Renaissance Technologies

59 results back to index


pages: 374 words: 114,600

The Quants by Scott Patterson

Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Meriwether, John Nash: game theory, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sergey Aleynikov, short selling, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

The first wave of quants went to banks such as Salomon Brothers, Morgan Stanley, and Goldman Sachs. But a few renegades struck off on their own, forming secretive hedge funds in the tradition of Ed Thorp. In a small, isolated town on Long Island one such group emerged. In time, it would become one of the most successful investing powerhouses the world had ever seen. Its name was Renaissance Technologies. It is fitting that Renaissance Technologies, the most secretive hedge fund in the world, founded by a man who once worked as a code breaker for the U.S. government, is based in a small Long Island town that once was the center of a Revolutionary War spy ring. The town of Setauket dates from 1655, when a half dozen men purchased a thirty-square-mile strip of land facing Long Island Sound from the Setalcott Indian tribe.

Mere days before the crash, Asness’s hedge fund was on the verge of filing the final papers for an initial public offering. Boaz Weinstein, chess “life master,” card counter, and powerful derivatives trader at Deutsche Bank, who built his internal hedge fund, Saba (Hebrew for “wise grandfather”), into one of the most powerful credit-trading funds on the planet, juggling $30 billion worth of positions. Jim Simons, the reclusive, highly secretive billionaire manager of Renaissance Technologies, the most successful hedge fund in history, whose mysterious investment techniques are driven by scientists poached from the fields of cryptoanalysis and computerized speech recognition. Ed Thorp, godfather of the quants. As a math professor in the 1950s, Thorp deployed his mathematical skills to crack blackjack, unifying the key themes of gambling and investing, and later became the first math genius to figure out how to use similar skills to make millions on Wall Street.

More important to the gathering crowd, Gowen was one of the most successful female poker players in the country. Muller, tan, fit, and at forty-two looking a decade younger than his age, a wiry Pat Boone in his prime, radiated the relaxed cool of a man accustomed to victory. He waved across the room to Jim Simons, billionaire math genius and founder of the most successful hedge fund on the planet, Renaissance Technologies. Simons, a balding, white-bearded wizard of quantitative investing, winked back as he continued chatting with the circle of admirers hovering around him. The previous year, Simons had pocketed $1.5 billion in hedge fund fees, at the time the biggest one-year paycheck ever earned by a hedge fund manager. His elite team of traders, hidden away in a small enclave on Long Island, marshaled the most mind-bending advances in science and mathematics, from quantum physics to artificial intelligence to voice recognition technology, to wring billions in profits from the market.


pages: 407 words: 104,622

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, blockchain, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, obamacare, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine

Grateful acknowledgment is made for permission to reprint the following photographs: 1: Courtesy of Lee Neuwirth © Lee Neuwirth 2: Courtesy of Seth Rumshinsky 3: Photo by Rick Mott, taken at the NJ Open Go Tournament, provided with permission, courtesy of Stefi Baum 4, 5: Courtesy of Brian Keating 6: Courtesy of David Eisenbud 7: Courtesy of Wall Street Journal and Jenny Strasburg 8: Patrick McMullan/Getty Images ISBN 9780735217980 (hardcover) ISBN 9780735217997 (ebook) ISBN 9780593086315 (international edition) Jacket design: Karl Spurzem Jacket image: (equations) Virtualphoto / Getty Images Version_1 CONTENTS Also by Gregory Zuckerman Title Page Copyright Dedication Cast of Characters A Timeline of Key Events Introduction Prologue PART ONE Money Isn’t Everything Chapter One Chapter Two Chapter Three Chapter Four Chapter Five Chapter Six Chapter Seven Chapter Eight Chapter Nine Chapter Ten Chapter Eleven PART TWO Money Changes Everything Chapter Twelve Chapter Thirteen Chapter Fourteen Chapter Fifteen Chapter Sixteen Epilogue Photographs Acknowledgments Appendices Notes Index About the Author To Gabriel and Elijah My signals in the noise CAST OF CHARACTERS James Simons Mathematician, code breaker, and founder of Renaissance Technologies Lenny Baum Simons’s first investing partner and author of algorithms that impacted the lives of millions James Ax Ran the Medallion fund and developed its first trading models Sandor Straus Data guru who played key early role at Renaissance Elwyn Berlekamp Game theorist who managed the Medallion fund at a key turning point Henry Laufer Mathematician who moved Simons’s fund toward short-term trades Peter Brown Computer scientist who helped engineer Renaissance’s key breakthroughs Robert Mercer Renaissance’s co-CEO, helped put Donald Trump in the White House Rebekah Mercer Teamed up with Steve Bannon to upend American politics David Magerman Computer specialist who tried to stop the Mercers’ political activities A TIMELINE OF KEY EVENTS 1938 Jim Simons born 1958 Simons graduates MIT 1964 Simons becomes code breaker at the IDA 1968 Simons leads math department at Stony Brook University 1974 Simons and Chern publish groundbreaking paper 1978 Simons leaves academia to start Monemetrics, a currency trading firm, and a hedge fund called Limroy 1979 Lenny Baum and James Ax join 1982 Firm’s name changes to Renaissance Technologies Corporation 1984 Baum quits 1985 Ax and Straus move the company to California 1988 Simons shuts down Limroy, launches the Medallion fund 1989 Ax leaves, Elwyn Berlekamp leads Medallion 1990 Berlekamp departs, Simons assumes control of the firm and fund 1992 Henry Laufer becomes full-time employee 1993 Peter Brown and Robert Mercer join 1995 Brown, Mercer achieve key breakthrough 2000 Medallion soars 98.5 percent 2005 Renaissance Institutional Equities Fund launches 2007 Renaissance and other quant firms suffer sudden losses 2010 Brown and Mercer take over firm 2017 Mercer steps down as co-CEO INTRODUCTION You do know—no one will speak with you, right?”

Grateful acknowledgment is made for permission to reprint the following photographs: 1: Courtesy of Lee Neuwirth © Lee Neuwirth 2: Courtesy of Seth Rumshinsky 3: Photo by Rick Mott, taken at the NJ Open Go Tournament, provided with permission, courtesy of Stefi Baum 4, 5: Courtesy of Brian Keating 6: Courtesy of David Eisenbud 7: Courtesy of Wall Street Journal and Jenny Strasburg 8: Patrick McMullan/Getty Images ISBN 9780735217980 (hardcover) ISBN 9780735217997 (ebook) ISBN 9780593086315 (international edition) Jacket design: Karl Spurzem Jacket image: (equations) Virtualphoto / Getty Images Version_1 CONTENTS Also by Gregory Zuckerman Title Page Copyright Dedication Cast of Characters A Timeline of Key Events Introduction Prologue PART ONE Money Isn’t Everything Chapter One Chapter Two Chapter Three Chapter Four Chapter Five Chapter Six Chapter Seven Chapter Eight Chapter Nine Chapter Ten Chapter Eleven PART TWO Money Changes Everything Chapter Twelve Chapter Thirteen Chapter Fourteen Chapter Fifteen Chapter Sixteen Epilogue Photographs Acknowledgments Appendices Notes Index About the Author To Gabriel and Elijah My signals in the noise CAST OF CHARACTERS James Simons Mathematician, code breaker, and founder of Renaissance Technologies Lenny Baum Simons’s first investing partner and author of algorithms that impacted the lives of millions James Ax Ran the Medallion fund and developed its first trading models Sandor Straus Data guru who played key early role at Renaissance Elwyn Berlekamp Game theorist who managed the Medallion fund at a key turning point Henry Laufer Mathematician who moved Simons’s fund toward short-term trades Peter Brown Computer scientist who helped engineer Renaissance’s key breakthroughs Robert Mercer Renaissance’s co-CEO, helped put Donald Trump in the White House Rebekah Mercer Teamed up with Steve Bannon to upend American politics David Magerman Computer specialist who tried to stop the Mercers’ political activities A TIMELINE OF KEY EVENTS 1938 Jim Simons born 1958 Simons graduates MIT 1964 Simons becomes code breaker at the IDA 1968 Simons leads math department at Stony Brook University 1974 Simons and Chern publish groundbreaking paper 1978 Simons leaves academia to start Monemetrics, a currency trading firm, and a hedge fund called Limroy 1979 Lenny Baum and James Ax join 1982 Firm’s name changes to Renaissance Technologies Corporation 1984 Baum quits 1985 Ax and Straus move the company to California 1988 Simons shuts down Limroy, launches the Medallion fund 1989 Ax leaves, Elwyn Berlekamp leads Medallion 1990 Berlekamp departs, Simons assumes control of the firm and fund 1992 Henry Laufer becomes full-time employee 1993 Peter Brown and Robert Mercer join 1995 Brown, Mercer achieve key breakthrough 2000 Medallion soars 98.5 percent 2005 Renaissance Institutional Equities Fund launches 2007 Renaissance and other quant firms suffer sudden losses 2010 Brown and Mercer take over firm 2017 Mercer steps down as co-CEO INTRODUCTION You do know—no one will speak with you, right?”

Grateful acknowledgment is made for permission to reprint the following photographs: 1: Courtesy of Lee Neuwirth © Lee Neuwirth 2: Courtesy of Seth Rumshinsky 3: Photo by Rick Mott, taken at the NJ Open Go Tournament, provided with permission, courtesy of Stefi Baum 4, 5: Courtesy of Brian Keating 6: Courtesy of David Eisenbud 7: Courtesy of Wall Street Journal and Jenny Strasburg 8: Patrick McMullan/Getty Images ISBN 9780735217980 (hardcover) ISBN 9780735217997 (ebook) ISBN 9780593086315 (international edition) Jacket design: Karl Spurzem Jacket image: (equations) Virtualphoto / Getty Images Version_1 CONTENTS Also by Gregory Zuckerman Title Page Copyright Dedication Cast of Characters A Timeline of Key Events Introduction Prologue PART ONE Money Isn’t Everything Chapter One Chapter Two Chapter Three Chapter Four Chapter Five Chapter Six Chapter Seven Chapter Eight Chapter Nine Chapter Ten Chapter Eleven PART TWO Money Changes Everything Chapter Twelve Chapter Thirteen Chapter Fourteen Chapter Fifteen Chapter Sixteen Epilogue Photographs Acknowledgments Appendices Notes Index About the Author To Gabriel and Elijah My signals in the noise CAST OF CHARACTERS James Simons Mathematician, code breaker, and founder of Renaissance Technologies Lenny Baum Simons’s first investing partner and author of algorithms that impacted the lives of millions James Ax Ran the Medallion fund and developed its first trading models Sandor Straus Data guru who played key early role at Renaissance Elwyn Berlekamp Game theorist who managed the Medallion fund at a key turning point Henry Laufer Mathematician who moved Simons’s fund toward short-term trades Peter Brown Computer scientist who helped engineer Renaissance’s key breakthroughs Robert Mercer Renaissance’s co-CEO, helped put Donald Trump in the White House Rebekah Mercer Teamed up with Steve Bannon to upend American politics David Magerman Computer specialist who tried to stop the Mercers’ political activities A TIMELINE OF KEY EVENTS 1938 Jim Simons born 1958 Simons graduates MIT 1964 Simons becomes code breaker at the IDA 1968 Simons leads math department at Stony Brook University 1974 Simons and Chern publish groundbreaking paper 1978 Simons leaves academia to start Monemetrics, a currency trading firm, and a hedge fund called Limroy 1979 Lenny Baum and James Ax join 1982 Firm’s name changes to Renaissance Technologies Corporation 1984 Baum quits 1985 Ax and Straus move the company to California 1988 Simons shuts down Limroy, launches the Medallion fund 1989 Ax leaves, Elwyn Berlekamp leads Medallion 1990 Berlekamp departs, Simons assumes control of the firm and fund 1992 Henry Laufer becomes full-time employee 1993 Peter Brown and Robert Mercer join 1995 Brown, Mercer achieve key breakthrough 2000 Medallion soars 98.5 percent 2005 Renaissance Institutional Equities Fund launches 2007 Renaissance and other quant firms suffer sudden losses 2010 Brown and Mercer take over firm 2017 Mercer steps down as co-CEO INTRODUCTION You do know—no one will speak with you, right?” I was picking at a salad at a fish restaurant in Cambridge, Massachusetts, in early September 2017, trying my best to get a British mathematician named Nick Patterson to open up about his former company, Renaissance Technologies. I wasn’t having much luck. I told Patterson that I wanted to write a book about how James Simons, Renaissance’s founder, had created the greatest moneymaking machine in financial history. Renaissance generated so much wealth that Simons and his colleagues had begun to wield enormous influence in the worlds of politics, science, education, and philanthropy. Anticipating dramatic societal shifts, Simons harnessed algorithms, computer models, and big data before Mark Zuckerberg and his peers had a chance to finish nursery school.


pages: 356 words: 105,533

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

algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!, zero-sum game

Eventually, several ATD employees joined up with Island before going on to work at other high-frequency firms, spreading the technique. Within a few years, automated traders such as ATD would make up the bulk of Island’s volume. Eventually, they would make up the bulk of all stock trading in the United States. ONE of the most successful and notorious automated traders would be a secretive, highly successful hedge fund based on Long Island, called Renaissance Technologies. At first, Renaissance’s programmers—the firm was entirely run by mathematicians, scientists, and computer wonks—were dubious of Island. The reason: Datek. They were suspicious that the Datek bandits were secretly watching Island’s flow and front-running it. But Island proved too big to ignore. One day in the late 1990s, several of Renaissance’s top executives, including a pair of AI experts who’d formerly worked at IBM, Peter Brown and Bob Mercer, paid a visit to 50 Broad.

