–115 IKB Deutsche Industriebank 40 immigration 139–140, 146, 147 impossible trinity 13 inter-governmentalism 96, 128, 174 interest rates 93, 164 internal devaluation 31, 65–66 International Monetary Fund (IMF) banking union 74 crisis-management planning 2, 4–5 Cyprus 86–87 errors 160–161 euro zone support 48 Greece 44–46, 56–57, 66, 83, 93–95, 160 Latvia 65 rainy-day funds 169–170 special drawing rights (SDR) 63 Iraq 143 Ireland 89, 110 bail-out 53–54, 56, 57, 89 bank crises 40, 71 bond yields 37, 47, 53, 61, 89 current-account balance 168 finances pre-crisis 30 GDP growth 32 influence 107 opt-outs 111, 139 public debt 159, 166 public opinion of EU 123 referendums 27, 28, 132 unemployment 159 Italy 2013 elections 107, 124, 126 bond yields 37, 61, 89 convergence criteria 17 current-account balance 168 danger of collapse 59 EMS exchange-rate mechanism 16 excessive deficit procedure 89 GDP growth 32 influence 100, 104, 107 interest rates 93 public debt 159, 166 public opinion of EU 123 single currency views 17 unemployment 159 J Jenkins, Roy 11 Jobbik 125 Juncker, Jean-Claude 98, 104, 177 candidate for Commission Presidency 131 EU 2005 budget crisis 28 Eurobonds 54 Eurogroup president 24 justice and home affairs (JHA) 139 K Karamanlis, Kostas 42 Karlsruhe constitutional court 45, 95, 128, 158 Kauder, Volker 105 Kerry, John 144 Kohl, Helmut 12, 18, 100 L labour markets 14, 33–34 Lagarde, Christine 51, 58, 62, 92 Laiki 86–88 Lamers, Karl 111 Lamont, Norman 17 Larosière, Jacques de 41, 74 Latin Monetary Union 9 Latvia 41, 65, 67, 88, 108 Lawson, Nigel 16 League of Catholic Families 125 legislative path 21–22 Lehman Brothers, ECB reaction to collapse 4 Letta, Enrico 107–108 Libya 143, 145 Lipsky, John 57 Lisbon treaty 28, 45, 194 foreign policy 142 institutions 20, 131 justice and home affairs (JHA) 139 subsidiarity 133 voting 20, 114 Lithuania 88, 113, 153 Long Term Refinancing Operations (LTRO) 68–70, 72 Luxembourg 77–78, 100, 108, 169 Luxembourg compromise 97 M Maastricht treaty 11–12, 15, 22, 142, 193 opt-outs and referendums 16, 110–111 MacDougall report (1977) 13, 169 Major, John 12, 111, 116 Malta 100, 112 Maroni, Roberto 34 Mayer, Thomas 1 McCreevy, Charlie 41 MEPs 20–21, 130 Merkel, Angela 2013 re-election 90 banking union 74–77 Cannes G20 summit (2011) 63–64 crisis response 40–41, 44 European constitution 28 fictitious memorandum to 1 future direction 178 power and influence 89, 102–106, 153 Sarkozy collaboration 60, 61–62, 102–103 support for Cyprus 86 support for Greece 5, 45, 49–52, 81–82 support for UK 118–119 union method 22, 128 voter support 125 Messina conference 8, 115 migration 139–140, 146, 147 Miliband, David 144 Mitterrand, François 11, 12, 18, 100 Mody, Ashoka 163 Moldova 149 Monnet, Jean 8, 152 Montebourg, Arnaud 104 Montenegro 147 Monti, Mario 64 influence 70, 75–76, 107 A New Strategy for the Single Market (2010) 137–138 Morocco 146 Morrison, Herbert 8 Morsi, Muhammad 145 Moscovici, Pierre 75 multi-annual financial framework 21, 27, 118 Mundell, Robert 12–13 mutualisation of debt 74, 103, 166–167 N national budgets 89, 125 National Front 124 NATO defence spending targets 145 European security 8 membership 110 Netherlands credit rating 77–78 excessive deficit procedure 89 influence 100, 108 ministerial accountability 133 UK sympathies 119 Nice treaty 194 no-bail-out rule 45, 162, 163–165 north–south divide 33–34, 108 Northern Rock 40 notes and coins 18 Nouy, Danièle 90 Nuland, Victoria 149 O Obama, Barack 63 official sector involvement (OSI) 83 OMT (outright monetary transactions) 79–81, 164, 175–176 Germany’s constitutional court judgment 95, 128 optimal currency-area theory 12–13, 14–15 Orban, Viktor 126 Osborne, George 117, 119 OSI (official sector involvement) 83 outright monetary transactions (OMT) 79–81, 164, 175–176 Germany’s constitutional court judgment 95, 128 P Pact for the Euro see Euro Plus Pact Papaconstantinou, George 43 Papademos, Lucas 64 Papandreou, George 56, 60 election 43 Greek referendum 61–62 resignation 2, 64 Party of Freedom 124 Poland 109, 113 Policy Exchange 1 political parties 124–125, 139–140 political union 10, 12, 133–134 Pompidou, Georges 10 Poos, Jacques 143 Portugal 110 bail-out 54, 57, 89–90 bond yields 37, 47, 53, 61, 89 public opinion of EU and euro 113 power, balance of 99–101 price stability goal of ECB 23 private-sector involvement (PSI) in debt restructuring 51–52 Prodi, Romano 17, 25, 97 Progressive Alliance of Socialists and Democrats (S&D) 130–131 public debt 15, 158–159 see also sovereign debt public opinion of EU and euro 121–124 Putin, Vladimir 149–150 Q qualified-majority voting 13, 20, 99, 121 negative qualified-majority voting 25, 195 quantitative easing (QE) 47, 15 R Rajoy, Mariano 70, 75–76, 127 recapitalisation, bank 58–59, 74–77, 84 redenomination 3–4, 153–154, 175 Reding, Viviane 139 referendums 27, 28, 121–122, 132 REFIT initiative 172 Regling, Klaus 26 Renzi, Matteo 107–108 rescue fund see European Stability Mechanism (ESM) resolution mechanism 90–91, 165, 195 single resolution mechanism (SRM) 195 single supervisory mechanism (SSM) 195 Romania 41, 108, 113, 124, 126, 147 Rome treaty 8, 97, 110, 193 Rösler, Philipp 78 Rueff, Jacques 9 Rumsfeld, Donald 143 Russia, influence on Ukraine 149–150 Rutte, Mark 77 S Samaras, Antonis 2, 78, 82, 93–94 Santer, Jacques 97 Sarkozy, Nicolas crisis response 40–41, 44 economic governance 49–50 European constitution 28 LTROs and the Sarkozy trade 69 Merkel collaboration 51–52, 60, 61–62, 102–103 Schäuble, Wolfgang 62, 75, 84, 90–91, 106, 111, 154 Schengen Agreement 110, 111–112 Schmidt, Helmut 11, 100 Schröder, Gerhard 18, 101, 127 Schulz, Martin 131 Schuman Day 8 Schuman, Robert 7–8 Scotland 112, 178 SDR (special drawing rights) 63 Securities Market Programme (SMP) 48, 79 services directive 34 Shafik, Nemat 65 Sikorski, Radek 109 Simitis, Costas 18 Simms, Brendan 179 single currency benefits 152 club within a club 112 driving forces 12–14 importance of 113 vision for 9 see also euro Single European Act 13, 193 single market 4, 137–138, 174–175 Sinn, Hans-Werner 101 six-pack 25, 50, 195 Slovakia 112 adoption of euro 41 influence 108 Slovenia 88–89, 112 influence 108 SMP (Securities Market Programme) 48, 79 snake in the tunnel 10 Solana, Javier 142 sovereign debt 165–166 see also public debt Spain 110 bail-out 70–73, 89 bank recapitalisation 84 bond yields 37, 89 CDS premiums 72 current-account balance 168 danger of collapse 59 excessive deficit procedure 89 finances pre-crisis 30 GDP growth 32 influence 107 public debt 159 public opinion of EU 123, 124 single currency views 17 unemployment 159 special drawing rights (SDR) 63 stability and growth pact 18, 24, 29, 50–51, 127, 194 Stark, Jürgen 59, 106 Steinbrück, Peer 43 Strauss-Kahn, Dominique 24, 44, 57 stress tests, bank 72, 175 subsidiarity 133, 141 Sweden 109, 111, 112 euro opt-out 18, 115 UK sympathies 119 Syria 145 Syriza 124 T Target II 157 Thatcher, Margaret 27, 110, 116 third energy package 136 Tilford, Simon 34 Tindemans, Leo 111 trade policy 138 Transatlantic Trade and Investment Partnership (TTIP) 138–139 treaty making and change 26–27, 173–174 Treaty of Amsterdam 111–112, 193 Treaty of Lisbon 28, 45, 194 foreign policy 142 institutions 20, 131 justice and home affairs (JHA) 139 subsidiarity 133 voting 20, 114 Treaty of Nice 194 Treaty of Rome 8, 97, 110, 193 Treaty on European Union (Maastricht treaty) 11–12, 15, 22, 142, 193 opt-outs and referendums 16, 110–111 Treaty on Stability, Co-ordination and Governance (TSCG) see fiscal compact treaty Tremonti, Giulio 54, 60 Trichet, Jean-Claude 151, 156 bond-buying 47–48, 52–53 crisis-management planning 2 early warnings 39–40 ECB president 23 IMF 44 Italy 59 True Finns 124 Turkey 132, 147, 148 Tusk, Donald 109, 114 two-pack 25, 89, 195 U UK Independence Party (UKIP) 118, 125, 140 Ukraine 149–150, 179–180 unemployment 158–159, 170 union method 19, 22 United Kingdom current-account balance 168 economic strengths and weaknesses 14 EMS exchange-rate mechanism 16 euro crisis reaction 117–118 euro membership 112 European budget contribution 27–28 European involvement 8, 10, 12, 115–119 future status 174–175 influence 100–101, 106, 109, 142–143 initial application to join EEC 9 opt-outs 110–111, 139 public opinion of EU 123 single currency views 17 United Left party 124 United States abandonment of gold standard 10 federalism model 177 foreign policy 143 performance compared with euro zone 154–155 Urpilainen, Jutta 77 V Van Gend en Loos v Nederlandse Administratie der Belastingen (1963) 21 Van Rompuy, Herman 98 crisis-management planning 3 Cyprus 87 European Council presidency 20, 28 Italy 63 roadmap for integration 74–75, 84, 173 support for Greece 43–45 Venizelos, Evangelos 57, 62 Verhofstadt, Guy 131 Véron, Nicolas 35 Vilnius summit 149 von Weizsäcker, Jakob 166 W Waigel, Theo 17–18 Wall Street flash crash 47 Weber, Axel 49, 56, 106 Weidmann, Jens 40, 80, 82 Weizsäcker, Jakob von 166 Werner report (1971) 10 Wilson, Harold 116 Wolfson Prize 1 World Bank 33 World Trade Organisation 138–139 Y Yanukovych, Viktor 149 Z Zapatero, José Luis Rodríguez 59, 62 Zollverein 9 PublicAffairs is a publishing house founded in 1997. It is a tribute to the standards, values, and flair of three persons who have served as mentors to countless reporters, writers, editors, and book people of all kinds, including me.
Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths
4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Berlin Wall, Bill Duvall, bitcoin, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, delayed gratification, dematerialisation, diversification, double helix, Elon Musk, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, George Akerlof, global supply chain, Google Chrome, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, John Nash: game theory, John von Neumann, knapsack problem, Lao Tzu, linear programming, martingale, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Steve Jobs, stochastic process, Thomas Malthus, traveling salesman, Turing machine, urban planning, Vickrey auction, Walter Mischel, Y Combinator
It turns out that two of the sellers were setting their prices algorithmically as constant fractions of each other: one was always setting it to 0.99830 times the competitor’s price, while the competitor was automatically setting their own price to 1.27059 times the other’s. Neither seller apparently thought to set any limit on the resulting numbers, and eventually the process spiraled totally out of control. It’s possible that a similar mechanism was in play during the enigmatic and controversial stock market “flash crash” of May 6, 2010, when, in a matter of minutes, the price of several seemingly random companies in the S&P 500 rose to more than $100,000 a share, while others dropped precipitously—sometimes to $0.01 a share. Almost $1 trillion of value instantaneously went up in smoke. As CNBC’s Jim Cramer reported live, dumbfounded, “That … it can’t be there. That is not a real price. Oh well, just go buy Procter!
a sale price of more than $23 million: The pricing on this particular Amazon title was noticed and reported on by UC Berkeley biologist Michael Eisen; see “Amazon’s $23,698,655.93 book about flies,” April 23, 2011 on Eisen’s blog it is NOT junk, http://www.michaeleisen.org/blog/?p=358. worsen the irrationality of the market: See, for instance, the reactions of Columbia University economist Rajiv Sethi in the immediate wake of the flash crash. Sethi, “Algorithmic Trading and Price Volatility.” save the entire herd from disaster: This can also be thought of in terms of mechanism design and evolution. It is better on average for any particular individual to be a somewhat cautious herd follower, yet everyone benefits from the presence of some group members who are headstrong mavericks. In this way, overconfidence can be thought of as a form of altruism.
See also randomness San Francisco Sartre, Jean-Paul Saxena, Nitin saying no scale, sorting and scale-free distributions scheduling Schmidt, Eric Schmidt, Peter Schooler, Lael Science Scientific American Scientific Management Scientist in the Crib, The (Gopnik) Seale, Darryl search, gap between verification and search engines search-sort tradeoff self-organizing lists second-chance scenario secretary problem burglar variant full-information variant recall variant rejection variant seeding selfish routing self-organizing lists sequential information processing serendipity Shallit, Jeffrey Shaw, George Bernard Shi, Yong Shoenfield, Joseph shop hours Shortest Processing Time unweighted weighted Shoup, Donald Sibneft oil company Sieve of Erastothenes Silicon Valley Simulated Annealing Sinatra, Frank Single Elimination single-machine scheduling Siroker, Dan size dominance hierarchies and memory hierarchy and sorting and Skype Sleator, Daniel slot machines small data as big data in disguise Smith, Adam Smith, Dan soccer social media Social Network, The (film) social networks social policy socks, sorting software, term coined solid-state drives solitaire sorting Sorting and Searching (Knuth) sort-search tradeoff soy milk space-time tradeoffs SpaceX spinning sports league commissioner overfitting and season scheduling tournament structures Sports Scheduling Group squirrels SRAM standardized tests Statistical Science status pecking order and races vs. fights and Stewart, Martha Steyvers, Mark stock market. See also investment strategies algorithmic trading and flash crash of 2010 storage storytelling Stucchio, Chris sum of completion times sum of weighted completion times sum of weighted lateness of jobs super filing system Tail Drop Tardos, Éva Tarjan, Robert task switching Taylor, Frederick TCP sawtooth. See also Transmission Control Protocol (TCP) teaching to the test technical investors telegraph telephone temperature temporal locality Tenenbaum, Josh tennis tournaments Texas Hold ’Em text messages “TeX Tuneup of 2012, The” (Knuth) Thanksgiving commerce theft, irrational responses and Things a Computer Scientist Rarely Talks About (Knuth) 37% rule Thoreau, Henry David thrashing threading Three Princes of Serendip, The Threshold Rule throughput Tibshirani, Robert Tikhonov, Andrey time interval of timeboxing time costs time management time-space tradeoffs Tolins, Jackson Tomlinson, Ray town size distributions Toxoplasma gondii traffic tragedy of the commons training scars transit systems Transmission Control Protocol (TCP) ACKs and backchannels and flow control and price of anarchy and traveling salesman problem Treat, Tyler “Treatise on the Probability of the Causes of Events” (Laplace) Tree, Jean Fox Trick, Michael triple handshake triple-or-nothing game trip planning.
Pinpoint: How GPS Is Changing Our World by Greg Milner
Ayatollah Khomeini, British Empire, data acquisition, Dava Sobel, Edmond Halley, Eratosthenes, experimental subject, Flash crash, friendly fire, Hedy Lamarr / George Antheil, Internet of things, Isaac Newton, John Harrison: Longitude, Kevin Kelly, land tenure, lone genius, Mars Rover, Mercator projection, place-making, polynesian navigation, precision agriculture, race to the bottom, Silicon Valley, Silicon Valley startup, skunkworks, smart grid, the map is not the territory
The problem, Humphreys maintains, is that the traders’ computers do not have the anti-spoofing protection the exchange applies to its own computers. By jacking directly into the GPS data stream, they leave themselves vulnerable to a spoofed signal that scrambles their computers’ clocks. What might the results of a successful spoofing attack on trading computers look like? Humphreys thinks it could cause a more catastrophic version of the 2010 Flash Crash, a thirty-minute hiccup when the major markets all but collapsed and then quickly rebounded. Though the cause of the crash is still debated, some evidence points to automatic trading programs used for high-frequency trading, which have instructions to pull out of the market if the program senses a problem. In his testimony, Humphreys noted that the crash revealed that many trading algorithms included automatic checks triggered by “unusual latency” in the data coming from exchanges.