Andresen, meanwhile, continued to court sophisticated traders—including the most dangerous shark of all. ANDRESEN was in the middle of his well-rehearsed pitch, ticking off all the benefits that Island brought investors who thrived on blinding speed and nosebleed volumes. The instant execution. The gobs of streaming data. The dirt-cheap fees. And if anyone was in the market for speed, data, and low fees, it was the hedge fund he was pitching to: Renaissance Technologies. But the reclusive, white-bearded chieftain of Renaissance, Jim Simons, didn’t seem to be listening. In fact, it seemed as if Simons had dozed off in the middle of Andresen’s presentation in a conference room at Island’s 50 Broad headquarters, his Merit cigarette burning to a cinder in an ashtray before him. Was Simons actually snoring? Disconcerted, Andresen muddled on, addressing his speech to the other Renaissance executives in the room, Peter Brown and Bob Mercer, the former IBM AI experts who’d turned Renaissance into an invincible trading machine.

The reaction to a panic tick was instantaneous: Cancel all bids and offers. Get out, now. Starting at 2:40, the panic ticks picked up speed dramatically. Timber Hill started dumping positions and pulling out as fast as possible. Peterffy called the trading desk again to see if anyone knew what was happening. No one did. PETER Brown had never seen the likes of it. No one had. The co–chief executive of Renaissance Technologies, the most sophisticated trading operation in the world, was sitting in his office, situated along a brightly lit hallway of a nondescript building that seemed more elementary school than state-of-the-art trading hub. Despite all of his sophistication, Brown was at a loss concerning what was causing the market to tank. Fears about Greece had gripped the market for days. Riots on the streets of Athens had unnerved investors.


pages: 399 words: 114,787

Dark Towers: Deutsche Bank, Donald Trump, and an Epic Trail of Destruction by David Enrich

Affordable Care Act / Obamacare, anti-globalists, Asian financial crisis, banking crisis, Berlin Wall, buy low sell high, collateralized debt obligation, commoditize, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, East Village, estate planning, Fall of the Berlin Wall, financial innovation, forensic accounting, high net worth, housing crisis, interest rate derivative, interest rate swap, Jeffrey Epstein, London Interbank Offered Rate, Lyft, Mikhail Gorbachev, NetJets, obamacare, offshore financial centre, post-materialism, Ralph Waldo Emerson, Renaissance Technologies, risk tolerance, Robert Mercer, rolodex, sovereign wealth fund, too big to fail, transcontinental railway, yield curve

The review didn’t find anything problematic. Bittar collected his money. Word of the gigantic payout rippled through London’s banking circles. As U.S. and British authorities started investigating Libor manipulation, Bittar’s bonanza would serve as an extreme example of how traders were incentivized to engage in fraud. Since the late 1990s, Deutsche had been peddling products to hedge funds, including the enormous Renaissance Technologies, that helped them avoid taxes. Founded by a former government code-breaker, Renaissance specialized in using computer programs to scout out tiny market inefficiencies that could be exploited. The firm recruited engineers and mathematicians, including an IBM programmer named Robert Mercer, a right-wing zealot who once noted that he enjoyed spending time with cats more than with people. Mercer eventually rose to the top of Renaissance, helping it become one of the world’s most successful hedge funds.

The job wasn’t glamorous, but it was vital to the bank’s prospects. Once again, BaFin said no. Passed over for yet another high-profile gig, Broeksmit was relegated to the board of an obscure U.S. legal entity—Deutsche Bank Trust Company Americas, or DBTCA. This was the corporate husk of the old Bankers Trust business, and it had long been a dumping ground for unsavory businesses. The tax-avoiding trades with Renaissance Technologies were housed there. So were the loans to Donald Trump. Executives in London and Frankfurt weren’t paying much attention to what happened inside this squirrelly unit. In fact, nobody was: DBTCA had barely a hundred employees, compared to the tens of thousands in other divisions, and it didn’t have its own chief financial officer or risk department. But it had become a crucial holding company, through which almost all of its American businesses channeled their transactions.

In the Libor case, prosecutors and regulators in at least three countries had concluded that Deutsche was one of the worst offenders, with responsibility for the scandal up and down the corporate ladder. Penalties would surely stretch into the billions. And in Washington, Bob Roach’s Senate committee had just finished its tax-avoidance investigation, and the result was a scathing report spelling out how Deutsche had enabled giant hedge funds like Renaissance Technologies to avoid billions of dollars in federal taxes. Shortly before the report was published, the Broeksmit family got an unsettling heads-up from the bank: Bill might be mentioned in an unfavorable light. The moment the report was posted online, Val searched the document, and sure enough, his father was named eight times in the ninety-six-page report. The cameos were brief but important. Back in 2008, as Deutsche’s lucrative work with Renaissance had intensified, Jain had dispatched Broeksmit to make sure everything was kosher.


pages: 317 words: 84,400

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

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, G4S, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Robert Mercer, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

And some states, including New York, have ordered 23andMe and similar services to get approval from the state’s health department, declaring their tests to be medical and therefore open to regulation. Such regulation is “appallingly paternalistic,” says 23andMe, adding that people have a right to information contained within their own genes. Such genomic scanning is now fast and affordable, thanks in part to Nick Patterson, a Wall Street hacker who after eight years at Renaissance Technologies, the quantitative hedge fund, joined up with the Broad Institute, a joint research center of Harvard and MIT, in 2001. Working at Renaissance, which makes money off of sorting data and spotting patterns that nobody else can, made Patterson the perfect person to help the Broad Institute, which was drowning in DNA data so deep that the researchers there found it to be unnavigable. The information from sequencing just hundreds of people’s complete DNA genomes produces data so copious that researchers usually don’t send it to others across the Internet because such a transfer would take weeks.

Are you going to the mall today won’t be mistaken with Our you going to the mall today because, simply, people never say our you going. Just as we learn grammar rules, so the machine-learning algorithm did as well. This method forms the backbone of the speech recognition programs we use today. Brown and Mercer’s breakthrough didn’t go unnoticed on Wall Street. They left IBM in 1993 for Renaissance Technologies, the hedge fund. Their work developing language algorithms could also be used to predict short-term trends in the financial markets, and versions of their algorithms became the core of Renaissance’s best funds. During a run powered by Brown and Mercer’s work, Renassiance went from $200 million in assets in 1993 to $4 billion in 2001.7 How the speech recognition algorithms are used in the markets isn’t exactly known, which is why Renaissance remains so successful.

“If you buy the wrong house, it can affect your life.” In early 2012, Kelman had just successfully recruited five Ivy League quant-hackers to join Redfin in Seattle. Two of them came from Bridgewater, the largest hedge fund in the world. Turning down Bridgewater is something that doesn’t often happen; a few years there and you have a very real chance to be a millionaire. The only thing crazier, perhaps, would be to turn down a job at Renaissance Technologies. The Long Island operation is so full of high-level engineering and physics PhDs that admirers like to call it the “best physics department in the world.” But at Y Combinator, the startup accelerator in Silicon Valley that continually draws in elite hacker talent, I met Ignacio Thayer, who, among other notable achievements, is the only person I’ve known to turn down Renaissance. Thayer had been a PhD candidate in computer science at Stanford when he interviewed with and was offered a job by the hedge fund.


pages: 584 words: 187,436

More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby

Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, John Meriwether, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, Paul Samuelson, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs

Hedge funds are the vehicles for loners and contrarians, for individualists whose ambitions are too big to fit into established financial institutions. Cliff Asness is a case in point. He had been a rising star at Goldman Sachs, but he opted for the freedom and rewards of running his own shop; a man who collects plastic superheroes is not going to remain a salaried antihero for long, at least not if he can help it. Jim Simons of Renaissance Technologies, the mathematician who emerged in the 2000s as the highest earner in the industry, would not have lasted at a mainstream bank: He took orders from nobody, seldom wore socks, and got fired from the Pentagon’s code-cracking center after denouncing his bosses’ Vietnam policy. Ken Griffin of Citadel, the second highest earner in 2006, started out trading convertible bonds from his dorm room at Harvard; he was the boy genius made good, the financial version of the entreprenerds who forged tech companies such as Google.

In 2008, buyers of illiquid assets paid heavily again, as we shall see presently. 13 THE CODE BREAKERS Not so many hedge funders have been to East Setauket. It is an hour’s drive from Manhattan, along the Long Island Express-way; it is separated from the hedge-fund cluster in Greenwich by a wedge of the Atlantic Ocean. But this sleepy Long Island township is home to what is perhaps the most successful hedge fund ever: Renaissance Technologies. Starting around the time that David Swensen invested in Farallon, Renaissance positively coined money; between the end of 1989 and 2006, its flagship fund, Medallion, returned 39 percent per year on average.1 By the mid-2000s, Renaissance’s founder, James Simons, had emerged as the highest hedge-fund earner of them all. He was not the world’s most famous billionaire, but he was probably its cleverest.

He was not the world’s most famous billionaire, but he was probably its cleverest. Simons was a mathematician and code breaker, a lifelong speculator and entrepreneur, and his extraordinary success derived from the combination of these passions. As a speculator, he had dabbled in commodities since his student days, acquiring the trading bug that set him up for future stardom. As an entrepreneur, he had launched a string of businesses; the name of his company, Renaissance Technologies, reflected its origins in high-tech venture capital. As a code cracker, Simons had worked at the Pentagon’s secretive Institute for Defense Analyses, where he learned how to build a research organization that was closed toward outsiders but collaborative on the inside. As a mathematician, he had affixed his name to a breakthrough known as the Chern-Simons theory and won the American Mathematical Society’s Oswald Veblen Prize, the highest honor in geometry.


pages: 296 words: 78,112

Devil's Bargain: Steve Bannon, Donald Trump, and the Storming of the Presidency by Joshua Green

4chan, Affordable Care Act / Obamacare, Ayatollah Khomeini, Bernie Sanders, business climate, centre right, Charles Lindbergh, coherent worldview, collateralized debt obligation, conceptual framework, corporate raider, crony capitalism, currency manipulation / currency intervention, Donald Trump, Fractional reserve banking, Goldman Sachs: Vampire Squid, Gordon Gekko, guest worker program, illegal immigration, immigration reform, liberation theology, low skilled workers, Nate Silver, Nelson Mandela, nuclear winter, obamacare, Peace of Westphalia, Peter Thiel, quantitative hedge fund, Renaissance Technologies, Robert Mercer, Ronald Reagan, Silicon Valley, social intelligence, speech recognition, urban planning

He was dressed up as one of his favorite movie characters of all-time, Brigadier General Frank Savage, the tough-as-nails commander, played by Gregory Peck, who takes over a demoralized World War II bombing unit and whips them into fighting shape in the 1949 classic Twelve O’Clock High. Ordinarily, Bannon wasn’t big into cosplay. But this was a special occasion: the annual Christmas party thrown by the reclusive billionaire Robert Mercer, an eccentric computer scientist who was co-CEO of the fabled quantitative hedge fund Renaissance Technologies. As introverted and private as Bannon was voluble and outspoken, Mercer was nonetheless a man of ardent passions. He collected machine guns and owned the gas-operated AR-18 assault rifle that Arnold Schwarzenegger wielded in The Terminator. He had built a $2.7 million model train set equipped with a miniature video camera to allow operators to experience the view from inside the cockpit of his toy engine.

And yet one donor thought otherwise. The reason Bannon appealed to Mercer and almost nobody else is that Mercer’s odd, charmed life had taught him to reject ordinary ways of thinking and reflexively seek advantage in places other people didn’t look or couldn’t see. It shaped his way of viewing the world and made him extravagantly rich. And the particular way in which Mercer had taken this worldview and applied it at Renaissance Technologies—by stringing together a series of models that functioned in tandem—was the same way that Bannon thought about politics and hoped to attack the system. The model that Mercer believed in so fiercely was devised by a mathematician and former code breaker for the Pentagon’s secretive Institute for Defense Analyses named Jim Simons. In the late 1970s, Simons was chairman of the math department at Stony Brook University on Long Island and an avid amateur speculator in commodities (he spent his wedding money trading soybean futures).

Money managers might get lucky for a spell and outperform a benchmark index. But efficient-market theory held that over the long term, they couldn’t consistently beat the market. Simons thought they could, if only they applied the right sort of expertise. With professional code breakers to detect systemic patterns in equity markets and mathematicians to write sophisticated algorithms, Renaissance Technologies, which Simons founded in 1982, built a program that traded on the basis of computer-generated signals. Before long, the firm was consistently outperforming discretionary traders. As his company flourished, Simons recruited additional mathematicians, astronomers, and computer scientists—but never economists or people with experience working on Wall Street. Simons considered them, in effect, to be intellectually corrupted by what he thought was the narrow and incurious way in which Wall Street trading houses approached financial markets.


Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, buy and hold, compound rate of return, Edward Thorp, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

My contention is that it is much more logical and sensible for someone to become a profitable $100,000 trader before xi P1: JYS fm JWBK321-Chan xii September 24, 2008 13:43 Printer: Yet to come PREFACE becoming a profitable $100 million trader. This can be shown to be true on many fronts. Many legendary quantitative hedge fund managers such as Dr. Edward Thorp of the former Princeton-Newport Partners (Poundstone, 2005) and Dr. Jim Simons of Renaissance Technologies Corp. (Lux, 2000) started their careers trading their own money. They did not begin as portfolio managers for investment banks and hedge funds before starting their own fund management business. Of course, there are also plenty of counterexamples, but clearly this is a possible route to riches as well as intellectual accomplishment, and for someone with an entrepreneurial bent, a preferred route.

An example of this is the summer 2007 meltdown, described in the previously cited article “What Happened to the Quants in August 2007?” by Amir Khandani and Andrew Lo. During August 2007, under the ominous cloud of a housing and mortgage default crisis, a number of well-known hedge funds experienced unprecedented losses, with Goldman Sachs’s Global Alpha fund falling 22.5 percent. Several billion dollars evaporated within all of one week. Even Renaissance Technologies Corporation, arguably the most successful quantitative hedge fund of all time, lost 8.7 percent in the first half of August, though it later recovered most of it. Not only is the magnitude of the loss astounding, but the widespread nature of it was causing great concern in the financial community. Strangest of all, few of these funds hold any mortgage-backed securities at all, ostensibly the root cause of the panic.

See Sharpe ratio Information, slow diffusion of, 117–118 Interactive Brokers, 15, 73, 82, 83 Investors, herdlike behavior of, 118–119 J January effect, 143–146 backtesting, 144–146 Java, 80, 85 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 178 K Kalman filter, 116 Kavanaugh, Paul, 149 Kelly formula, 95, 97, 100–103, 105, 107, 153, 161 calculating the optimal allocation based on, 100–102 calculating the optimal leverage based on, 99 simple derivation of, when return distribution is Gaussian, 112–113 Kerviel, Jérôme, 160 Khandani, Amir, 104 Kirk Report, 10 L LeSage, James, 168 Leverage, 5, 95–103 Liquidnet, 73 Lo, Andrew, 104 Logical Information Machines, 35, 36 Long-only versus market-neutral strategies, calculating Sharpe ratio for, 45–47 Long-Term Capital Management, 110, 157 Long-term wealth, maximizing, 96 Look-ahead bias, 51–52 Loss aversion, 108–109 M Market impact, 22 MarketQA (Quantitative Analytics), 35 Markov models, hidden, 116, 121 Printer: Yet to come INDEX R , 21, 32–34, MATLAB 137–139 calculating optimal allocation using Kelly formula, 100–102 a quick survey of, 163–168 using in automated trading systems, 80, 81, 83, 85 using to avoid look-ahead bias, 51–52 using to backtest January effect, 144–146 mean-reverting strategy with and without transaction costs, 61–65 year-on-year seasonal trending strategy, 146–148 using to calculate maximum drawdown and its duration, 48–50 using to calculate Sharpe ratio for long-only strategies, 46–47 using for pair trading, 56–58, 59–60 using to scrape web pages for financial data, 34 MCSI Barra, 35, 136 Mean-reverting versus momentum strategies, 116–119 Mean-reverting time series, calculation of the half-life of, 141–142 Millennium Partners, 12 Model risk, 107 ModelStation (Clarifi), 35 Momentum strategies, mean-reverting versus, 116–119 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 Index Money and risk management, 95–113 optimal capital allocation and leverage, 95–103 psychological preparedness, 108–111 risk management, 103–108 Murphy, Kevin, 168 N National Association of Securities Dealers (NASD) Series 7 examination, 70 National Bureau of Economic Research, 10 Neural networks, 116 New York Mercantile Exchange (NYMEX), 16, 149 Northfield Information Services, 136 O Oanda, 37, 73 Octave, 33 O-Matrix, 33 Ornstein-Uhlenbeck formula, 140–141, 142 Out-of-sample testing, 53–55 P Pair trading of GLD and GDX, 55 Paper trading, 55 testing your system by, 89–90 Parameterless trading models, 54–55 PFG Futures, 73 Plus-tick rule, elimination of, 92, 120 Posit (ITG), 73 Position risk, 107 Printer: Yet to come 179 Post earnings announcement drift (PEAD), 118 Principal component analysis (PCA), 136–139 Profit and loss (P&L), 6, 89 curve, 20 Programming consultant, hiring a, 86–87 Psychological preparedness, 108–111 Q Qian, Edward, 154 Quantitative Analytics, 35 Quantitative Services Group, 136 Quantitative trading, 1–8 business case for, 4–8 demand on time, 5–7 marketing, nonnecessity of, 7–8 scalability, 5 the way forward, 8 special topics in, 115–156 exit strategy, 140–143 factor models, 133–139 high-frequency trading strategies, 151–153 high-leverage versus high-beta portfolio, 153–154 mean-reverting versus momentum strategies, 116–119 regime switching, 119–126 seasonal trading strategies, 143–151 stationarity and cointegration, 126–133 who can become a quantitative trader, 2–4 Quotes-plus.com, 37 P1: JYS ind JWBK321-Chan October 2, 2008 14:7 180 R Random walking, 116 REDIPlus trading platform (Goldman Sachs), 73, 82, 83, 84 Regime shifts, 25, 91–92 Regime switching, 119–126 academic attempts to model, 120–121 Markov, 121 using a machine learning tool to profit from, 122–126 Regulation T (SEC), 5, 14, 69–70 Renaissance Technologies Corporation, 104 Representativeness bias, 109 Reverse split, 38 Risk management, 103–108. See also Money and risk management “Risk Parity Portfolios” (Qian), 154 Round-trip transaction, 23 Russell 2000 index, 19 S SAC Capital Advisors, 19 Sample size, 53 Schiller, Robert, 118 Scilab, 33 “Seasonal Trades in Stocks” (blog entry), 11 Seasonal trading strategies, 143–151 gasoline futures, 148–151 Securities and Exchange Commission (SEC), 92 Regulation T, 5, 14, 69–70 Seeking Alpha, 10 Sensitivity analysis, 60 Printer: Yet to come INDEX Sharpe ratio, 11, 17, 18–21, 43–47, 58–59, 61, 66, 98, 102, 151, 153, 161 calculating for long-only versus market-neutral strategies, 45–47 Sheppard, Kevin, 168 Slippage, 23, 88 Social Science Research Network, 10 Société Générale, 144, 160 Software risk, 108 Specific return, 134 Split and dividend-adjusted data, 36–40 Standard & Poor’s small-cap index, 19, 87 Stationarity, 126–133 Statistical arbitrage trading.


Mindf*ck: Cambridge Analytica and the Plot to Break America by Christopher Wylie

4chan, affirmative action, Affordable Care Act / Obamacare, availability heuristic, Berlin Wall, Bernie Sanders, big-box store, Boris Johnson, British Empire, call centre, Chelsea Manning, chief data officer, cognitive bias, cognitive dissonance, colonial rule, computer vision, conceptual framework, cryptocurrency, Daniel Kahneman / Amos Tversky, desegregation, Dominic Cummings, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Etonian, first-past-the-post, Google Earth, housing crisis, income inequality, indoor plumbing, information asymmetry, Internet of things, Julian Assange, Lyft, Marc Andreessen, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, obamacare, Peter Thiel, Potemkin village, recommendation engine, Renaissance Technologies, Robert Mercer, Ronald Reagan, Rosa Parks, Sand Hill Road, Scientific racism, Shoshana Zuboff, side project, Silicon Valley, Skype, uber lyft, unpaid internship, Valery Gerasimov, web application, WikiLeaks, zero-sum game

” * * * — BANNON’S INTEREST IN OUR work wasn’t merely academic; he had big ideas for SCL. He told Nix of a major right-wing donor who might be persuaded to make an investment in the firm. Robert Mercer was unusual for a billionaire. He’d gotten a Ph.D. in computer science in the early 1970s, then went on to become a cog in the wheel at IBM for twenty-some years. In 1993, he joined a hedge fund called Renaissance Technologies, where he used data science and algorithms to inform his investments—and made a stupid amount of money doing it. Mercer wasn’t one of these wheeler-dealer types who frenetically bought and sold businesses. He was an extremely introverted engineer who applied his technical skills very specifically to the art and science of making money. Over the years, Mercer had donated millions of dollars to conservative campaigns.

But it was also tacky, as Rebekah had decorated it with random artsy-craftsy touches: ceramic figurines, throw pillows, holiday decorations. In the living room, she had a magnificent grand piano, and on top of it was a clusterfuck of knickknacks and framed family photos. Rebekah was an interesting case. She had studied biology and mathematics at Stanford and earned a master’s in operations research and engineering economic systems. She had then followed her father into trading at Renaissance Technologies but left when she began homeschooling her children. In 2006, she and her sisters bought a bakery in Manhattan, so her life became primarily about kids and chocolate chunk cookies. She had a super-perky air about her, like some kind of right-wing cheerleader. And because she had so much money to give, she was an influential person in GOP circles. Unlike more cynical Republican Party operatives, she had what Mark Block called “TB”—she was a true believer in these conservative crusades.

This brought to mind experiments from the 1990s in a niche field of sociology called “artificial societies,” which involved attempts by crude multi-agent systems to “grow” societies in silico. I could remember as a teenager reading Isaac Asimov’s Foundation series, where scientists used large data sets about societies to create the field of “psychohistory,” which allowed them to not only predict the future but also control it. Mercer had involved people from his company Renaissance Technologies in the original scoping of SCL, and, given that Nix was so focused on money and a hedge fund was part of the early stages of this project, everyone was under the impression that this was going to become a commercial venture. To put it crudely, if we could copy everyone’s data profiles and replicate society in a computer—like the game The Sims but with real people’s data—we could simulate and forecast what would happen in society and the market.


pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial intermediation, fixed income, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, trade route, transaction costs, value at risk, yield curve, zero-sum game

167 Forecasting Methodologies 168 Tradable News 173 Application of Event Arbitrage 175 Conclusion 184 CHAPTER 13 Statistical Arbitrage in High-Frequency Settings 185 Mathematical Foundations 186 Practical Applications of Statistical Arbitrage 188 Conclusion 199 viii CONTENTS CHAPTER 14 Creating and Managing Portfolios of High-Frequency Strategies 201 Analytical Foundations of Portfolio Optimization 202 Effective Portfolio Management Practices 211 Conclusion 217 CHAPTER 15 Back-Testing Trading Models 219 Evaluating Point Forecasts 220 Evaluating Directional Forecasts 222 Conclusion 231 CHAPTER 16 Implementing High-Frequency Trading Systems 233 Model Development Life Cycle 234 System Implementation 236 Testing Trading Systems 246 Conclusion 249 CHAPTER 17 Risk Management 251 Determining Risk Management Goals 252 Measuring Risk 253 Managing Risk 266 Conclusion 271 CHAPTER 18 Executing and Monitoring High-Frequency Trading 273 Executing High-Frequency Trading Systems 274 Monitoring High-Frequency Execution 280 Conclusion 281 Contents ix CHAPTER 19 Post-Trade Profitability Analysis 283 Post-Trade Cost Analysis 284 Post-Trade Performance Analysis 295 Conclusion 301 References 303 About the Web Site 323 About the Author 325 Index 327 Acknowledgments This book was made possible by a terrific team at John Wiley & Sons: Deb Englander, Laura Walsh, Bill Falloon, Tiffany Charbonier, Cristin RiffleLash, and Michael Lisk. I am also immensely grateful to all reviewers for their comments, and to my immediate family for their encouragement, edits, and good cheer. xi CHAPTER 1 Introduction igh-frequency trading has been taking Wall Street by storm, and for a good reason: its immense profitability. According to Alpha magazine, the highest earning investment manager of 2008 was Jim Simons of Renaissance Technologies Corp., a long-standing proponent of high-frequency strategies. Dr. Simons reportedly earned $2.5 billion in 2008 alone. While no institution was thoroughly tracking performance of highfrequency funds when this book was written, colloquial evidence suggests that the majority of high-frequency managers delivered positive returns in 2008, whereas 70 percent of low-frequency practitioners lost money, according to the New York Times.

European time zones give Londoners an advantage in trading currencies, and Singapore firms tend to specialize in Asian markets. While high-frequency strategies can be run from any corner of the world at any time of day, natural affiliations and talent clusters emerge at places most conducive to specific types of financial securities. The largest high-frequency names worldwide include Millennium, DE Shaw, Worldquant, and Renaissance Technologies. Most of the highfrequency firms are hedge funds or other proprietary investment vehicles 4 HIGH-FREQUENCY TRADING TABLE 1.1 Classification of High-Frequency Strategies Typical Holding Period Strategy Description Automated liquidity provision Quantitative algorithms for optimal pricing and execution of market-making positions <1 minute Market microstructure trading Identifying trading party order flow through reverse engineering of observed quotes <10 minutes Event trading Short-term trading on macro events <1 hour Deviations arbitrage Statistical arbitrage of deviations from equilibrium: triangle trades, basis trades, and the like <1 day that fly under the radar of many market participants.