., 65, 67 speed estimates, 16 Spiers, Hugo, 133 spread-spectrum systems, 54–56, 77 Springfield, Mo., 89 Sputnik, 30–37, 251 sound signals emitted by, xviii, 36–37, 39 SRI International, 122 Stanford University, 48, 122, 142, 171 GPS Laboratory at, 61, 139, 140, 181 Stewart, Brian, xiii–xiv stimulus-and-response concept, 115–16, 118, 133 stock exchanges, 161–64 automated trading in, 161–62 high-frequency traders in, 161–63 importance of accurate timing in, 162 2010 Flash Crash of, 163 volatility in, 163 storms, 27, 192 Strathclyde, University of, 195 Strebe, Daniel, 241 Streetcorner Research (Schwitzgebel), 174 subatomic particles, xvii, 155–56 sugar beets, 73–75, 101–5, 274 sun, 24, 25, 245 activity on, 28 Earth’s orbit of, 41, 228 energy from, 227 radiation from, 258 Sunda peninsula, 3–4 Super Bowl, XLVII, 192 Supreme Court, German, 186, 187 Supreme Court, U.S., 178–80, 186, 188–91 Survey of the Coast, 248 Swiss Alps, 158 Switzerland, 158–59, 167 Syene (Aswan), 245 Synchronous Grid of Continental Europe, 158–59 Synchrophasors, 159–61, 163 connecting clocks to, 160, 161 Tahiti, 7–9, 10, 13, 24, 106, 263, 264–66 Taiwan, 4 Taliban, 72 Taos, N.Mex., 180 Tasmania, 4 TechRepublic, 191 Tehran, 84 U.S. hostages held in, 77, 87 Tele Atlas company, 242 Telefónica, 192 telematics industry, 183–84 telephones, 59, 156–57, 191 cellular, 95, 119, 183 digital protocols for, 156, 157 integration of GPS devices and, 119, 154, 244 long-distance service on, 81, 157 mobile, 192, 242 multiplexing techniques and, 156 911 calls on, 158 smart, 55, 72, 145, 223, 244 Yellow Pages guide to, 123 telescopes, 29 radio, 209, 261 Terrestrial Environment, The: Solid-Earth and Ocean Physics (Williamstown Report), 208–9 terrorism, 148, 153 possible targets of, 170–72 Terry, Ron, 50–51 Tevake, 11, 18, 264 disappearance of, 13–14, 21 Texas, 40, 42, 230 Texas, University of (UT), 151 Radio Navigation Laboratory at, 150 Texas Instruments, 58, 78–79, 214 theodolites, 251 think tanks, 122, 146 Thompson, Nainoa, 266 3M company, 179 TI-4100 receivers, 214 Timation project, 40, 42–45, 47, 56–57, 153 Tokyo, xv, 48–49, 91, 121, 225 Tolman, Edward, 115–20, 133, 276 TomTom company, 242–45 data-collection vans of, 242–44 mobile phone mapping program of, 242 WGS 84 referencer system used by, 244–45 topography, 124 torpedoes, radio-controlled, 54 Transcontinental Arc, 249 Transit program, 38–40, 42–43, 45, 76, 81, 259, 270 Transportation Department, U.S., xviii, 149, 165 Transworld Data, 191 Trimble, Charlie, 80–81, 83–94, 95–98, 104, 140–41, 211–12, 254 Trimble 4000A GPS, 87 Trimble GPS receivers, 158 Trimble Navigation, 81, 83–88, 93–94, 96–97, 126, 182 Trimpacks, 94, 95–96 Tripoli, 62 troposhere, 227 Tsikada program, 44 tsunamis, 202, 222, 225–26 Tuamotu Archipelago, 12 Tübingen, 130–31 Tuck, Ed, 88–90 Tupaia, 7–11, 269 Cook and, 7–10, 21–24, 26, 263–64, 266 illness and death of, 10, 11 navigational skill of, 8–10 Pacific map of, 10, 13, 14, 263–64 TV-3 rocket, 32–35 Twin Falls, Idaho, 111 Twitter, 194 U-2 reconnaissance aircraft, 67 UNAVCO (nonprofit university-funded consortium), 215, 224 United Kingdom, 27, 104, 156–57, 187–88, 197, 252 100 wealthiest people in, 242 see also England; Scotland United Nations, 63 Soviet delegation to, 35 United Parcel Service (UPS), 143, 184 United States, 9 bureaucracy in, 93 coastlands of, 108, 224 economy and security of, 143 farms in, 104 400 wealthiest Americans in, 127, 239 Great Plains of, 73 infrastructure sectors of, 143, 144–45 Northeastern, 170–72 Northwestern, 202 nuclear strike capabilities of, 62 Southwestern, 59 Soviet relations with, 29–30, 62, 82 Western, 74, 215, 224 United States Standard Datum, 249 United States v.
How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester
asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, Bretton Woods, BRICs, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, Plutocrats, plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Richard Feynman, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, South Sea Bubble, sovereign wealth fund, Steve Jobs, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trickle-down economics, Washington Consensus, working poor, yield curve
History suggests that there are risks in the fact that many of the tricks involved are likely to do the same thing in the same way, and therefore be prone to dramatically exaggerating movements in the markets—remember, equity markets now mainly consist of this kind of trading. It was computer-based portfolio insurance—computer programs all doing the same thing at the same time—that caused the Wall Street crash of October 1987. It seems to have been high-frequency trading that caused the “flash crash” of 6 May 2011, in which the US stock market fell by more than 10 percent and lost $1 trillion of value in less than twenty minutes. But the causes of the flash crash are still not really understood. That, right there, is really alarming.44 HNWI High net worth individual, a reference to a rich person as defined by the financial services industry. The definition is fixed: it means he or she has more than a million dollars in financial assets—meaning assets other than their “residences, collectables, consumer durables and consumables.”
Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, asset-backed security, Atul Gawande, bank run, barriers to entry, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, cloud computing, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, search engine result page, shareholder value, Silicon Valley, Snapchat, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks
The mere existence of an AAA rating, or insurance from AIG, led to a FINANCE’S ALGORITHMS 131 false sense of security for many investors. Here, buy and sell signals can take on a life of their own, leading to momentum trading and herding.124 Algorithmic trading can create extraordinary instability and frozen markets when split-second trading strategies interact in unexpected ways.125 Consider, for instance, the flash crash of May 6, 2010, when the stock market lost hundreds of points in a matter of minutes.126 In a report on the crash, the CFTC and SEC observed that “as liquidity completely evaporated,” trades were “executed at irrational prices as low as one penny or as high as $100,000.”127 Traders had programmed split-second algorithmic strategies to gain a competitive edge, but soon found themselves in the position of a sorcerer’s apprentice, unable to control the technology they had developed.128 Though prices returned to normal the same day, there is no guarantee future markets will be so lucky.
David Golumbia, “High-Frequency Trading: Networks of Wealth and the Concentration of Power,” Social Semiotics 23 (2013): 278–299. 120. HFT often involves “very high order amounts; rapid order cancellation; a flat position at the end of the trading day; extracting very low margins per trade; and trading at ultra-fast speeds.” Andrew J. Keller, “Robocops: Regulating High Frequency Trading after the Flash Crash of 2010,” Ohio State Law Journal 73 (2012): 1459. 121. Matthew O’Brien, “High-Speed Trading Isn’t About Efficiency—It’s About Cheating,” The Atlantic, February 8, 2014. Available at http://www.the atlantic .com /business /archive /2014 /02/high-speed-trading -isnt-about-efficiency-its-about-cheating /283677/; Charles Schwab and Walt Bettinger, “Statement on High-Frequency Trading,” April 3, 2014.
Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari
23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, invention of writing, invisible hand, Isaac Newton, job automation, Kevin Kelly, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!
Three years previously, on 6 May 2010, the New York stock exchange underwent an even sharper shock. Within five minutes – from 14:42 to 14:47 – the Dow Jones dropped by 1,000 points, wiping out $1 trillion. It then bounced back, returning to its pre-crash level in a little over three minutes. That’s what happens when super-fast computer programs are in charge of our money. Experts have been trying ever since to understand what happened in this so-called ‘Flash Crash’. We know algorithms were to blame, but we are still not sure exactly what went wrong. Some traders in the USA have already filed lawsuits against algorithmic trading, arguing that it unfairly discriminates against human beings, who simply cannot react fast enough to compete. Quibbling whether this really constitutes a violation of rights might provide lots of work and lots of fees for lawyers.5 And these lawyers won’t necessarily be human.
Goode’ 257–61, 358, 387, 388 Bible 46; animal kingdom and 76–7, 93–5; Book of Genesis 76–8, 77, Dataism and 381; 93–4, 97; composition of, research into 193–5; evolution and 102; homosexuality and 192–3, 195, 275; large-scale human cooperation and 174; Old Testament 48, 76; power of shaping story 172–3; scholars scan for knowledge 235–6; self-absorption of monotheism and 173, 174; source of authority 275–6; unique nature of humanity, promotes 76–8 biological poverty line 3–6 biotechnology 14, 43–4, 46, 98, 269, 273, 375 see also individual biotech area Bismarck, Otto von 31, 271 Black Death 6–8, 6, 7, 11, 12 Borges, Jorge Luis: ‘A Problem’ 299–300 Bostrom, Nick 327 Bowden, Mark: Black Hawk Down 255 bowhead whale song, spectrogram of 358, 358 brain: Agricultural Revolution and 156–7, 160; artificial intelligence and 278, 278; biological engineering and 44; brain–computer interfaces 48, 54, 353, 359; consciousness and 105–13, 116, 118–19, 121–4, 125; cyborg engineering and 44–5; Dataism and 368, 393, 395; free will and 282–8; happiness and 37, 38, 41; self and 294–9, 304–5; size of 131, 132; transcranial stimulators and manipulation of 287–90; two hemispheres 291–4 brands 156–7, 159–60, 159, 162 Brezhnev, Leonid 273 Brin, Sergey 28, 336 Buddhism 41, 42, 94, 95, 181, 185, 187, 221, 246, 356 Calico 24, 28 Cambodia 264 Cambridge Declaration on Consciousness, 2012 122 capitalism 28, 183, 206, 208–11, 216–17, 218–19, 251–2, 259, 273–4, 369–73, 383–6, 396 see also economics/economy Caporreto, Battle of, 1917 301 Catholic Church 147, 183; Donation of Constantine 190–2, 193; economic and technological innovations and 274; marriage and 26; papal infallibility principle 147, 190, 270–1; Protestant revolt against 185–7; religious intolerance and 198; Thirty Years War and 242, 243, 246; turns from creative into reactive force 274–5 see also Bible and Christianity Ceauçescu, Nicolae 133–4, 134, 135–6, 137 Charlie Hebdo 226 Château de Chambord, Loire Valley, France 62, 62 Chekhov Law 17, 18, 55 child mortality 10, 33, 175 childbirth, narration of 297–8, 297 China 1, 269; biotech and 336; Civil War 263; economic growth and 206, 207, 210; famine in 5, 165–6; Great Leap Forward 5, 165–6, 376; Great Wall of 49, 137–8, 178; liberalism, challenge to 267–8; pollution in 213–14; Taiping Rebellion, 1850–64 271; Three Gorges Dam, building of 163, 188, 196 Chinese river dolphin 188, 196, 395 Christianity: abortion and 189; animal welfare and 90–6; change from creative to reactive force 274–6; economic growth and 205; homosexuality and 192–3, 225–6, 275–6; immortality and 22 see also Bible and Catholic Church Chukwu 47 CIA 57, 160, 293–4 Clever Hans (horse) 128–30, 129 climate change 20, 73, 151, 213, 214–17, 376, 377, 397 Clinton, Bill 57 Clovis, King of France 227, 227 Cognitive Revolution 156, 352, 378 Cold War 17, 34, 149, 206, 266, 372 cold water experiment (Kahneman) 294–5, 338 colonoscopy study (Kahneman and Redelmeier) 296–7 Columbus, Christopher 197, 359, 380 Communism 5, 56, 57, 98, 149, 165, 166, 171, 181; cooperation and 133–7, 138; Dataism and 369, 370–3, 394, 396; economic growth and 206, 207, 208, 217, 218; liberalism, challenge to 264–6, 271–4; religion and 181, 182, 183; Second World War and 263 computers: algorithms and see algorithms; brain–computer interfaces 48, 54, 287, 353, 359; consciousness and 106, 114, 117–18, 119, 120; Dataism and 368, 375, 388, 389 Confucius 46, 267, 391–2; Analects 269, 270 Congo 9, 10, 15, 19, 168, 206, 257–61, 387, 388 consciousness: animal 106–7, 120–32; as biologically useless by-product of certain brain processes 116–17; brain and locating 105–20; computer and 117–18, 119, 120, 311–12; current scientific thinking on nature of 107–17; denying relevance of 114–16; electrochemical signatures of 118–19; intelligence decoupling from 307–50, 352, 397; manufacturing new states of 360, 362–3, 393; positive psychology and 360; Problem of Other Minds 119–20; self and 294–5; spectrum of 353–9, 359, 360; subjective experience and 110–20; techno-humanism and 352, 353–9 cooperation, intersubjective meaning and 143–51, 155–77; power of human 131–51, 155–77; revolution and 132–7; size of group and 137–43 Cope, David 324–5 credit 201–5 Crusades 146–8, 149, 150–1, 190, 227–8, 240, 305 Csikszentmihalyi, Mihaly 360 customer-services departments 317–18 cyber warfare 16, 17, 59, 309–10 Cyborg 2 (movie) 334 cyborg engineering 43, 44–5, 66, 275, 276, 310, 334 Cyrus, King of Persia 172, 173 Daoism 181, 221 Darom, Naomi 231 Darwin, Charles: evolutionary theory of 102–3, 252, 271, 372, 391; On the Origin of Species 271, 305, 367 data processing: Agricultural Revolution and 156–60; Catholic Church and 274; centralised and distributed (communism and capitalism) 370–4; consciousness and 106–7, 113, 117; democracy, challenge to 373–7; economy and 368–74; human history viewed as a single data-processing system 377–81, 388; life as 106–7, 113, 117, 368, 377–81, 397; stock exchange and 369–70; value of human experience and 387–9; writing and 157–60 see also algorithms and Dataism Dataism 366, 367–97; biological embracement of 368; birth of 367–8; computer science and 368; criticism of 393–5; economy and 368–74; humanism, attitude towards 387–8; interpretation of history and 377–80; invisible hand of the data flow, belief in the 385–7; politics and 370–4, 375–6; power/control over data 373–7; privacy and 374, 384–5; religion of 380–5; value of experience and 387–9 Dawkins, Richard 305 de Grey, Aubrey 24, 25, 27 Deadline Corporation 331 death, 21–9 see also immortality Declaration of the Rights of Man and of the Citizen, The 308–9 Deep Blue 320, 320 Deep Knowledge Ventures 322, 323 DeepMind 321 Dehaene, Stanislas 116 democracy: Dataism and 373–5, 376, 377, 380, 391, 392, 396; evolutionary humanism and 253–4, 262–3; humanist values and 226–8; liberal humanism and 248–50, 262–7, 268; technological challenge to 306, 307–9, 338–41 Dennett, Daniel 116 depression 35–6, 39, 40, 49, 54, 67, 122–4, 123, 251–2, 287, 357, 364 Descartes, René 107 diabetes 15, 27 Diagnostic and Statistical Manual of Mental Disorders (DSM) 223–4 Dinner, Ed 360 Dix, Otto 253; The War (Der Krieg) (1929–32) 244, 245, 246 DNA: in vitro fertilisation and 52–4; sequencing/testing 52–4, 143, 332–4, 336, 337, 347–8, 392; soul and 105 doctors, replacement by artificial intelligence of 315, 316–17 Donation of Constantine 190–2, 193 drones 288, 293, 309, 310, 310, 311 drugs: computer-assisted methods for research into 323; Ebola and 203; pharmacy automation and 317; psychiatric 39–41, 49, 124 Dua-Khety 175 dualism 184–5, 187 Duchamp, Marcel: Fountain 229–30, 233, 233 Ebola 2, 11, 13, 203 economics/economy: benefits of growth in 201–19; cooperation and 139–40; credit and 201–5; Dataism and 368–73, 378, 383–4, 385–6, 389, 394, 396, 397; happiness and 30, 32, 33, 34–5, 39; humanism and 230, 232, 234, 247–8, 252, 262–3, 267–8, 269, 271, 272, 273; immortality and 28; paradox of historical knowledge and 56–8; technology and 307–8, 309, 311, 313, 318–19, 327, 348, 349 education 39–40, 168–71, 231, 233, 234, 238, 247, 314, 349 Eguía, Francisco de 8 Egypt 1, 3, 67, 91, 98, 141, 142, 158–62, 170, 174–5, 176, 178–9, 206; Lake Fayum engineering project 161–2, 175, 178; life of peasant in ancient 174–5, 176; pharaohs 158–60, 159, 174, 175, 176; Revolution, 2011 137, 250; Sudan and 270 Egyptian Journalists Syndicate 226 Einstein, Albert 102, 253 electromagnetic spectrum 354, 354 Eliot, Charles W. 309 EMI (Experiments in Musical Intelligence) 324–5 Engels, Friedrich 271–2 Enki 93, 157, 323 Epicenter, Stockholm 45 Epicurus 29–30, 33, 35, 41 epilepsy 291–2 Erdoğan, Recep Tayyip 207 eugenics 52–3, 55 European Union 82, 150, 160, 250, 310–11 evolution 37–8, 43, 73–4, 75, 78, 79–83, 86–7, 89, 102–5, 110, 131, 140, 150, 203, 205, 252–3, 260, 282, 283, 297, 305, 338, 359, 360, 388, 391 evolutionary humanism 247–8, 252–7, 260–1, 262–3, 352–3 Facebook 46, 137, 340–1, 386, 387, 392, 393 famine 1–6, 19, 20, 21, 27, 32, 41, 55, 58, 166, 167, 179, 205, 209, 219, 350 famine, plague and war, end of 1–21 First World War, 1914–18 9, 14, 16, 52, 244, 245, 246, 254, 261–2, 300–2, 301, 309, 310 ‘Flash Crash’, 2010 313 fMRI scans 108, 118, 143, 160, 282, 332, 334, 355 Foucault, Michel: The History of Sexuality 275–6 France: famine in, 1692–4 3–4, 5; First World War and 9, 14, 16; founding myth of 227, 227; French Revolution 155, 308, 310–11; health care and welfare systems in 30, 31; Second World War and 164, 262–3 France, Anatole 52–3 Frederick the Great, King 141–2 free will 222–3, 230, 247, 281–90, 304, 305, 306, 338 freedom of expression 208, 382, 383 freedom of information 382, 383–4 Freudian psychology 88, 117, 223–4 Furuvik Zoo, Sweden 125–6 Future of Employment, The (Frey/Osborne) 325–6 Gandhi, Indira 264, 266 Gazzaniga, Professor Michael S. 292–3, 295 GDH (gross domestic happiness) 32 GDP (gross domestic product) 30, 32, 34, 207, 262 genetic engineering viii, 23, 25, 41, 44, 48, 50, 52–4, 212, 231, 274, 276, 286, 332–8, 347–8, 353, 359, 369 Germany 36; First World War and 14, 16, 244, 245, 246; migration crisis and 248–9, 250; Second World War and 255–6, 262–3; state pensions and social security in 31 Gilgamesh epic 93 Gillies, Harold 52 global warming 20, 213, 214–17, 376, 377, 397 God: Agricultural Revolution and 95, 96, 97; Book of Genesis and 77, 78, 93–4, 97, 98; Dataism and 381, 382, 386, 389, 390, 393; death of 67, 98, 220, 234, 261, 268; death/immortality and 21, 22, 48; defining religion and 181, 182, 183, 184; evolutionary theory and 102; hides in small print of factual statements 189–90, 195; homosexuality and 192–3, 195, 226, 276; humanism and 220, 221, 222, 224, 225, 226, 227, 228, 229, 234–7, 241, 244, 248, 261, 268, 270, 271, 272, 273, 274, 276, 305, 389, 390–1; intersubjective reality and 143–4, 145, 147–9, 172–3, 179, 181, 182, 183, 184, 189–90, 192–3, 195; Middle Ages, as source of meaning and authority in 222, 224, 227, 228, 235–7, 305; Newton myth and 97, 98; religious fundamentalism and 220, 226, 268, 351; Scientific Revolution and 96, 97, 98, 115; war narratives and 241, 244 gods: Agricultural Revolution and theist 90–6, 97, 98, 156–7; defining religion and 180, 181, 184–5; disappearance of 144–5; dualism and 184–5; Epicurus and 30; humans as (upgrade to Homo Deus) 21, 25, 43–9, 50, 55, 65, 66, 98; humanism and 98; intersubjective reality and 144–5, 150, 155, 156–7, 158–60, 161–3, 176, 178–80, 323, 352; modern covenant and 199–200; new technologies and 268–9; Scientific Revolution and 96–7, 98; spirituality and 184–5; war/famine/plague and 1, 2, 4, 7, 8, 19 Google 24, 28, 114, 114, 150, 157, 163, 275, 312, 321, 322, 330, 334–40, 341, 384, 392, 393; Google Baseline Study 335–6; Google Fit 336; Google Flu Trends 335; Google Now 343; Google Ventures 24 Gorbachev, Mikhail 372 Götze, Mario 36, 63 Greece 29–30, 132, 173, 174, 228–9, 240, 265–6, 268, 305 greenhouse gas emissions 215–16 Gregory the Great, Pope 228, 228 guilds 230 hackers 310, 313, 344, 382–3, 393 Hadassah Hospital, Jerusalem 287 Hamlet (Shakespeare) 46, 199 HaNasi, Rabbi Yehuda 94 happiness 29–43 Haraway, Donna: ‘A Cyborg Manifesto’ 275–6 Harlow, Harry 89, 90 Harris, Sam 196 Hassabis, Dr Demis 321 Hattin, Battle of, 1187 146, 147 Hayek, Friedrich 369 Heine, Steven J. 354–5 helmets: attention 287–90, 362–3, 364; ‘mind-reading’ 44–5 Henrich, Joseph 