MARKET PARTICIPANTS Competitors High-frequency trading firms compete with other investment management firms for quick access to market inefficiencies, for access to trading and operations capital, and for recruiting of talented trading strategists. Competitive investment management firms may be proprietary trading divisions of investment banks, hedge funds, and independent proprietary trading operations. The largest independent firms deploying high-frequency strategies are DE Shaw, Tower Research Capital, and Renaissance Technologies. Investors Investors in high-frequency trading include fund of funds aiming to diversify their portfolios, hedge funds eager to add new strategies to their existing mix, and private equity firms seeing a sustainable opportunity to create wealth. Most investment banks offer leverage through their “prime” services. Services and Technology Providers Like any business, a high-frequency trading operation requires specific support services.


Fortunes of Change: The Rise of the Liberal Rich and the Remaking of America by David Callahan

affirmative action, Albert Einstein, American Legislative Exchange Council, automated trading system, Bernie Sanders, Bonfire of the Vanities, carbon footprint, carried interest, clean water, corporate social responsibility, David Brooks, demographic transition, desegregation, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Thorp, financial deregulation, financial independence, global village, Gordon Gekko, greed is good, high net worth, income inequality, Irwin Jacobs: Qualcomm, Jeff Bezos, John Markoff, Kickstarter, knowledge economy, knowledge worker, Marc Andreessen, Mark Zuckerberg, market fundamentalism, medical malpractice, mega-rich, Mitch Kapor, Naomi Klein, NetJets, new economy, offshore financial centre, Peter Thiel, plutocrats, Plutocrats, profit maximization, quantitative trading / quantitative finance, Ralph Nader, Renaissance Technologies, Richard Florida, Robert Bork, rolodex, Ronald Reagan, school vouchers, short selling, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, stem cell, Steve Ballmer, Steve Jobs, unpaid internship, Upton Sinclair, Vanguard fund, War on Poverty, working poor, World Values Survey

Over 100 of our employees hold PhDs, almost 40 are entrepreneurs who previously founded their own companies, and approximately 20 percent are published authors whose work ranges from highly technical papers in specialized academic journals to award-winning mystery novels.” This is definitely not the Sarah Palin demographic. With a crew like this, it’s no wonder that 94 percent of the firm’s political contributions in the 2008 election cycle went to Democrats. Or that Larry Summers chose D. E. Shaw as a place to hang out after being booted from the president’s office at Harvard University. Another brainy hedge fund, Renaissance Technologies, has much the same profile. That firm, whose employees collectively contributed almost half a million dollars to Democrats in the 2008 election—and nearly no one in the firm gave money to Republicans— was founded by one of the most successful quantitative traders of all time, James Simons. In 2008, during the great bear market, Simons earned an almost unbelievable $2.8 billion, and he made nearly that much in 2009.

“Our trading models tend to buy stocks that are recently out of favor and sell those recently in favor,” Simons explained in 2008. During its first eleven years of operation, Simons’s Medallion Fund achieved a stunning cumulative return of 2,478 percent. Fortune has called Simons the “smartest billionaire,” and his firm has come as close as anyone has yet, it would seem, to inventing a money-making machine. Simons steered far clear of Wall Street in building Renaissance Technologies. The firm operates out of a fifty-acre campus on Long Island, near Stony Brook, and is run by people you’d more likely encounter in a faculty lounge that at a pricey steak house. “We hire physicists, mathematicians, astronomers and computer scientists and they typically know nothing about finance,” Simons once explained in a speech. “We haven’t hired out of Wall Street at all.”2 The culture at Renaissance is decidedly casual, and suits are scarce.

These individuals may start out narrowly focused on making money, but they develop a social conscience later in life— once big money is in the bank—that leads to liberal politics. This has been Robert Schulman’s trajectory. Schulman grew up in a working-class neighborhood in Brooklyn in the late 1950s and c02.indd 38 5/11/10 8:51:54 AM 39 what’s the matter with connecticut? Contributions from Hedge Fund Employees: 2008 Election Cycle Company Elliott Management Fortress Investment Group Citadel Investment Group SAC Capital Partners D.E. Shaw & Co. Renaissance Technologies Oaktree Capital Management Tudor Investment Paulson & Co. HBK Capital Management Total Dem GOP $1,507,990 $676,107 $572,828 $535,050 $519,415 $471,900 $363,150 $361,200 $329,246 $319,900 1% 88% 68% 80% 94% 95% 76% 77% 62% 58% 99% 12% 32% 20% 6% 5% 24% 23% 38% 42% Source: Center for Responsive Politics. the 1960s, the son of a small business owner. His parents were nominally Democratic but conservative.


pages: 162 words: 50,108

The Little Book of Hedge Funds by Anthony Scaramucci

Andrei Shleifer, asset allocation, Bernie Madoff, business process, carried interest, corporate raider, Credit Default Swap, diversification, diversified portfolio, Donald Trump, Eugene Fama: efficient market hypothesis, fear of failure, fixed income, follow your passion, Gordon Gekko, high net worth, index fund, John Meriwether, Long Term Capital Management, mail merge, margin call, mass immigration, merger arbitrage, money market fund, Myron Scholes, NetJets, Ponzi scheme, profit motive, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, risk/return, Ronald Reagan, Saturday Night Live, Sharpe ratio, short selling, Silicon Valley, Thales and the olive presses, Thales of Miletus, the new new thing, too big to fail, transaction costs, Vanguard fund, Y2K, Yogi Berra, zero-sum game

He understands the Jones model and uses it to make superior returns regardless of market conditions.”4 However, his greatest impact on the industry may indeed lie in the generation of hedge fund managers that his genius spurred. Known throughout hedge fund land as “Tiger Cubs,” nearly 20 percent of all assets run by money managers were once employed by Tiger. Other large players emerged from the hidden cloak of mystery, including Paul Tudor Jones’ Tudor Investment Corporation, James Simons’ Renaissance Technology, and Louis Bacon’s Moore Capital. And there were hosts of others, including Tom Steyer, Richard Perry, and Oscar Shafer, all of whom had a competitive edge that they were exploiting in the markets to yield absolute returns and great performance. The Revenge of the Nerds In early 2000, hedge funds were in trouble. Despite the success of a few managers who successfully navigated the tech stock world, many hedge funds fell victim to the speculatory market that was saturated with growth stocks.

Inside the Mind of a Super Capitalist As Mallaby so keenly reports, “Hedge funds are vehicles for loners and contrarians, for individualists whose ambitions are too big to fit into established financial institutions.” They aren’t the corporate obsequious types. And yet, hedge fund managers come in many different shapes and sizes—from PhDs in quantitative finance (Cliff Asness of AQR Capital Management) to college students trading convertible bonds out of their Ivy League dorm rooms (Ken Griffin of Citadel) to nerdy, mathematical quants (James Simons of Renaissance Technologies) to hyper, passionate, active traders (Daniel Loeb of Third Point). As it would be impossible to define the true essence of a hedge fund manager, below are some interesting—and somewhat humorous—insights into the psychographic portraits of these masters of the universe. According to a survey conducted by Russ Alan Prince, author of Fortune Fortress: Money Talks: 89.8 percent of hedge fund professionals view the hedge fund business as the way to become rich.


pages: 198 words: 53,264

Big Mistakes: The Best Investors and Their Worst Investments by Michael Batnick

activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, asset allocation, bitcoin, Bretton Woods, buy and hold, buy low sell high, cognitive bias, cognitive dissonance, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, endowment effect, financial innovation, fixed income, hindsight bias, index fund, invention of the wheel, Isaac Newton, John Meriwether, Kickstarter, Long Term Capital Management, loss aversion, mega-rich, merger arbitrage, Myron Scholes, Paul Samuelson, quantitative easing, Renaissance Technologies, Richard Thaler, Robert Shiller, Robert Shiller, Snapchat, Stephen Hawking, Steve Jobs, Steve Wozniak, stocks for the long run, transcontinental railway, value at risk, Vanguard fund, Y Combinator

Everyone wants to find the next Microsoft. But there a plethora of problems come with swinging for the fences. First of all and most obviously, they are incredibly difficult to come by. The 50 largest hedge funds do 50% of all NYSE listed stock trading, and the smallest one spends $100 million annually buying information.19 Imagine that you were physically exchanging stock certificates with Jim Simons of Renaissance Technologies every time you went to buy or sell a stock. This is who you're playing against. The idea that you will stumble upon riches by dumb luck alone is possible, but a little naive. The second problem, and this is a problem we all wish for, is that once you've experienced outsized success in the stock market, you crave a similar rush. Earning 4% tax free in municipal bonds doesn't quite have the same feeling as earning a multithousand return.

., founding, 132 Paulson, John, 3, 129, 131–132 merger/arbitrage, 133 Pearson, Mike, 113 Buffett, contrast, 114 Pellegrini, Paolo, 132–133 Penn Dixie Cement, shares (purchase), 58 Pershing Square Capital Management, 89 Pittsburgh National Bank, 101 Plasmon (Twain investment), 28 Polaroid, trading level, 70 Poppe, David, 114 Portfolio turnover, 69 Portugal, Ireland, Italy, Greece, Spain (PIIGS), 158 Post‐go‐go years meltdown, 147 Post III, William, 131 Price, Teddy, 19–20 Princeton University, 47–48 Private/public investing, history, 149 Profit sharing, 68 Prospect Theory (Kahneman/Tversky), 126 Pyramid schemes, 93 Qualcomm, gains, 57 Quantitative easing program, 134–135 Quantum Fund, 100, 103 Ramirez, Alberto/Rosa, 132 Rational thinking, suspension, 27 Recession, odds (calculation), 38 Renaissance Technologies, 135 Return on equity, term (usage), 4 Reverse crash, 100 Risk, arrival, 32 Risk management, 23 Roaring Twenties, bull market cycle, 7 Robertson, Julian, 58 Roche, Cullen, 99 Rockefeller, John, 30 Rogers, Henry (“Hell Hound”), 30–32 Rooney, Frank, 80, 81 Rosenfeld, Eric, 39, 41 Ruane, Bill, 4, 109, 112 Ruane & Cunniff, 112 Ruane, Cunniff & Goldfarb, 110–111 Russell 3000, 135 Russia, Quantum Fund loss, 103–104 Sacca, Chris, 145, 149–150 Salomon Brothers, 39 Buffett investment, 79 Samuelson, Paul (remarks), 51 San Francisco Call, 31 Schloss, Walter, 4 Schmidt, Eric, 150 Scholes, Myron, 39 Nobel Prize in Economics, 40–41 Schroeder, Alice, 80 Schwager, Jack, 159 Sears, Ackman targeting, 90 Sears Holdings, 109 Securities and Exchange Act, 7 Securities and Exchange Commission (SEC) 13D registration, 90 creation, 22 Security Analysis (Graham), 3–5 See's Candy Berkshire Hathaway purchase, 78 purchase, 142 Self‐esteem, satisfaction (impact), 75–76 Sequoia Fund, 107 operation, 110–111 Shiller, Robert, 75–76, 87 Short squeeze, 93 Silvan, Jon, 94 Simmons, Bill, 151 Simons, Jim, 135 Slack, Sacca investment, 149 Smith, Adam, 68, 121 Snapchat, 151 Snap, going public, 151 Snowball, The, (Schroeder), 80 Social activities, engagement, 87–88 Soros Fund Management, losses, 105 Soros, George, 58, 60, 100, 103 interaction, 102 reform, 121 South Sea Company shares, 37 Speculation, 15 avoidance, 28 SPY, 62 Stagecoach Corporate Stock Fund, 52 Stamp revenues, trading, 141–142 Standard Oil, 30 Standard & Poor's 500 (S&P500) ETF, 62 gains, 112, 114 performance, comparison, 119 shorting, 163 Valeant performance, comparison, 113 Steinhardt, Fine, Berkowitz & Company, opening, 58 Steinhardt, Michael, 55, 58 performance record, 59–60 Steinhardt Overseas Fund, 60 Stoker, Bram, 30 Stock market, choices, 114–115 Stocks crashing/reverse crashing, 100 return, 99 stock‐picking ability, 88 Stock trader, training, 18 Strategic Aggressive Investing Fund, 102 Sunk cost, 110 Sun Valley Conference, 57 “Superinvestors of Graham‐and‐Doddsville, The,” 111–112 Taleb, Nassim, 42 Target, Ackman targeting, 90 TDP&L, 50 Tech bubble, inflation, 57 Technivest, 50 Thaler, Richard H., 75, 126 Thinking, Fast and Slow, (Kahneman), 15 Thorndike, Dorain, Paine & Lewis, Inc., 48 Time horizons, 120 Time Warner, AOL merger, 49 Tim Ferriss Show, The, (podcast), 150 Tim Hortons, spinoff, 89 Tract on Monetary Reform, A, (Keynes), 125–126 Trader (Jones), 119 Trustees Equity Fund, decline, 50 Tsai, Jerry, 65, 68 stocks, trading, 69 ten good games, 71 Tsai Management Research, sale, 70 Tversky, Amos, 81 Twain, Mark (Samuel Clemens), 25, 27, 75 bankruptcy filings, 32 money, losses, 27–32 public opinion, hypersensitivity, 31 Twilio, Sacca investment, 149 Twitter, Sacca investment, 149–150 Uber, Sacca investment, 149 Undervalued issues, selection, 10 Union Pacific, shares (sale), 18 United Copper, cornering, 19 United States housing bubble, 132 University Computing, trading level, 70 US bonds international bonds, spreads, 41 value, decline, 61 U.S. housing bubble, impact, 132 U.S.


pages: 701 words: 199,010

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

affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sam Peltzman, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Winner of the 1997 Nobel prize in economics. Alan Schwartz: CEO and President of Bear Stearns during 2008. William Sharpe: Professor at Stanford University and co-inventor of the CAPM. Won the 1990 Nobel in economics for his work on asset pricing theory. Robert Shustak: CFO of LTCM. Currently CFO and COO of the hedge fund founded by Sanford Grossman, QFS. James Simons: Founder and CEO of Renaissance Technologies, one of the most successful quantitative hedge funds. This hedge fund also suffered during the Quant crisis. Simons was a mathematician prior to his entry into finance. George Soros: Founder of Soros Fund Management. Famous for his hedge fund bet that the British pound would devalue. Warren Spector: Co-President of Bear Stearns and Head of Mortgages and Fixed Income. John Thain: Chairman and CEO of Merrill Lynch during the financial crisis.

Collateral markdowns had left the funds unable to meet margin calls, and Sowood needed help.8 The Bear Stearns and Sowood hedge fund failures alerted markets to the possibility of spillover effects from problems in the credit and housing markets, though most investors treated the fund failures as isolated events. Then came the August 2007 quant crisis. Between August 1 and August 10, 2007, quantitative hedge funds lost abnormally large amounts of money. Some funds closed. For example, by August 10, Renaissance Technologies,9 the amazing algorithmic hedge fund, was down 8.7% in the first days of August and down 7.4% year to date. HighBridge Statistical Opportunities Fund was down 18% for the month; Tykhe Capital LLC, a New York-based quantitative fund, was down 20% for the month; AQR’s flagship fund was down 13% by August 10; by August 14, 2007, Goldman Sachs Global Equity Opportunities Fund had lost more than 30% in one week.10 What Was the Quant Crisis?

Sowood’s name came from South Woodside Avenue, the street in Wellesley, Massachusetts, where Larson lived when he started at Harvard Management. 8. Citadel Investment Group, a large investment management firm with $14 billion under management at the time, also assumed what remained of Amaranth’s natural gas financial swap book in October 2006. Amaranth, another hedge fund, spectacularly went bust in September 2006. 9. Mathematician James Simons, who earned his BS from MIT and his PhD from UC Berkeley, started Renaissance Technologies in 1982. As of late 2011, it managed around $15 billion. Its most famous offering is the Medallion Fund, which is closed to outside investors. Rumors say it consistently returned 35% net of fees from 1989 to 2007, though there’s no way to really know. The funds charge much higher fees than do typical hedge funds, relying on great performance to attract investors. Medallion charges management fees of 5% and incentive fees of 36%.


pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, beat the dealer, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, business cycle, butterfly effect, buy and hold, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, John Nash: game theory, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, martingale, Myron Scholes, new economy, Paul Lévy, Paul Samuelson, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, The Chicago School, The Myth of the Rational Market, tulip mania, Vilfredo Pareto, volatility smile

His contributions to physics and mathematics are as theoretical as could be, with a focus on classifying the features of complex geometrical shapes. It’s hard to even call him a numbers guy — once you reach his level of abstraction, numbers, or anything else that resembles traditional mathematics, are a distant memory. He is not someone you would expect to find wading into the turbulent waters of hedge fund management. And yet, there he is, the founder of the extraordinarily successful firm Renaissance Technologies. Simons created Renaissance’s signature fund in 1988, with another mathematician named James Ax. They called it Medallion, after the prestigious mathematics prizes that Ax and Simons had won in the sixties and seventies. Over the next decade, the fund earned an unparalleled 2,478.6% return, blowing every other hedge fund in the world out of the water. To give a sense of how extraordinary this is, George Soros’s Quantum Fund, the next most successful fund during this time, earned a mere 1,710.1% over the same period.

The idea is that the value of a Big Mac hamburger from McDonald’s is a reliable constant that can be used to compare the value of money in different countries and at different times.) Together, Malaney and Weinstein developed an entirely novel way of solving the index number problem by adapting a tool from mathematical physics known as gauge theory. (The early mathematical development of modern gauge theory — the topic on which Weinstein wrote his dissertation — was largely the work of Jim Simons, the mathematical physicist turned hedge fund manager who founded Renaissance Technologies in the 1980s.) Gauge theories use geometry to compare apparently incomparable physical quantities. This, Malaney and Weinstein argued, was precisely what was at issue in the index number problem — although there, instead of incomparable physical quantities, one was trying to compare different economic variables. It was an unusual, highly technical way of thinking about economics.

The crisis was equally a failure of government policy and regulation, since the shadow banking system that ultimately collapsed ran with essentially no oversight. Either regulators didn’t know what was happening, they didn’t understand the risks, or they believed that the industry would regulate itself. The crisis resulted from failures on all fronts. It’s worth emphasizing once again that just as O’Connor survived the 1987 crash by being a little more sophisticated in how it used its models than anyone else, Jim Simons’s Renaissance Technologies returned 80% in 2008 — again by being smarter than the competition. What’s the difference between Renaissance and other hedge funds? It’s that Renaissance has figured out a way to do what my dissertation advisor claimed was impossible: do science on Wall Street. This has not involved airing its ideas publicly. Indeed, Renaissance is more secretive than most. But its employees haven’t forgotten how to think like physicists, how to question their assumptions and constantly search for the chinks in their models’ armor.


pages: 258 words: 71,880

Street Fighters: The Last 72 Hours of Bear Stearns, the Toughest Firm on Wall Street by Kate Kelly

bank run, buy and hold, collateralized debt obligation, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, fixed income, housing crisis, index arbitrage, Long Term Capital Management, margin call, moral hazard, quantitative hedge fund, Renaissance Technologies, risk-adjusted returns, shareholder value, technology bubble, too big to fail, traveling salesman

Bear was now trading at about $65 on the New York Stock Exchange. Overnight, some of Bear’s lenders—the dozens of American and overseas banks that extended it billions of dollars a day to conduct business—began tightening the reins. The Dutch bank ING refused to refresh some of Bear’s credit, and others soon followed suit. Right away, Bear’s major clients heard the message: The firm was no longer safe. Hedge funds like Renaissance Technologies Corp., the enormous trading firm that had long been a top client, began reducing their balance levels immediately, worrying that if Bear went down, their money would be stuck on a sinking ship. Bear shares fell further, even amid public denials by Molinaro and others that any real trouble was afoot. Monday, Three Days Earlier Alan Schwartz had spent the weekend in Palm Beach. He had flown there initially to attend a board dinner for one of his longtime clients, the wireless telephone company Verizon Communications, and planned to stay on for an investment conference.

Novelly, Paul Obama, Barack Oros, John Overlander, Craig OwnIt Mortgage Solutions Pandit, Vikram Parr, Gary acquisition and investment efforts of board of directors and Flowers team and prime brokerage unit sale and worries about Schwartz of Parsons, Dick Paulson, Hank Ackermann’s call with background of Bush’s discussions with crisis of confidence feared by deal price and Flowers bid and Geithner’s calls with at Goldman TV appearances of Paulson, John Paulson, Wendy Peloton Partners LLP Perelman, Ronald Peretié, Michel Petrie, Milton PIMCO “Plan to Save Bear Stearns—Important—Please Read” Portney, Emily Presidential Advisory Committee President’s Working Group on Financial Markets prime brokerage division, Bear attempted sale of Goldman group and Morgan Stanley and withdrawals from Primerica private-client services (PCS) rating agencies Redstone, Sumner Reed, John Refco Renaissance Technologies Corp. repurchase agreements (repo loans) Residential Capital risk-based capital allocation plan risk management of Bear Rose, Charlie Royal Bank of Canada (RBC) Royal Bank of Scotland (RBS) Rubin, Howard Rubin, Robert Sachs, Lee Salerno, Fred Sallie Mae Salomon Brothers Salomon Inc. Santander Bank Schneider, Alison Schneider, Jack Schoenthal, David Schorr, Glenn Schwartz, Adam Schwartz, Alan acquisition efforts and background of on bankruptcy board meeting called by Cayne’s reliance on CNBC interview of compensation of Dimon’s calls with Fed loan time frame and Geithner’s calls with H.


pages: 505 words: 142,118

A Man for All Markets by Edward O. Thorp

3Com Palm IPO, Albert Einstein, asset allocation, beat the dealer, Bernie Madoff, Black Swan, Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, carried interest, Chuck Templeton: OpenTable:, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Edward Thorp, Erdős number, Eugene Fama: efficient market hypothesis, financial innovation, George Santayana, German hyperinflation, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Meriwether, John Nash: game theory, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, margin call, Mason jar, merger arbitrage, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, price anchoring, publish or perish, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, RFID, Richard Feynman, risk-adjusted returns, Robert Shiller, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, Stanford marshmallow experiment, statistical arbitrage, stem cell, stocks for the long run, survivorship bias, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration

Consider that Long-Term Capital Management, which had the crème de la crème of financial economists, blew up spectacularly in 1998, losing a multiple of what they thought their worst-case scenario was. The second method, that of the information theorists as pioneered by Ed, is practiced by traders and scientist-traders. Every surviving speculator uses explicitly or implicitly this second method (evidence: Ray Dalio, Paul Tudor Jones, Renaissance Technologies, even Goldman Sachs!). I said every because, as Peters and Gell-Mann have shown, those who don’t will eventually go bust. And thanks to that second method, if you inherit, say, $82,000 from uncle Morrie, you know that a strategy exists that will allow you to double the inheritance without ever going through bankruptcy. — Some additional wisdom I personally learned from Thorp: Many successful speculators, after their first break in life, get involved in large-scale structures, with multiple offices, morning meetings, coffee, corporate intrigues, building more wealth while losing control of their lives.

Small investors like my niece and my house cleaner, watching the equity index funds in their IRAs plunge, asked me if they should dump their stocks. Many investors had to sell, including the wealthiest university endowment fund in the country, Harvard’s, valued at $36.9 billion in early 2008 but now desperate for cash. Hedge funds, which were supposed to protect investors against such declines, dropped an average 18 percent. Even so, the most highly compensated hedge fund manager, James Simons of Renaissance Technologies, made $2.5 billion. The top twenty-five managers collected $11.6 billion, down from $22.5 billion in 2007. It was now twenty years after the end of Princeton Newport Partners, and hedge funds had proliferated until there were ten thousand worldwide, with total equity estimated at $2 trillion. Their worldwide pool of wealthy investors is a mix of private individuals, trusts, corporations, pension and profit-sharing plans, foundations, and endowments.

Business Day headline New York Times, September 28, 2000. CHAPTER 19 billion shares annually The Medallion Fund, a hedge fund closed to new investors, run by mathematician James Simons, includes a similar and far larger trading operation than ours with a higher rate of turnover and a vast annual trading volume. Now an investment vehicle for Simons and his associates in his firm Renaissance Technologies Corporation, it is probably the most successful hedge fund in history. of our researchers David Gelbaum. Gerry Bamberger For this and much more, see A Demon of Our Design, by Richard Bookstaber, Wiley, New York, 2008. in the securities industry See the book The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, by Scott Patterson, Crown, New York, 2010.


pages: 321

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

algorithmic trading, asset allocation, automated trading system, backtesting, barriers to entry, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial intermediation, Flash crash, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, popular capitalism, prediction markets, price discovery process, profit motive, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

But formulating the problems in such a way that the new machines could solve them required a new type of market operator, who historically had not been part of the sales and trading ecosystem: PhDs and other analytically minded individuals, not traditionally Wall Street material, became sought-after contributors to this new and modernized version of the trading floor. A NEW INDUSTRY One of the early adopters of computer-based investment methods to exploit systematic alphas was James Simons, an award-winning mathematician and former chair of the mathematics department at Stony Brook University. In 1982, Simons founded Renaissance Technologies, an East Setauket, New York-based firm that became known for the successful deployment of systematic market-neutral strategies. Six years later, former Columbia University computer science professor, David Shaw, launched D.E. Shaw & Co. in New York City. Shaw had spent two years at Morgan Stanley, part of a group whose mandate was to develop stock forecasting algorithms using historical price records.