354–5 Hercules 43, 176 Herodotus 173, 174 Hinduism 90, 94, 95, 181, 184, 187, 197, 206, 261, 268, 269, 270, 348, 381 Hitler, Adolf 181, 182, 255–6, 352–3, 375 Holocaust 165, 257 Holocene 72 Holy Spirit 227, 227, 228, 228 Homo deus: Homo sapiens upgrade to 43–9, 351–66; techno-humanism and 351–66 Homo sapiens: conquer the world 69, 100–51; end famine, plague and war 1–21; give meaning to the world 153–277; happiness and 29–43; Homo deus, upgrade to 21, 43–9; immortality 21–9; loses control, 279–397; problems with predicting history of 55–64 homosexuality 120, 138–9, 192–3, 195, 225–6, 236, 275 Hong Xiuquan 271 Human Effectiveness Directorate, Ohio 288 humanism 65–7, 98, 198, 219; aesthetics and 228–9, 228, 233, 233, 241–6, 242, 245; economics and 219, 230–1, 232, 232; education system and 231, 233, 233, 234; ethics 223–6, 233; evolutionary see evolutionary humanism; formula for knowledge 237–8, 241–2; homosexuality and 225–6; liberal see liberal humanism; marriage and 223–5; modern industrial farming, justification for 98; nationalism and 248–50; politics/voting and 226–7, 232, 232, 248–50; revolution, humanist 220–77; schism within 246–57; Scientific Revolution gives birth to 96–9; socialist see socialist humanism/socialism; value of experience and 257–61; techno-humanism 351–66; war narratives and 241–6, 242, 245, 253–6; wars of religion, 1914–1991 261–7 hunter-gatherers 34, 60, 75–6, 90, 95, 96–7, 98, 140, 141, 156, 163, 169, 175, 268–9, 322, 355, 360, 361, 378 Hussein, Saddam 18, 310 IBM 315–16, 320, 330 Iliescu, Ion 136, 137 ‘imagined orders’ 142–9 see also intersubjective meaning immigration 248–50 immortality 21–9, 30, 43, 47, 50, 51, 55, 56, 64, 65, 67, 138, 179, 268, 276, 350, 394–5 in vitro fertilisation viii, 52–3 Inanna 157, 323 India: drought and famine in 3; economic growth in modern 205–8, 349; Emergency in, 1975 264, 266; Hindu revival, 19th-century 270, 271, 273; hunter-gatherers in 75–6, 96; liberalism and 264, 265; population growth rate 205–6; Spanish Flu and 9 individualism: evolutionary theory and 103–4; liberal idea of undermined by twenty-first-century science 281–306; liberal idea of undermined by twenty-first-century technology 327–46; self and 294–304, 301, 303 Industrial Revolution 57, 61, 270, 274, 318, 319, 325, 374 inequality 56, 139–43, 262, 323, 346–50, 377, 397 intelligence: animal 81, 82, 99, 127–32; artificial see artificial intelligence; cooperation and 130–1, 137; decoupling from consciousness 307–50, 352, 397; definition of 130–1; development of human 99, 130–1, 137; upgrading human 348–9, 352 see also techo-humanism; value of consciousness and 397 intelligent design 73, 102 internet: distribution of power 374, 383; Internet-of-All-Things 380, 381, 382, 388, 390, 393, 395; rapid rise of 50 intersubjective meaning 143–51, 155–77, 179, 323, 352 Iraq 3, 17, 40, 275 Islam 8, 18, 21, 22, 64, 137, 188, 196, 205, 206, 207, 221, 226, 248, 261, 268, 269, 270, 271, 274, 275, 276, 351, 392; fundamentalist 18, 196, 226, 268, 269, 270, 275, 351 see also Muslims Islamic State (IS) 275, 351 Isonzo battles, First World War 300–2, 301 Israel 48, 96, 225–6, 249 Italy 262, 300–2, 301 Jainism 94–5 Jamestown, Virginia 298 Japan 30, 31, 33, 34, 207, 246, 349 Jefferson, Thomas 31, 192, 249, 282, 305 Jeopardy!
The New Depression: The Breakdown of the Paper Money Economy by Richard Duncan
asset-backed security, bank run, banking crisis, banks create money, Ben Bernanke: helicopter money, Bretton Woods, currency manipulation / currency intervention, debt deflation, deindustrialization, diversification, diversified portfolio, fiat currency, financial innovation, Flash crash, Fractional reserve banking, income inequality, inflation targeting, Joseph Schumpeter, laissez-faire capitalism, liquidity trap, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, money: store of value / unit of account / medium of exchange, mortgage debt, private sector deleveraging, quantitative easing, reserve currency, Ronald Reagan, savings glut, special drawing rights, The Great Moderation, too big to fail, trade liberalization
In early May, the results of the bank stress test came as a relief to markets. Nonetheless, the impact on stock prices of the creation and injection of $1.75 trillion in new fiat money into the credit market should not be underappreciated–particularly considering movements in stock prices after QE1 came to an end. Quantitative Easing: Round Two Five weeks after QE1 ended on March 31, 2010, the U.S. stock market experienced a flash crash when, in one day, stock prices plummeted 10 percent before recovering to close down only 3 percent on the day. By early July the stock market was down 14 percent from its post-QE1 peak of 11,205 on April 26. That drop destroyed trillions of dollars in paper wealth, producing a negative wealth effect that immediately impacted consumption. The 2010 “soft patch” had begun. By August, most economic indicators were flashing red, and concerns over the risks of a double-dip recession began to take hold.
3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, Peter Thiel, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E
High-frequency trading, where computers trade with each other at speeds no human can even follow – never mind participate in – took off in the early 21st century, although it has reportedly fallen back from around two-thirds of all US equity trades at the start of the 2008 credit crunch to around 50% in 2012. (8) There is still confusion about the impact of this on the financial markets. The “flash crash” of 2010, in which the Dow Jones lost almost 10% of its value in a few minutes was initially blamed on high-frequency trading, but later reports claimed that the AIs had actually mitigated the fall. The crash prompted the New York Stock Exchange to introduce “circuit breakers” which suspend trading of a stock whose price moves suspiciously quickly. The financial Armageddon which some pundits forecast has not arrived, and although there will undoubtedly be further shocks to the system, most market participants expect that new AI tools will continue to be developed for and absorbed by what has always been one of the most dynamic and aggressive sectors of the economy.
Geek Sublime: The Beauty of Code, the Code of Beauty by Vikram Chandra
Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Apple II, barriers to entry, Berlin Wall, British Empire, business process, conceptual framework, create, read, update, delete, crowdsourcing, East Village, European colonialism, finite state, Firefox, Flash crash, glass ceiling, Grace Hopper, haute couture, iterative process, Jaron Lanier, John von Neumann, land reform, London Whale, Paul Graham, pink-collar, revision control, Silicon Valley, Silicon Valley ideology, Skype, Steve Jobs, Steve Wozniak, theory of mind, Therac-25, Turing machine, wikimedia commons, women in the workforce
Every programmer is familiar with the most infamous bugs: the French Ariane 5 rocket that went off course and self-destructed forty seconds after lift-off because of an error in converting between representations of number values; the Therac-25 radiation therapy machine that reacted to a combination of operator input and a “counter overflow” by delivering doses of radiation a hundred times more intense than required, resulting in the agonizing deaths of five people and injuries to many others; the “Flash Crash” of 2010, when the Dow Jones suddenly plunged a thousand points and recovered just as suddenly, apparently as a result of automatic trading programs reacting to a single large trade. These are the notorious bugs, but there are bugs in every piece of software that you use today. A professional “cyber warrior,” whose job it is to find and exploit bugs for the US government, recently estimated that “most of the software written in the world has a bug every three to five lines of code.”18 These bugs may not kill you, but they cause your system to freeze, they corrupt your data, and they expose your computers to hackers.
The Only Game in Town: Central Banks, Instability, and Avoiding the Next Collapse by Mohamed A. El-Erian
Airbnb, balance sheet recession, bank run, barriers to entry, Bretton Woods, British Empire, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, collapse of Lehman Brothers, corporate governance, currency peg, Erik Brynjolfsson, eurozone crisis, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, Flash crash, forward guidance, friendly fire, full employment, future of work, Hyman Minsky, If something cannot go on forever, it will stop, income inequality, inflation targeting, Jeff Bezos, Kenneth Rogoff, Khan Academy, liquidity trap, Martin Wolf, megacity, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, oil shale / tar sands, price stability, principal–agent problem, quantitative easing, risk tolerance, risk-adjusted returns, risk/return, Second Machine Age, secular stagnation, sharing economy, sovereign wealth fund, The Great Moderation, The Wisdom of Crowds, too big to fail, University of East Anglia, yield curve
The advanced economies’ failure to grow nominal GDP has made life a lot more difficult for emerging countries—a phenomenon that accentuates not only their internal challenges but also the trials of navigating a global financial system that is inevitably distorted by the pursuit of unconventional policies and is periodically subject to discomforting bouts of financial instability. Examples include the “taper tantrum” of May–June 2013, the U.S. Treasury “flash crash” in October 2014, the Swiss currency shock of January 2015, the volatility in German bond rates in May–June 2015, and the surprise China currency move in August 2015—all of which highlighted the combined impact of disorderly unwinds by levered traders, little appetite for risk taking among broker-dealers and other intermediaries, and a tendency for some end investors to head quickly to the door at the sign of trouble.
Swimming With Sharks: My Journey into the World of the Bankers by Joris Luyendijk
bank run, barriers to entry, Bonfire of the Vanities, bonus culture, collapse of Lehman Brothers, collective bargaining, credit crunch, Credit Default Swap, Emanuel Derman, financial deregulation, financial independence, Flash crash, glass ceiling, Gordon Gekko, high net worth, hiring and firing, inventory management, job-hopping, London Whale, Nick Leeson, offshore financial centre, regulatory arbitrage, shareholder value, sovereign wealth fund, the payments system, too big to fail
As a last resort people actually rip the cable out of the computer if things start to go wrong, the compliance officer went on. ‘I’ve seen that happen, but it seems ridiculously primitive in such technological environments.’ A number of back- and middle-office workers claimed to have witnessed high frequency trading computers being disconnected: after a tsunami, an unusually large terrorist attack or the sudden prospect of a Greek default. What if the plug cannot be pulled in time? In the ‘flash crash’ of 6 May 2012, share prices suddenly lost hundreds and hundred of points in a matter of minutes. Then, as quickly and inexplicably as they had crashed, prices recovered – while the world of finance looked on in terrified bewilderment. Complexity can render even insiders helpless, explained the ‘head of structured credit’ at a big bank. In his mid thirties, slightly restless but quick to laugh, he is one of the most good-natured people I have met in two years of researching.
Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen
Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, New Journalism, oil shock, p-value, passive investing, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond
But again, a careful study of expected return might have easily, perhaps even more easily, led to the same protection by avoiding certain investments on the ground that their expected returns were too low. Frankly, I think a lot of the answer is that discussing risk is inherently sexier than discussing expected returns. Good forecasts of expected return add up over the long term, but don’t matter for squat next week. Risk can kill you, or make you a hero, in the time it takes you to say “flash crash”. Would you rather write about a tsunami or erosion? More specifically, from a safe perch, say the hindsight of authoring a book, which event would be more dramatic to narrate? The analogy, forced though it may be, continues to work as erosion, or expected return, might be mind-numbingly boring at any one moment, but it has a heck of a lot to say about how the future will be shaped. For perspective, you want to see a black swan?
For example, the New York Stock Exchange has lost market share in U.S. equity turnover to various electronic platforms and dark pools. Market-makers used to be the only explicit liquidity providers but they have increasingly been superseded by hedge funds and other stat arb traders. High-frequency traders are important suppliers of short-term liquidity, but they are accused of being “fair-weather liquidity providers” who leave the market when problems arise (witness the Flash Crash in May 2010). Figure 18.7 plots the histories of an illiquidity proxy and the gross annual profits of a liquidity provision strategy from a study by Rinne–Suominen (2010). (The illiquidity proxy is based on the idea that frequent return reversals are indicative of an asset’s illiquidity as buying or selling demand causes a larger temporary price concession for less liquid assets. Estimates of return reversal tendencies for single stocks are then aggregated to get a proxy of market-wide illiquidity.)
The Unwinding: An Inner History of the New America by George Packer
Affordable Care Act / Obamacare, Apple's 1984 Super Bowl advert, bank run, big-box store, citizen journalism, cleantech, collateralized debt obligation, collective bargaining, Credit Default Swap, credit default swaps / collateralized debt obligations, deindustrialization, diversified portfolio, East Village, El Camino Real, Elon Musk, family office, financial independence, financial innovation, Flash crash, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, housing crisis, income inequality, informal economy, Jane Jacobs, life extension, Long Term Capital Management, low skilled workers, margin call, Mark Zuckerberg, market bubble, market fundamentalism, Maui Hawaii, Menlo Park, new economy, New Journalism, obamacare, Occupy movement, oil shock, peak oil, Peter Thiel, Ponzi scheme, Richard Florida, Ronald Reagan, Ronald Reagan: Tear down this wall, shareholder value, side project, Silicon Valley, Silicon Valley startup, single-payer health, smart grid, Steve Jobs, strikebreaker, The Death and Life of Great American Cities, the scientific method, too big to fail, union organizing, urban planning, We are the 99%, We wanted flying cars, instead we got 140 characters, white flight
But as Wall Street aggressively fought any but the smallest changes, inertia set in at the SEC, and, once again, nothing happened. May 6, 2010, was the day when Connaughton’s second life in government began to end. In the early afternoon, the stock market suddenly plummeted seven hundred points in eight minutes before reversing itself, with the momentary disappearance of almost a trillion dollars in wealth. The flash crash, as it came to be called, was caused by the kind of automated trading that Kaufman had warned about. A few hours later, Kaufman was sitting in the presiding officer’s chair when Mark Warner, the Virginia Democrat, explained to the Senate what had just happened. “I have become a believer,” he said, and invited Kaufman to come down to the floor and essentially say to the world, “I told you so”—which Kaufman did.
While we disagree with one another on substantive issues, we can enjoy each other’s company on a personal level, a civil level.” And Senator Dodd wished Senator Shelby a very happy birthday. Later that night, Kaufman returned to his office in Russell. Connaughton asked him what he should put into a press release. Kaufman could muster only three words: “I am disappointed.” They had known it was doomed, but the size of their defeat was devastating. In the span of a few hours, they had been vindicated by the flash crash, then thoroughly whipped on too big to fail. The southerner in Connaughton, the romantic believer in lost causes, told the staff, “Some things are worth fighting for.” * * * On May 21, the Dodd bill passed the Senate, and on July 21, President Obama signed the Dodd-Frank Wall Street Reform and Consumer Protection Act into law. The Volcker Rule was now a ghost of itself, with the details left to regulators.
The Clash of the Cultures by John C. Bogle
asset allocation, collateralized debt obligation, corporate governance, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, estate planning, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, Flash crash, Hyman Minsky, income inequality, index fund, interest rate swap, invention of the wheel, market bubble, market clearing, mortgage debt, new economy, Occupy movement, passive investing, Ponzi scheme, principal–agent problem, profit motive, random walk, rent-seeking, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, statistical arbitrage, The Wealth of Nations by Adam Smith, transaction costs, Vanguard fund, William of Occam
In theory, this gives investors all the tools they need to design the portfolio which closely matches their risk preferences and economic outlook.” Not What It Says on the Tin “One risk relates to liquidity. In some sectors, like emerging markets, it is easier for investors to buy and sell an ETF than to trade in the underlying illiquid assets. But the liquidity risk has not gone away. During the market turmoil known as the “flash crash” in May 2010, the Dow Jones Industrial Average briefly dropped 1,000 points as liquidity evaporated: 60–70 percent of the trades that subsequently had to be canceled were in ETFs. “Another risk concerns a change in the nature of ETFs—the development of “synthetic ETFs” and related products such as exchange-traded notes (ETNs). These funds do not own assets like shares or bonds; instead they arrange a derivative deal with an investment bank, which guarantees to deliver the return of the targeted benchmark, exposing the ETF investor to the risk that the bank fails to pay up. . . .
asset allocation, Bernie Madoff, Cass Sunstein, Credit Default Swap, David Brooks, delayed gratification, diversification, diversified portfolio, Donald Trump, Elliott wave, en.wikipedia.org, estate planning, financial innovation, Flash crash, game design, greed is good, high net worth, impulse control, income inequality, index fund, London Whale, Mark Zuckerberg, mortgage debt, oil shock, payday loans, pension reform, Ponzi scheme, quantitative easing, Ralph Nader, RAND corporation, random walk, Richard Thaler, Ronald Reagan, Saturday Night Live, too big to fail, transaction costs, Unsafe at Any Speed, upwardly mobile, Vanguard fund, wage slave, women in the workforce, working poor, éminence grise
Schiff is still something of a joke to many, but others, like Roubini, who predicted a major bank collapse just weeks before Bear Stearns went under, and Swiss-born Hong Kong–residing investment manager Marc Faber who, demonstrating his sly sense of humor, named his investment newsletter the Gloom Boom & Doom Report, are well-respected by most of the establishment. Needless to say, bad times are almost always good times for anyone predicting economic Armageddon. Behavioral finance experts explain this by claiming what they call the recency effect; that is, our natural human bias to overemphasize the recent past over other experiences (in other words, goodbye Internet bubble, hello Flash Crash!). I suspect something else, however. There can be a strange comfort in economic Armageddon. Gurus with their doomsday scenarios bring an odd sort of order to what otherwise could seem like a random series of ghastly events. According to them, all this bad stuff is happening for a reason. And if you understand the reason, their sales pitch goes, you can be protected from the disaster to come.
The Glass Cage: Automation and Us by Nicholas Carr
Airbnb, Andy Kessler, Atul Gawande, autonomous vehicles, business process, call centre, Captain Sullenberger Hudson, Checklist Manifesto, cloud computing, David Brooks, deliberate practice, deskilling, Elon Musk, Erik Brynjolfsson, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, global supply chain, Google Glasses, Google Hangouts, High speed trading, indoor plumbing, industrial robot, Internet of things, Jacquard loom, Jacquard loom, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, knowledge worker, Lyft, Mark Zuckerberg, means of production, natural language processing, new economy, Nicholas Carr, Norbert Wiener, Oculus Rift, pattern recognition, Peter Thiel, place-making, Plutocrats, plutocrats, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, TaskRabbit, technoutopianism, The Wealth of Nations by Adam Smith, Watson beat the top human players on Jeopardy!