Shaw & Co. 8 design 25–30 automated searches 111–120 backtesting 33–41 case study 31–41 core concepts 3–6 data inputs 4, 25–26, 43–47 evaluation 28–29 expressions 4 flow chart 41 future performance 29–30 horizons 4–50 intraday alphas 219–221 machine learning 121–126 noise reduction 26 optimization 29–30 prediction frequency 27 quality 5 risk-on/risk off alphas 246–247 robustness 89–93 smoothing 54–55, 59–60 triple-axis plan 83–88 universe 26 value 27–30 digital filters 127–128 digitization 7–9 dimensionality 129–132 disclosures 192 distressed assets 202–203 diversification automated searches 118–119 exchange-traded funds 233 portfolios 83–88, 108 DL see deep learning dot (inner) product 63–64 Dow, Charles 7 DPIN see dynamic measure of the probability of informed trading drawdowns 106–107 dual timestamping 78 dynamic measure of the probability of informed trading (DPIN) 214–215 dynamic parameterization 132 early-exercise premium 174 earnings calls 181, 187–188 earnings estimates 184–185 earnings surprises 185–186 efficiency, automated searches 111–113 Index295 efficient markets hypothesis (EMH) 11, 135 ego 19 elegance of models 75 EMH see efficient markets hypothesis emotions 19 ensemble methods 124–125 ensemble performance 117–118 estimation of risk 102–106 historical 103–106 position-based 102–103 shrinkage 131 ETFs see exchange-traded funds Euclidean space 64–66 evaluation 13–14, 28–29 backtesting 13–14, 33–41, 69–76 bias 77–82 bootstrapping 107 correlation 28–29 cutting losses 20–21 data selection 74–75 drawdowns 107 information ratio 28 margin 28 overfitting 72–75 risk 101–110 robustness 89–93 turnover 49–60 see also validation event-driven strategies 195–205 business cycle 196 capital structure arbitrage 204–205 distressed assets 202–203 index-rebalancing arbitrage 203–204 mergers 196–199 spin-offs, split-offs & carve-outs 200–202 exchange-traded funds (ETFs) 223–240 average daily trading volume 239 challenges 239–240 merits 232–233 momentum alphas 235–237 opportunities 235–238 research 231–240 risks 233–235 seasonality 237–238 see also index alphas exit costs 19, 21 expectedness of news 164 exponential moving averages 54 expressions, simple 4 extreme alpha values 104 extrinsic risk 101, 106, 108–109 factor risk heterogeneity 234 factors financial statements 147 to alphas 148 failure modes 84 fair disclosures 192 fair value of futures 223 Fama–French three-factor model 96 familiarity bias 81 feature extraction 130–131 filters 127–128 finance blogs 181–182 finance portals 180–181, 192 financial statement analysis 141–154 balance sheets 143 basics 142 cash flow statements 144– 145, 150–152 corporate governance 146 factors 147–148 fundamental analysis 149–154 growth 145–146 income statements 144 negative factors 146–147 special considerations 147 finite impulse response (FIR) filters 127–128 296Index FIR filters see finite impulse response filters Fisher Transform 91 five-day reversion alpha 55–59 Float Boost 125 forecasting behavioral economics 11–12 computer adoption 7–9 frequencies 27 horizons 49–50 statistical arbitrage 10–11 UnRule 17–21 see also predictions formation of the industry 8–9 formulation bias 80 forward-looking bias 72 forwards 241–249 checklist 243–244 Commitments of Traders report 244–245 instrument groupings 242–243 seasonality 245–246 underlying assets 241–242 frequencies 27 full text analysis 164 fundamental analysis 149–154 future performance 29–30 futures 241–249 checklist 243–244 Commitments of Traders report 244–245 fair value 223 instrument groupings 242–243 seasonality 245–246 underlying assets 241–242 fuzzy logic 126 General Electric 200 generalized correlation 64–66 groupings, futures and forwards 242–243 group momentum 157–158 growth analysis 145–146 habits, successful 265–271 hard neutralization 108 headlines 164 hedge fund betas see risk factors hedge funds, initial 8–9 hedging 108–109 herding 81–82, 190–191 high-pass filters 128 historical risk measures 103–106 horizons 49–50 horizontal mergers 197 Huber loss function 129 humps 54 hypotheses 4 ideas 85–86 identity matrices 65 IIR filters see infinite impulse response filters illiquidity premium 208–211 implementation core concepts 12–13 triple-axis plan 86–88 inaccuracy of models 10–11 income statements 144 index alphas 223–240 index changes 225–228 new entrants 227–228 principles 223–225 value distortion 228–230 see also exchange-traded funds index-rebalancing arbitrage 203–204 industry formation 8–9 industry-specific factors 188–190 infinite impulse response (IIR) filters 127–128 information ratio (IR) 28, 35–36, 74–75 initial hedge funds 8–9 inner product see dot product inputs, for design 25–26 integer effect 138 intermediate variables 115 Index297 intraday data 207–216 expected returns 211–215 illiquidity premium 208–211 market microstructures 208 probability of informed trading 213–215 intraday trading 217–222 alpha design 219–221 liquidity 218–219 vs. daily trading 218–219 intrinsic risk 102–103, 105–106, 109 invariance 89 inverse exchange-traded funds 234 IR see information ratio iterative searches 115 Jensen’s alpha 3 L1 norm 128–129 L2 norm 128–129 latency 46–47, 128, 155–156 lead-lag effects 158 length of testing 75 Level 1/2 tick data 46 leverage 14–15 leveraged exchange-traded funds 234 limiting methods 92–93 liquidity effect 96 intraday data 208–211 intraday trading 218–219 and spreads 51 literature, as a data source 44 look-ahead bias 78–79 lookback days, WebSim 257–258 looking back see backtesting Lo’s hypothesis 97 losses cutting 17–21, 109 drawdowns 106–107 loss functions 128–129 low-pass filters 128 M&A see mergers and acquisitions MAC clause see material adverse change clause MACD see moving average convergence-divergence machine learning 121–126 deep learning 125–126 ensemble methods 124–125 fuzzy logic 126 look-ahead bias 79 neural networks 124 statistical models 123 supervised/unsupervised 122 support vector machines (SVM) 122, 123–124 macroeconomic correlations 153 manual searches, pre-automation 119 margin 28 market commentary sites 181–182 market effects index changes 225–228 see also price changes market microstructure 207–216 expected returns 211–215 illiquidity premium 208–211 probability of informed trading 213–215 types of 208 material adverse change (MAC) clause 198–199 max drawdown 35 max stock weight, WebSim 257 mean-reversion rule 70 mean-squared error minimization 11 media 159–167 academic research 160 categorization 163 expectedness 164 finance information 181–182, 192 momentum 165 novelty 161–162 298Index sentiment 160–161 social 165–166 mergers and acquisitions (M&A) 196–199 models backtesting 69–76 elegance 75 inaccuracy of 10–11 see also algorithms; design; evaluation; machine learning; optimization momentum alphas 155–158, 165, 235–237 momentum effect 96 momentum-reversion 136–137 morning sunshine 46 moving average convergencedivergence (MACD) 136 multiple hypothesistesting 13, 20–21 narrow framing 81 natural gas reserves 246 negative factors, financial statements 146–147 neocognitron models 126 neural networks (NNs) 124 neutralization 108 WebSim 257 newly indexed companies 227–228 news 159–167 academic research 160 categories 163 expectedness 164 finance information 181–182, 192 momentum 165 novelty 161–162 relevance 162 sentiment 160–161 volatility 164–165 NNs see neural networks noise automated searches 113 differentiation 72–75 reduction 26 nonlinear transformations 64–66 normal distribution, approximation to 91 novelty of news 161–162 open interest 177–178 opportunities 14–15 optimization 29–30 automated searches 112, 115–116 loss functions 128–129 of parameter 131–132 options 169–178 concepts 169 open interest 177–178 popularity 170 trading volume 174–177 volatility skew 171–173 volatility spread 174 option to stock volume ratio (O/S) 174–177 order-driven markets 208 ordering methods 90–92 O/S see option to stock volume ratio outliers 13, 54, 92–93 out-of-sample testing 13, 74 overfitting 72–75 data mining 79–80 reduction 74–75, 269–270 overnight-0 alphas 219–221 overnight-1 alphas 219 parameter minimization 75 parameter optimization 131–132 PCA see principal component analysis Pearson correlation coefficients 62–64, 90 peer pressure 156 percent profitable days 35 performance parameters 85–86 Index299 PH see probability of heuristicdriven trading PIN see probability of informed trading PnL see profit and loss pools see portfolios Popper, Karl 17 popularity of options 170 portfolios correlation 61–62, 66 diversification 83–88, 108 position-based risk measures 102–103 positive bias 190 predictions 4 frequency 27 horizons 49–50 see also forecasting price changes analyst reports 190 behavioral economics 11–12 efficient markets hypothesis 11 expressions 4 index changes 225–228 news effects 159–167 relative 12–13, 26 price targets 184 price-volume strategies 135–139 pride 19 principal component analysis (PCA) 130–131 probability of heuristic-driven trading (PH) 214 probability of informed trading (PIN) 213–215 profit and loss (PnL) correlation 61–62 drawdowns 106–107 see also losses profit per dollar traded 35 programming languages 12 psychological factors see behavioral economics put-call parity relation 174 Python 12 quality 5 quantiles approximation 91 quintile distributions 104–105 quote-driven markets 208 random forest algorithm 124–125 random walks 11 ranking 90 RBM see restricted Boltzmann machine real estate investment trusts (REITs) 227 recommendations by analysts 182–183 recurrent neural networks (RNNs) 125 reduction of dimensionality 130–131 of noise 26 of overfitting 74–75, 269–270 of risk 108–109 Reg FD see Regulation Fair Disclosure region, WebSim 256 regions 85–86 regression models 10–11 regression problems 121 regularization 129 Regulation Fair Disclosure (Reg FD) 192 REITs see real estate investment trusts relationship models 26 relative prices 12–13, 26 relevance, of news 162 Renaissance Technologies 8 research 7–15 analyst reports 179–193 automated searches 111–120 backtesting 13–14 300Index behavioral economics 11–12 computer adoption 7–9 evaluation 13–14 exchange-traded funds 231–240 implementation 12–13 intraday data 207–216 machine learning 121–126 opportunities 14–15 perspectives 7–15 statistical arbitrage 10–11 triple-axis plan 83–88 restricted Boltzmann machine (RBM) 125 Reuleaux triangle 70 reversion alphas, five-day 55–59 risk 101–110 arbitrage 196–199 control 108–109 drawdowns 106–107 estimation 102–106 extrinsic 101, 106, 108–109 intrinsic 102–103, 105–106, 109 risk factors 26, 95–100 risk-on/risk off alphas 246–247 risk-reward matrix 267–268 RNNs see recurrent neural networks robustness 89–93, 103–106 rules 17–18 evaluation 20–21 see also algorithms; UnRule Russell 2000 IWM fund 225–226 SAD see seasonal affective disorder scale of automated searches 111–113 search engines, analyst reports 180–181 search spaces, automated searches 114–116 seasonality exchange-traded funds 237–238 futures and forwards 245–246 momentum strategies 157 and sunshine 46 selection bias 77–79, 117–118 sell-side analysts 179–180 see also analyst reports sensitivity tests 119 sentiment analysis 160–161, 188 shareholder’s equity 151 Sharpe ratios 71, 73, 74–75, 221, 260 annualized 97 Shaw, David 8 shrinkage estimators 131 signals analysts report 190, 191–192 cutting losses 20–21 data sources 25–26 definition 73 earnings calls 187–188 expressions 4 noise reduction 26, 72–75 options trading volume 174–177 smoothing 54–55, 59–60 volatility skew 171–173 volatility spread 174 sign correlation 65 significance tests 119 Simons, James 8 simple moving averages 55 simulation backtesting 71–72 WebSim settings 256–258 see also backtesting size factor 96 smoothing 54–55, 59–60 social media 165–166 sources of data 25–26, 43–44, 74–75 automated searches 113–114 see also data sparse principal component analysis (sPCA) 131 Spearman’s rank correlation 90 Index301 special considerations, financial statements 147 spin-offs 200–202 split-offs 200–202 spreads and liquidity 51 and volatility 51–52 stat arb see statistical arbitrage statistical arbitrage (stat arb) 10–11, 69–70 statistical models, machine learning 123 step-by-step construction 5, 41 storage costs 247–248 storytelling 80 subjectivity 17 sunshine 46 supervised machine learning 122 support vector machines (SVM) 122, 123–124 systemic bias 77–80 TAP see triple-axis plan tax efficiency, exchange-traded funds 233 teams 270–271 temporal-based correlation 63–64, 65 theory-fitting 80 thought processes of analysts 186–187 tick data 46 timestamping and bias 78–79 tracking errors 233–234 trades cost of 50–52 crossing effect 52–53 latency 46–47 trend following 18 trimming 92 triple-axis plan (TAP) 83–88 concepts 83–86 implementation 86–88 tuning of turnover 59–60 see also smoothing turnover 49–60 backtesting 35 control 53–55, 59–60 costs 50–52 crossing 52–53 examples 55–59 horizons 49–50 smoothing 54–55, 59–60 WebSim 260 uncertainty 17–18 underlying principles 72–73 changes in 109 understanding data 46 unexpected news 164 universes 26, 85–86, 239–240, 256 UnRule 17–18, 20–21 unsupervised machine learning 122 validation, data 45–46 valuation methodologies 189 value of alphas 27–30 value distortion, indices 228–230 value factors 96 value investing 96, 141 variance and bias 129–130 vendors as a data source 44 vertical mergers 197 volatility and news 164–165 and spreads 51–52 volatility skew 171–173 volatility spread 174 volume of options trading 174–177 price-volume strategies 135–139 volume-synchronized probability of informed trading (VPIN) 215 302Index VPIN see volume-synchronized probability of informed trading weather effects 46 WebSim 253–261 analysis 258–260 backtesting 33–41 data types 255 example 260–261 settings 256–258 weekly goals 266–267 weighted moving averages 55 Winsorization 92–93 Yahoo finance 180 Z-scoring 92


pages: 332 words: 91,780

Starstruck: The Business of Celebrity by Currid

"Robert Solow", barriers to entry, Bernie Madoff, Donald Trump, income inequality, index card, industrial cluster, Mark Zuckerberg, Metcalfe’s law, natural language processing, place-making, Ponzi scheme, post-industrial society, prediction markets, Renaissance Technologies, Richard Florida, Robert Metcalfe, rolodex, shareholder value, Silicon Valley, slashdot, transaction costs, upwardly mobile, urban decay, Vilfredo Pareto, winner-take-all economy

Not only do most successful financiers loathe the media, they also couldn’t attain a profile without having the deals, the flair, as well as the salaries that make them mediaworthy, and even then, many top financiers remain out of the public eye.17 Most investment bankers and hedge fund managers do not seek out media attention, because it is not a requirement to make money, which is the essential goal of their job.18 In 2008, James Simons, a hedge fund manager at Renaissance Technologies, made $2.5 billion. The next year, after the 2008 tallies of earnings were available and Simons was publicly known to be at the top of the heap, his name made it into only 246 news stories. John Arnold of Centaurus Energy made $1.5 billion and was named in a mere 408 stories; Raymond Dalio of Bridgewater Associates generated $780 million in earnings and yet he was mentioned in just 16 news stories.