Miscalculations of risk, exacerbated by high-speed computerized trading programs, played a major role in the near meltdown of the world’s financial system in 2008. As Tufts University management professor Amar Bhidé has suggested, “robotic methods” of decision making led to a widespread “judgment deficit” among bankers and other Wall Street professionals.20 While it may be impossible to pin down the precise degree to which automation figured in the disaster, or in subsequent fiascos like the 2010 “flash crash” on U.S. exchanges, it seems prudent to take seriously any indication that a widely used technology may be diminishing the knowledge or clouding the judgment of people in sensitive jobs. In a 2013 paper, computer scientists Gordon Baxter and John Cartlidge warned that a reliance on automation is eroding the skills and knowledge of financial professionals even as computer-trading systems make financial markets more risky.21 Some software writers worry that their profession’s push to ease the strain of thinking is taking a toll on their own skills.
The Fear Index by Robert Harris
algorithmic trading, backtesting, banking crisis, dark matter, family office, Fellow of the Royal Society, fixed income, Flash crash, high net worth, implied volatility, mutually assured destruction, Renaissance Technologies, speech recognition
and saw Hoffmann with fire coming out of his fingers. Not now, he thought, don’t do it yet – not till VIXAL has finished its trades. Beside him Gabrielle screamed, ‘Alex!’ Quarry flung himself towards the door. The fire left Hoffmann’s hand, seemed to dance in the air for an instant, and then expanded into a brilliant bursting star. THE SECOND AND decisive liquidity crisis of the seven-minute ‘flash crash’ had begun just as Hoffmann dropped the empty jerry can, at 8.45 p.m. Geneva time. All over the world investors were watching their screens and either ceasing to trade or selling up altogether. In the words of the official report: ‘Because prices simultaneously fell across many types of securities, they feared the occurrence of a cataclysmic event of which they were not aware, and which their systems were not designed to handle … A significant number withdrew completely from the markets.’
3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, banking crisis, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, computer age, debt deflation, deskilling, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Khan Academy, knowledge worker, labor-force participation, labour mobility, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, performance metric, Peter Thiel, Plutocrats, plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Richard Feynman, Rodney Brooks, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce
Likewise, automated trading algorithms are now responsible for nearly two-thirds of stock market trades, and Wall Street firms have built huge computing centers in close physical proximity to exchanges in order to gain trading advantages measured in tiny fractions of a second. Between 2005 and 2012, the average time to execute a trade dropped from about 10 seconds to just 0.0008 seconds,56 and robotic, high-speed trading was heavily implicated in the May 2010 “flash crash” in which the Dow Jones Industrial Average plunged nearly a thousand points and then recovered for a net gain, all within the space of just a few minutes. Viewed from this perspective, financialization is not so much a competing explanation for our seven economic trends; it is rather—at least to some extent—one of the ramifications of accelerating information technology. In this, there is a strong cautionary note as we look to the future: as IT continues its relentless progress, we can be certain that financial innovators, in the absence of regulations that constrain them, will find ways to leverage all those new capabilities—and, if history is any guide, it won’t necessarily be in ways that benefit society as a whole.
CIOs at Work by Ed Yourdon
8-hour work day, Apple's 1984 Super Bowl advert, business intelligence, business process, call centre, cloud computing, crowdsourcing, distributed generation, Flash crash, Googley, Grace Hopper, Infrastructure as a Service, Innovator's Dilemma, inventory management, Julian Assange, knowledge worker, Mark Zuckerberg, Nicholas Carr, rolodex, shareholder value, Silicon Valley, six sigma, Skype, smart grid, smart meter, software as a service, Steve Ballmer, Steve Jobs, Steven Levy, the scientific method, WikiLeaks, Y2K, Zipcar
The Internet is a Petri dish, I think, in some respects for some of these architectural failures to not only happen, but also the magnitude of them could be enormous at some point as we build up solutions and capabilities out of all the components that exist. And I don’t say that in a way that should be interpreted as we shouldn’t use the Internet or it’s dangerous, but I think we have to be prepared for some of these bigger failures to occur, and we will recover from them relatively quickly, but they will occur, like the stock market crash. Yourdon: Oh, the “flash crash” last year? Scott: We keep having these long, deep depressions that once were the case, but we’re still having these events, and I think of threats that we may face in the technology space in much the same way. They will happen, we will recover reasonably quickly, but they might be rather prolific in terms of their impact. Yourdon: I can certainly tell you that the Defense Department and various other government agencies spend a lot of time worrying about that, simply because there are people who are trying to deliberately exploit these things as opposed to accidental architectural defects.
Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else by Chrystia Freeland
Albert Einstein, algorithmic trading, banking crisis, barriers to entry, Basel III, battle of ideas, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Branko Milanovic, Bretton Woods, BRICs, business climate, call centre, carried interest, Cass Sunstein, Clayton Christensen, collapse of Lehman Brothers, conceptual framework, corporate governance, credit crunch, Credit Default Swap, crony capitalism, Deng Xiaoping, don't be evil, double helix, energy security, estate planning, experimental subject, financial deregulation, financial innovation, Flash crash, Frank Gehry, Gini coefficient, global village, Goldman Sachs: Vampire Squid, Gordon Gekko, Guggenheim Bilbao, haute couture, high net worth, income inequality, invention of the steam engine, job automation, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, linear programming, London Whale, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, NetJets, new economy, Occupy movement, open economy, Peter Thiel, place-making, Plutocrats, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, postindustrial economy, Potemkin village, profit motive, purchasing power parity, race to the bottom, rent-seeking, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, self-driving car, short selling, Silicon Valley, Silicon Valley startup, Simon Kuznets, Solar eclipse in 1919, sovereign wealth fund, stem cell, Steve Jobs, The Spirit Level, The Wealth of Nations by Adam Smith, Tony Hsieh, too big to fail, trade route, trickle-down economics, Tyler Cowen: Great Stagnation, wage slave, Washington Consensus, winner-take-all economy
Before the invention of the personal computer, the securitization of mortgages—which turned out to be part of the kindling for the financial crisis—would not have been possible. Nor would the algorithmic trading revolution, in which machines are replacing centuries-old stock exchanges and a couple of lines of corrupt code can trigger a multibillion-dollar loss of market value in moments, as occurred during the “flash crash” on May 6, 2010. — Revolution is the new global status quo, but not everyone is good at responding to it. My shorthand for the archetype best equipped to deal with it is “Harvard kids who went to provincial public schools.” They got into Harvard, or, increasingly, its West Coast rival, Stanford, so they are smart, focused, and reasonably privileged. But they went to public schools, often in the hinterlands, so they have an outsider’s ability to spot the weaknesses of the ruling paradigm and don’t have so much vested in the current system that they are afraid of stepping outside it.
3D printing, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K
These jokes capture much of what I think about the risks of machines taking over important societal functions and then running amok. Like Tversky, I know more about natural stupidity than about artificial intelligence, so I have no basis for forming an opinion about whether machines can think, and if so, whether such thoughts would be dangerous to humans. I leave that debate to others. Like anyone who follows financial markets, I’m aware of incidents such as the Flash Crash in 2010, when poorly designed trading algorithms caused stock prices to fall suddenly, only to recover a few minutes later. But this example is more an illustration of artificial stupidity than hyperintelligence. As long as humans continue to write programs, we’ll run the risk that some important safeguard has been omitted. So, yes, computers can screw things up, just like humans with “fat fingers” can accidently issue an erroneous buy or sell order for gigantic amounts of money.
Affordable Care Act / Obamacare, asset-backed security, bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, call centre, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, corporate governance, Credit Default Swap, cross-subsidies, dematerialisation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial innovation, financial intermediation, fixed income, Flash crash, forward guidance, Fractional reserve banking, full employment, George Akerlof, German hyperinflation, Goldman Sachs: Vampire Squid, Growth in a Time of Debt, income inequality, index fund, inflation targeting, interest rate derivative, interest rate swap, invention of the wheel, Irish property bubble, Isaac Newton, London Whale, Long Term Capital Management, loose coupling, low cost carrier, M-Pesa, market design, millennium bug, mittelstand, moral hazard, mortgage debt, new economy, Nick Leeson, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shock, passive investing, peer-to-peer lending, performance metric, Peter Thiel, Piper Alpha, Ponzi scheme, price mechanism, purchasing power parity, quantitative easing, quantitative trading / quantitative ﬁnance, railway mania, Ralph Waldo Emerson, random walk, regulatory arbitrage, Renaissance Technologies, rent control, Richard Feynman, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, Schrödinger's Cat, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, sovereign wealth fund, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, Steve Wozniak, The Great Moderation, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tobin tax, too big to fail, transaction costs, tulip mania, Upton Sinclair, Vanguard fund, Washington Consensus, We are the 99%, Yom Kippur War
As this book went to press, police swept of an unprepossessing semi-detached house in Hounslow, south west London, and arrested a man who, they implausibly suggested, had caused the incident by trading from his front room. The frightening truth is that with short term trading conducted by computers using algorithms, no person fully understands what is happening. Although no particularly serious consequences followed on that occasion, the vision of technology out of control was a disturbing portent of the future.21 On 15 October 2014 an equally inexplicable ‘flash crash’ was experienced in the market for US Treasury securities. The first great speculative bubble of the modern era was seen in the late 1980s in Japanese shares and Japanese property. At the peak of the boom it was claimed that the grounds of the emperor’s palace were worth more than the state of California. Whether this had been true or not, it would not remain so: the bubble burst. Japanese and foreign investors incurred large losses: the principal Japanese stock market indexes are even today less than half the level they reached at the peak.