Ono, Yoko Oppenheimer, Jerry Oscars, see Academy Awards Owl and Weasel (newsletter) Oxford University Page, Larry Pakistan Palencia, Francisco Palin, Sarah Paltrow, Gwyneth paparazzi Paramount Pictures Pareto Principle Paris Park City (Utah) Parliament, British participatory culture path dependency Pattinson, Robert Peake, John Penguin Press Penn, Sean People for the Ethical Treatment of Animals (PETA) People magazine PepsiCo PerezHilton.com; see also Hilton, Perez personal digital assistant (PDA) applications Pew Research Center Peyton-Jones, Julia Phoenix, River Physical Impossibility of Death in the Mind of Someone Living, The (Hirst) Picasso, Pablo Pinsky, Drew Pitt, Brad Pittsburgh Playboy Polaroid Scene Politico newspaper and blog politics; academic; British; celebrity residual in; geography of stardom and; networks in Ponzi schemes pop art Popeye Series (Koons) Pop Idol (TV show) Pop magazine Portman, Natalie Posen, Zac Price, Katie (“Jordan”) Princeton University Proenza fashion house Project Runway (TV show) publicists; democratic celebrities and; fees of; geography of stardom and; lack of, in Bollywood Puppy (Koons) Putnam, Robert Queen Latifah Radar magazine Rainie, Lee RateMyProfessors.com Ravid, Gilad RCA Records Reader, The (film) reality TV; British; financial celebrities on; narcissism of; relative celebrities on; stars of; see also specific shows Real Madrid Real World, The (TV show) Reed, David Reed, Lou Reid, Tara Rein, Irving Reinhardt, Doug relative celebrities; in academia; in art world; characteristics of,; mainstream celebrities versus; networks of Reliance film company Renaissance Technologies Republican Party residual, see celebrity residual Reuters Reynolds, Jamie Richards, Mark Richter, Gerhard Ripa, Kelly Ritchie, Guy Rivera, Mariano Roberts, Julia Rodarte fashion house Rodriguez, Alex (A-Rod) ROFLCon Rogers and Cowan Rolling Stone Roman Empire Romer, Paul Ronaldinho (Ronaldo de Assis Moreira) Ronaldo, Cristiano Ronson, Samantha Rose, Jessica Rosen, Sherwin Rosenfield, Stan Ross, Andrew Roubini, Nouriel Rousing, Hans Rousseau, Jean-Jacques Royal Festival Hall (London) Ruscha, Ed Ruth, Babe Saatchi, Charles Sachs, Jeffrey Salomon, Rick Salomon Brothers Samuels, David Sandler, Adam San Francisco 49ers São Paulo, traffic in Schouler fashion house Schroeder, Alice Science magazine Scientific American Scientologists Scorsese, Martin Scotland Seacrest, Ryan Seattle Mariners Secret Service Sedgwick, Edie Seipp, Catherine Senate, U.S.


pages: 598 words: 169,194

Bernie Madoff, the Wizard of Lies: Inside the Infamous $65 Billion Swindle by Diana B. Henriques

accounting loophole / creative accounting, airport security, Albert Einstein, banking crisis, Bernie Madoff, break the buck, British Empire, buy and hold, centralized clearinghouse, collapse of Lehman Brothers, computerized trading, corporate raider, diversified portfolio, Donald Trump, dumpster diving, Edward Thorp, financial deregulation, financial thriller, fixed income, forensic accounting, Gordon Gekko, index fund, locking in a profit, mail merge, merger arbitrage, money market fund, plutocrats, Plutocrats, Ponzi scheme, Potemkin village, random walk, Renaissance Technologies, riskless arbitrage, Ronald Reagan, short selling, Small Order Execution System, source of truth, sovereign wealth fund, too big to fail, transaction costs, traveling salesman

It was just a routine examination of the brokerage firm’s books and records, they said, the kind of thing that happens all the time. This was not entirely true; the examination was not routine. It was a belated response to a set of e-mails that an alert SEC staffer had found in the files of a prominent hedge fund firm during a truly routine examination nearly a year earlier. The fund manager, Renaissance Technologies, had an indirect stake in Madoff through its Meritor hedge fund. The Renaissance e-mails, written in late 2003, expressed the same mystification about Madoff’s performance and practices as the Barron’s and Ocrant articles had in the spring of 2001. In one of the e-mails, a senior executive shared his doubts with his investment committee. “First of all, we spoke to an ex-Madoff trader,” the executive said.

Questions were left hanging, but in early 2004 the shorthanded SEC staff members were told to shift their attention to a wide-ranging investigation of the mutual funds industry, which seemed more important because mutual funds were mainstream America’s primary investment vehicle. No one logged the tip from Harry Markopolos in 2001, or the nearly identical one from the hedge fund manager in 2003, into the agency’s internal data base of investigative information. So there were no records of those earlier, unexamined warnings when the e-mails from Renaissance Technologies were found in 2004. At least the Renaissance e-mails were taken seriously at the SEC—albeit at a glacial pace. In fact, they were the reason William David Ostrow and Peter Lamore were sitting in an office in the Lipstick Building in April 2005 watching Bernie Madoff lose his temper. Almost shouting, Madoff repeated his original question: “What are you looking for?” Lamore shot back, “Well, what do you want us to look for?

See also Securities and Exchange Commission; Securities Investors Protection Corporation; and specific firms and investigations automation and, 42, 44–46, 49 deregulation and, 79–80, 86, 121–22 Europe and, 68 fixed commissions and, 65–66 hedge funds and, 104, 131, 172 history of, 28, 35–36, 42 investor protection and, 341–47 Madoff criticizes, 180 negligence and, 78, 267 order flow controversy and, 87 reform of, post-2008, 241–42 Renaissance Technologies, 140–43, 157 Reserve fund, 195 Retirement Accounts Inc, 127 retirement savings, 172–73, 342–44 retrocession fees, 171 Richards, Lee S., III, 21–23, 222–23, 239 Richards, Lori, 140, 142, 145–46 Richmond Fairfield Associates, 147 Rockefeller, David, 174 Rogers, Casey & Barksdale, 130 Rogerscasey Inc, 132, 141 Ross, Burt, 276 Roth, Eric, 212 Rothko, Mark, 113 Rothschild et Cie, 170 Rye funds, 130–31 Sage, Maurice, 63, 65 Salomon Brothers, 71 Salomon Smith Barney, 118 Samuels, Andrew Ross, 293 Santa Clara fund, 169, 172 Sarbanes, Paul, 122 savings and loans crisis, 53 Schama, Simon, 213 Schapiro, Mary, 228–29, 241, 301–3, 326–27 Schlichter, Arthur, 63 Schulman, Robert I., 131 Schwartz, Michael, 276 Second Circuit Court of Appeals, 309, 324 Securities and Exchange Commission (SEC), 10, 31 Barron’s article and, 121–22 Chais and, 58, 301 Cohmad and, 300–301 deregulation and, 78–79 failure of, and reform post-2008, 240–42, 301–4, 311, 326–27 failure of, in Madoff case, 210, 227–30, 266, 296, 345 financial crisis of 2008 and, 196, 228–29 fixed commissions and, 66 Friehling and, 255, 301 hedge funds and, 126, 142, 172 investigation by, after Madoff confession, 17, 228, 239, 241, 245, 270–72 investigation of 1992, 94–102, 132, 272, 335 investigation of 2001–4, 138, 140, 145–46 investigation of 2005, 139–46, 151 investigation of late 2005–6, 153–59, 162–66, 172, 227, 271–72 Joel suspended by, 43 Kotz report of 2009, 302–4 Madoff arrest and, 17–18, 22, 224, 275 Madoff critique of, 180 Madoff employees charged by, 297–98, 310 Madoff family not charged by, 286, 293 Madoff sons report father to, 10, 12, 14 Madoff victims and, 220, 222, 236, 239, 264, 267–68, 303–4 Markopolos and, 122–25, 142–43, 153–57, 162, 227 NASD and, 86 Office of Compliance Inspections and Examinations, 121 OTC and, 45–46 regulations of 1970s, 42, 79 Shana Madoff’s husband and, 179 Securities Industry Association, 80 Securities Investor Protection Act (1970), 234, 262, 308 Securities Investor Protection Corporation (SIPC), 44 cash advances by, 220, 222, 260, 306–8 creditor meetings and, 244 indirect investors and, 235, 304–5, 325–26 legal expenses and, 246, 311 Madoff case taken by, 220–22, 224 net equity dispute and, 235–36, 242, 255, 259–66, 268, 307, 324–25 Picard assigned as trustee by, 216–18 Picower settlement and, 328–30 reform of, 311, 325–27 Sedgwick, Kyra, 212 September 11, 2001, attacks, 90, 125, 265 Shad, John, 79–80, 86, 96 Shapiro, Carl, 4, 61–62, 72–74, 84, 86, 92, 100–101, 137, 152, 158, 183–84, 205, 307, 335 philanthropy and, 62, 340 settlement by, 320 Shapiro, Ellen, 72–73 Shapiro, Ruth, 137 Shearson Lehman Hutton, 108 Sheehan, David J., 217–19, 222, 237, 239–40, 244, 258, 263–64, 267, 269, 294, 307–8, 311–16, 318–20, 328–30 Shopwell chain, 65 short sales, 30, 196 Siegman, Miriam, 276–77, 298 Simons, Nat, 141 Singapore, 171, 212 Smith Barney, 131 Sonar report, 322 Sorkin, Ira Lee “Ike,” 1–2, 6–7, 15–17, 20, 96–98, 101, 224, 238, 242–43, 248–51, 270, 273, 275, 277, 279 Sorkin, Nathan, 242, 248 Sorkin, Rosalie, 242, 248 Soros, George, xxiii, 60 sovereign immunity principle, 303–4 Soviet Union, former, 169 S&P 500, 83, 94, 118, 123, 176, 204, 296 Spain, 1, 239, 245 Spielberg, Steven, 212, 215, 340 Spitzer, Eliot, 241 split-strike conversion strategy, 75–77, 83–85, 112, 115, 117–18, 192, 196, 199, 271 size constraints and, 93, 106, 128 Sporkin, Stanley, 79 spread, defined, 45 Squadron, Howard, 88–89, 96, 242 Squillari, Eleanor, 3, 6–8, 11–14, 161 stagflation, 52 Stanton, Louis L., 22, 222, 300 Steinhardt, Michael, 25–26 Sterling Stamos fund, 148 stock market.


pages: 558 words: 168,179

Dark Money: The Hidden History of the Billionaires Behind the Rise of the Radical Right by Jane Mayer

affirmative action, Affordable Care Act / Obamacare, American Legislative Exchange Council, anti-communist, Bakken shale, bank run, battle of ideas, Berlin Wall, Capital in the Twenty-First Century by Thomas Piketty, carried interest, centre right, clean water, Climategate, Climatic Research Unit, collective bargaining, corporate raider, crony capitalism, David Brooks, desegregation, diversified portfolio, Donald Trump, energy security, estate planning, Fall of the Berlin Wall, George Gilder, housing crisis, hydraulic fracturing, income inequality, Intergovernmental Panel on Climate Change (IPCC), invisible hand, job automation, low skilled workers, mandatory minimum, market fundamentalism, mass incarceration, Mont Pelerin Society, More Guns, Less Crime, Nate Silver, New Journalism, obamacare, Occupy movement, offshore financial centre, oil shale / tar sands, oil shock, plutocrats, Plutocrats, Powell Memorandum, Ralph Nader, Renaissance Technologies, road to serfdom, Robert Mercer, Ronald Reagan, school choice, school vouchers, The Bell Curve by Richard Herrnstein and Charles Murray, The Chicago School, the scientific method, University of East Anglia, Unsafe at Any Speed, War on Poverty, working poor

In the wake of the 2008 market crash, as Obama and the Democrats began talking increasingly about Wall Street reforms, financiers like Schwarzman, Cohen, and Singer who flocked to the Koch seminars had much to lose. The hedge fund run by another of the Kochs’ major investors, Robert Mercer, an eccentric computer scientist who made a fortune using sophisticated mathematical algorithms to trade stocks, also seemed a possible government target. Democrats in Congress were considering imposing a tax on stock trading, which the firm he co-chaired, Renaissance Technologies, did in massive quantities at computer-driven high frequency. Although those familiar with his thinking maintained that his political activism was separate from his pecuniary interests, Mercer had additional business reasons to be antigovernment. The IRS was investigating whether his firm improperly avoided paying billions of dollars in taxes, a charge the firm denied. Employment laws, too, would prove an embarrassing headache to him; three domestic servants soon sued him for refusing to pay overtime and maintained that he had docked their wages unfairly for infractions such as failing to replace shampoo bottles from his bathrooms when they were less than one-third full.