The Price of Inequality: How Today's Divided Society Endangers Our Future by Joseph E. Stiglitz
affirmative action, Affordable Care Act / Obamacare, airline deregulation, Andrei Shleifer, banking crisis, barriers to entry, Basel III, battle of ideas, Berlin Wall, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, collapse of Lehman Brothers, collective bargaining, colonial rule, corporate governance, Credit Default Swap, Daniel Kahneman / Amos Tversky, Dava Sobel, declining real wages, deskilling, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, financial innovation, Flash crash, framing effect, full employment, George Akerlof, Gini coefficient, income inequality, income per capita, indoor plumbing, inflation targeting, invisible hand, John Harrison: Longitude, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kenneth Rogoff, labour market flexibility, London Interbank Offered Rate, lone genius, low skilled workers, Mark Zuckerberg, market bubble, market fundamentalism, medical bankruptcy, microcredit, moral hazard, mortgage tax deduction, obamacare, offshore financial centre, paper trading, patent troll, payday loans, price stability, profit maximization, profit motive, purchasing power parity, race to the bottom, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, shareholder value, short selling, Silicon Valley, Simon Kuznets, spectrum auction, Steve Jobs, technology bubble, The Chicago School, The Fortune at the Bottom of the Pyramid, The Myth of the Rational Market, The Spirit Level, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, transaction costs, trickle-down economics, ultimatum game, uranium enrichment, very high income, We are the 99%, women in the workforce
Markets were clearly not being efficient. A report of the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission “portrayed a market so fragmented and fragile that a single large trade could send stocks into a sudden spiral.” “Findings Regarding the Market Events of May 6, 2010,” report dated September 30, 2010. I served on an advisory panel to the SEC/CFTC on market reforms motivated by the flash crash. Its report is available at http://www.sec.gov/news/studies/2010/marketeventsreport.pdf. 39. Tax changes are an arena where framing is particularly contentious: does one express, say, a tax cut in terms of the percent reduction in their tax rate, in the absolute reduction in their tax rate, or in terms of the absolute dollar value that goes to each group. In one way of presenting the Bush tax cuts, the top 1 percent were the big beneficiaries, with one-third of the benefits going to the top 1 percent (two-thirds of the benefits went to the top 20 percent) and 1 percent of the benefits going to the bottom 20 percent.
Antifragile: Things That Gain From Disorder by Nassim Nicholas Taleb
Air France Flight 447, Andrei Shleifer, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, credit crunch, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discrete time, double entry bookkeeping, Emanuel Derman, epigenetics, financial independence, Flash crash, Gary Taubes, Gini coefficient, Henri Poincaré, high net worth, Ignaz Semmelweis: hand washing, informal economy, invention of the wheel, invisible hand, Isaac Newton, James Hargreaves, Jane Jacobs, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, meta analysis, meta-analysis, microbiome, moral hazard, mouse model, Norbert Wiener, pattern recognition, placebo effect, Ponzi scheme, principal–agent problem, purchasing power parity, quantitative trading / quantitative ﬁnance, Ralph Nader, random walk, Ray Kurzweil, rent control, Republic of Letters, Ronald Reagan, Rory Sutherland, Silicon Valley, six sigma, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, stochastic process, stochastic volatility, The Great Moderation, The Wealth of Nations by Adam Smith, Thomas Malthus, too big to fail, transaction costs, urban planning, Yogi Berra, Zipf's Law
The stock exchanges have converted from “open outcry” where wild traders face each other, yelling and screaming as in a souk, then go drink together. Traders were replaced by computers, for very small visible benefits and massively large risks. While errors made by traders are confined and distributed, those made by computerized systems go wild—in August 2010, a computer error made the entire market crash (the “flash crash”); in August 2012, as this manuscript was heading to the printer, the Knight Capital Group had its computer system go wild and cause $10 million dollars of losses a minute, losing $480 million. And naive cost-benefit analyses can be a bit harmful, an effect that of course swells with size. For instance, the French have in the past focused on nuclear energy as it seemed “clean” and cheap. And “optimal” on a computer screen.
23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, litecoin, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, offshore financial centre, optical character recognition, pattern recognition, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, WikiLeaks, Y Combinator, zero day
FBI and intelligence officials had come across the SEA before, when it previously hacked the New York Times, the BBC, and CBS News, but its latest attack was enough to have it branded as a terrorist organization by some and land it on the FBI’s most wanted list. The AP Twitter White House explosion debacle was not the first time algorithms had run amok on Wall Street, and it surely won’t be the last. More important, a Securities and Exchange Commission investigation into these types of incidents, including the infamous Flash Crash in May 2010, concluded the market, dominated by ultrafast trading algorithms, “had become so fragmented and fragile that a single large trade could send stocks into a sudden spiral.” In a world now measured in millionths of a second and heading exponentially faster all the time, there is literally no time for human intervention once the algos begin to go awry. The Syrian Electronic Army’s ability to roil global financial markets in an instant lays bare the economic risks of cyber terrorism to a deeply interconnected world, automated by computers and operating at near the speed of light.
Stress Test: Reflections on Financial Crises by Timothy F. Geithner
Affordable Care Act / Obamacare, asset-backed security, Atul Gawande, bank run, banking crisis, Basel III, Bernie Madoff, Bernie Sanders, Buckminster Fuller, Carmen Reinhart, central bank independence, collateralized debt obligation, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, David Brooks, Doomsday Book, eurozone crisis, financial innovation, Flash crash, Goldman Sachs: Vampire Squid, housing crisis, Hyman Minsky, illegal immigration, implied volatility, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, Martin Wolf, McMansion, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Nate Silver, Northern Rock, obamacare, paradox of thrift, pets.com, price stability, profit maximization, pushing on a string, quantitative easing, race to the bottom, RAND corporation, regulatory arbitrage, reserve currency, Saturday Night Live, savings glut, short selling, sovereign wealth fund, The Great Moderation, The Signal and the Noise by Nate Silver, Tobin tax, too big to fail, working poor
Greece’s credit default swaps implied a 50 percent probability that it would default within five years; its two-year bond yields rose into double digits, while Germany’s remained below 1 percent. Italy came under severe pressure, and banks throughout the eurozone struggled to borrow dollars. We had a chilling scare on May 6, during a general market swoon triggered by concerns about Europe, when U.S. stocks suddenly plunged an additional 5 percent in a few minutes. The cause of this “flash crash” turned out to be a malfunctioning trading algorithm, but as it was happening we thought the fear in Europe might be spiraling out of control. “This sucks,” Rahm declared in a typically succinct email. On a G-7 conference call the next day, I told the Europeans they needed a much broader and more aggressive strategy to contain the crisis. The first and most important element would be a massive firewall to prevent the Greek inferno from spreading, a demonstration that Europe had the capacity and the will to prevent contagion.
Hedge Fund Market Wizards by Jack D. Schwager
asset-backed security, backtesting, banking crisis, barriers to entry, Bernie Madoff, Black-Scholes formula, British Empire, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, James Dyson, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative ﬁnance, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve
I never buy or sell anything at the market because I’m wrong and have to get out. I have never done that in my career. I’ll begin to work out of the position, and if I see it turn around, I’ll get back in again, thinking that I was right all along. So you have never had a position that made you cry uncle? I have a lot of positions that made me cry uncle, but I don’t capitulate. [Zach speaking] Jimmy never panics. When we had the “flash crash,” his first question was, “Is there something wrong with the data?” It took him about a minute to realize the price quotes weren’t wrong, and he went long everything right there. [Jimmy continues] I don’t let myself panic. Even if I don’t know what’s going on, I’m not going to sell. I might lose 5 percent more trying to find out what is going on, but I’m not going to make a decision because other people are choosing to make a decision out of emotion.
The Stack: On Software and Sovereignty by Benjamin H. Bratton
1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Jony Ive, Julian Assange, Khan Academy, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, performance metric, personalized medicine, Peter Thiel, phenotype, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, WikiLeaks, working poor, Y Combinator
Before this break, the growth of planetary-scale computing systems was seen more generally as a beneficent blossoming. The old order would be swept away and a new day illuminated with the power of networks, iStuff, Twitter revolutions, “Internet freedom,” and smart cities. After this break, however, the sky darkened, and now the Cloud portends instead state surveillance, tax evasion, structural unemployment, troll culture, and flash crashes. Reality, however, is actually more radical in both directions. The thesis of this book holds that the official utopia and the official dystopia are not particularly useful frames of reference, and that neither provide a robust and intelligent program for art, design, economics, or engineering. In fact, the messianic effervescence of the former and the apocalyptic panic of the latter are part of the problem.
Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman
3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, bitcoin, blockchain, business process, call centre, centre right, Clayton Christensen, clean water, cloud computing, corporate social responsibility, crowdsourcing, David Brooks, demand response, demographic dividend, demographic transition, Deng Xiaoping, Donald Trump, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, game design, gig economy, global supply chain, illegal immigration, immigration reform, income inequality, indoor plumbing, Internet of things, invention of the steam engine, inventory management, Jeff Bezos, job automation, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, low skilled workers, Lyft, Mark Zuckerberg, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, pattern recognition, planetary scale, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, Steve Jobs, TaskRabbit, Thomas L Friedman, transaction costs, Transnistria, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra
Such markets tend to have low ‘spreads’—the difference between the prices at which one can buy or sell a stock, which reflects the fee that dealers demand and thus transaction costs for investors.” But there are real downsides, it added: “The algorithms they use to trade profitably make more errors and are programmed to get out of the market altogether when markets get too volatile. The problem is exacerbated by the similarity of the algorithms used by many high-frequency trading firms—they all bail out at the same time. That is what happened in the 2010 flash crash.” Humans can do the same but machines can do it bigger and faster and, arguably, can be more easily spoofed into huge losses. “In 2012, a flaw in the algorithms of one of the largest US high-frequency trading firms, Knight Capital, caused losses of $440 million in forty-five minutes as its system bought at higher prices than it sold.” But my favorite line in the Nature article was still to come.