(After The New Yorker published my investigative article on the Kochs, “Covert Operations,” that August, The Daily Caller was the chosen receptacle for the retaliatory opposition research on me, although, after it proved false, the Web site decided not to run it.) Only in 2011 did it surface that in New York, at least, the “Ground Zero mosque” controversy had been stirred up for political gain in part by money from Robert Mercer, the co-CEO of the $15 billion Long Island hedge fund Renaissance Technologies. To aid a conservative candidate in New York, Mercer gave $1 million to help pay for ads attacking supporters of the “Ground Zero mosque.” A former computer programmer who had a reputation as a brilliant mathematician and an eccentric loner, Mercer was a relative newcomer to the Koch summits. But he was immediately impressed by the organization. He had long held the government in low regard and shared the Kochs’ antipathy toward government regulations.

He said it would demonstrate that “the other side creates divisiveness, but we solve problems.” There were in fact more than a few connections between the defense bar and the Koch network. A surprising number of the donors had been ensnared in serious legal problems. Not only had the Kochs faced environmental, workplace safety, fraud, and bribery allegations; many others in their group had legal issues too. At that moment, Renaissance Technologies, the hedge fund co-directed by Bob Mercer, who had become an increasingly active member of the Koch network, was still under investigation by the Internal Revenue Service for avoiding more than $6 billion in taxes between 2000 and 2013. In a 2014 Senate inquiry, Democratic senator Carl Levin denounced the company’s accounting as a “pretty stunning bit of phony and abusive tax machinations.”


pages: 831 words: 98,409

SUPERHUBS: How the Financial Elite and Their Networks Rule Our World by Sandra Navidi

activist fund / activist shareholder / activist investor, assortative mating, bank run, barriers to entry, Bernie Sanders, Black Swan, Blythe Masters, Bretton Woods, butterfly effect, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, cognitive bias, collapse of Lehman Brothers, collateralized debt obligation, commoditize, conceptual framework, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, diversification, East Village, Elon Musk, eurozone crisis, family office, financial repression, Gini coefficient, glass ceiling, Goldman Sachs: Vampire Squid, Google bus, Gordon Gekko, haute cuisine, high net worth, hindsight bias, income inequality, index fund, intangible asset, Jaron Lanier, John Meriwether, Kenneth Arrow, Kenneth Rogoff, knowledge economy, London Whale, Long Term Capital Management, longitudinal study, Mark Zuckerberg, mass immigration, McMansion, mittelstand, money market fund, Myron Scholes, NetJets, Network effects, offshore financial centre, old-boy network, Parag Khanna, Paul Samuelson, peer-to-peer, performance metric, Peter Thiel, plutocrats, Plutocrats, Ponzi scheme, quantitative easing, Renaissance Technologies, rent-seeking, reserve currency, risk tolerance, Robert Gordon, Robert Shiller, Robert Shiller, rolodex, Satyajit Das, shareholder value, Silicon Valley, social intelligence, sovereign wealth fund, Stephen Hawking, Steve Jobs, The Future of Employment, The Predators' Ball, The Rise and Fall of American Growth, too big to fail, women in the workforce, young professional

Bank CEOs earn significantly less, as they head publicly-listed companies with utility-like character, who to a large extent deal with financially so-called unsophisticated investors. Also, their job has more of a corporate management nature, rather than solely an investment management one. The twenty-five best-paid hedge fund managers in 2013 earned a total of $21.1 billion, in 2014 $11.62 billion, and in 2015 $12.94 billion.24 As the Guardian points out, the $1.7 billion that the two top earners, Kenneth Griffin of Citadel and James Simons of Renaissance Technologies, made in 2015, is equivalent to the annual salaries of 112,000 people at a minimum wage of $15,080. In fact, Simon’s earnings were so large in 2015 that if he were a country, it would rate as the world’s 178th most productive nation.25 In 2013, George Soros (net worth $24.9 billion) led the pack with an estimated $4 billion. Since converting his fund into a family office, he’s no longer included in the hedge funder compensation lists.

See Network power undue concentration of, 164 Power lunches, 124–125 Power of Alumni Networks, The, 42 “Power-law distribution,” 20 “Predators’ Ball,” 192 Predictions, 50 Preferential attachment, xxvi “Pricelings,” 103 Prince, Chuck, 56, 139–140, 203 Princeton University, 36, 50, 157, 199 Principles, 63, 71 Printing money, 178, 211 Private banks, 37 Private equity firms, 61, 144 Private parties, 126–128 Private sector, 164–165 Prostitution, 194 Protectionism, 212 Psychological detachment, 223–224 Psychopaths, 66 Public sector, 164–165, 168 Purcell, Philip, 139 Putin, Vladimir, 114–116, 219 Q Qatari ruling family, 171 R Racial discrimination, 198, 200, 201, 203 “Radical truth,” 71 Rainbow Room, 109–110 Rama, Edi, 27 Rand, Ayn, 71 Rania, Queen, 114 Reddit, 203 Reflexivity theory, 63 Regular banks, 37 Regulatory capture, 164, 217–218 Reinhart, Carmen, 107 Relational capital, 97 Relational capture, 163, 217 Renaissance Technologies, 87–88 Renova Group, 144 “Rent-seeking,” 12, 22 Renumeration, 87 Reputation integrity as source of, 59 methods of bolstering, 40 status and, 22–23 Research firms, 43 Reset, 203 Residences, 89–92 Residential mortgages, 12 Resilience gap, 156–158 “Revolving door” phenomenon description of, 10–11, 163, 218 Robert Rubin as example of, 163–170 Tony Blair as example of, 170–172 Rhodes, Bill, 131 Rice, Condoleezza, 172 “Rich-get-richer phenomenon,” xxvii, 19, 77, 92 Rikers Island, 39, 193 “Rise of the Overclass, The,” 80 Risk, 30 Risk management systems, 30, 64 Risk taking, 66, 225 Ritz Hotel, 132 Robbins, Tony, 23 Robertson, Julian, 27 Robin Hood charity gala, 75–76 Robin Hood Foundation, 75–76, 103 Robinson, James D.


pages: 328 words: 97,711

Talking to Strangers: What We Should Know About the People We Don't Know by Malcolm Gladwell

Berlin Wall, Bernie Madoff, borderless world, crack epidemic, Ferguson, Missouri, financial thriller, light touch regulation, Mahatma Gandhi, Milgram experiment, moral panic, Ponzi scheme, Renaissance Technologies, Snapchat

If you want to understand how deception works, there is no better place to start. 4 In my book Blink, I wrote of Paul Ekman’s claim that a small number of people are capable of successfully detecting liars. For more on the Ekman-Levine debate, see the extended commentary in the Notes. 5 SAFE stands for Security Analyst File Environment. I love it when people start with the acronym and work backward to create the full name. Chapter Four The Holy Fool 1. In November 2003, Nat Simons, a portfolio manager for the Long Island–based hedge fund Renaissance Technologies, wrote a worried email to several of his colleagues. Through a complicated set of financial arrangements, Renaissance found itself with a stake in a fund run by an investor in New York named Bernard Madoff, and Madoff made Simons uneasy. If you worked in the financial world in New York in the 1990s and early 2000s, chances are you’d heard of Bernard Madoff. He worked out of an elegant office tower in Midtown Manhattan called the Lipstick Building.

The difference between Markopolos and Renaissance, however, is that Renaissance trusted the system. Madoff was part of one of the most heavily regulated sectors in the entire financial market. If he was really just making things up, wouldn’t one of the many government watchdogs have caught him already? As Nat Simons, the Renaissance executive, said later, “You just assume that someone was paying attention.” Renaissance Technologies, it should be pointed out, was founded in the 1980s by a group of mathematicians and code-breakers. Over its history, it has probably made more money than any other hedge fund in history. Laufer, the Renaissance executive to whom Simons turned for advice, has a PhD in mathematics from Princeton University and has written books and articles with titles such as Normal Two-Dimensional Singularities and “On Minimally Elliptic Singularities.”


Smart Mobs: The Next Social Revolution by Howard Rheingold

A Pattern Language, augmented reality, barriers to entry, battle of ideas, Brewster Kahle, Burning Man, business climate, citizen journalism, computer vision, conceptual framework, creative destruction, Douglas Engelbart, Douglas Engelbart, experimental economics, experimental subject, Extropian, Hacker Ethic, Hedy Lamarr / George Antheil, Howard Rheingold, invention of the telephone, inventory management, John Markoff, John von Neumann, Joi Ito, Joseph Schumpeter, Kevin Kelly, Metcalfe's law, Metcalfe’s law, more computing power than Apollo, New Urbanism, Norbert Wiener, packet switching, Panopticon Jeremy Bentham, pattern recognition, peer-to-peer, peer-to-peer model, pez dispenser, planetary scale, pre–internet, prisoner's dilemma, RAND corporation, recommendation engine, Renaissance Technologies, RFID, Richard Stallman, Robert Metcalfe, Robert X Cringely, Ronald Coase, Search for Extraterrestrial Intelligence, SETI@home, sharing economy, Silicon Valley, skunkworks, slashdot, social intelligence, spectrum auction, Steven Levy, Stewart Brand, the scientific method, transaction costs, ultimatum game, urban planning, web of trust, Whole Earth Review, zero-sum game

Katie Hafner, “Web Sites Begin to Self Organize,” New York Times, 18 January 2001, <http://www.nytimes.com/2001/01/18/technology/18SELF.html > (24 January 2002). 18. Everything2, <http://www.everything2.com/ > (9 February 2002). 19. Blogger, <http://www.blogger.com > (5 February 2002). 20. Farhad Manjoo, “Blah, Blah, Blah, and Blog,” Wired News, 18 February 2002, <http://www.wired.com/news/print/0,1294,50443,00.html > (24 February 2002). 21. Henry Jenkins, “Digital Renaissance,” Technology Review, March 2002, <http://www.technologyreview.com/articles/jenkins0302.asp > (24 February 2002). 22. Slashdot FAQ, <http://slashdot.org/faq/ > (9 February 2002). 23. Ibid. 24. eBay, <http://pages.ebay.com/community/aboutebay/overview/index.html > (5 February 2002). 25. Peter Kollock, “The Production of Trust in Online Markets,” in Advances in Group Processes 16, ed. E. J. Lawler et al.

John Geirland, “Mobile Community,” TheFeature.com, 24 September 2001, <http://thefeature.com/article.jsp?pageid=12836 > (1 March 2002). 39. Gordon Gould, Alex Levine, and Andrew Pimentel, interview by author, November 2001, New York. 40. ENGwear: Wearable Wireless Systems for Electronic News Gathering, <http://www.eyetap.org/hi/ENGwear/ > (1 March 2002). 41. Mann and Niedzviecki, Cyborg, 175176. 42. Ibid., 177178. 43. Hall, “Mobile Reporting.” 44. Henry Jenkins, “Digital Renaissance,” Technology Review, March 2002, <http://www.technologyreview.com/articles/jenkins0302.asp > (24 February 2002). 45. Gerd Kortuem et al., “When Peer-to-Peer Comes Face-to-Face: Collaborative Peer-to-Peer Computing in Mobile Ad Hoc Networks,” 2001 International Conference on Peer-to-Peer Computing (P2P2001), 2729 August 2001, Linköping, Sweden, <http://www.cs.uoregon.edu/research/wearables/Papers/p2p2001.pdf > (6 March 2002). 46.


pages: 733 words: 179,391

Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

"Robert Solow", Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, Sam Peltzman, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, survivorship bias, Thales and the olive presses, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

First, statarb was not nearly as popular Adaptive Markets in Action • 293 in 1998 as it was in 2007. The TASS hedge fund database we used in our study reported nineteen statarb funds in July 1998, compared to eightytwo in July 2007, and assets under management grew from $3 billion at the start of 1998 to $19 billion at the start of 2007 (and these figures don’t include leverage or funds that choose not to report to TASS, which includes D. E. Shaw, Renaissance Technologies, and several other very successful and secretive statarb funds). Therefore, statarb was not nearly as crowded a trade in 1998 as it was in 2007. Second, few commercial banks were involved in statarb in 1998, but because of the relatively low-risk/high-return performance of Shaw, Renaissance, and other statarb managers, and the growing need for higher yielding assets in the declining-yield environment of the early 2000s, these banks started to take an interest.

Markopolos submitted his findings several times to the SEC: in 2000, through its Boston office, which was never recorded reaching the SEC’s Northeast Regional Office (NERO);22 in 2001, which NERO decided not to pursue after one day’s analysis;23 in 2005, on which more below; via a significant follow-up email in 2007, which was “ignored,” in the words of the Office of Investigations report;24 and in April 2008, when the findings failed to arrive due to an incorrect email address.25 Two similar analyses were brought to the SEC’s attention, one directly and one indirectly. In May 2003, an unnamed hedge fund manager contacted the SEC’s Office of Compliance Inspections and Examinations (OCIE) with a parallel analysis.26 In November 2003, upper management at Renaissance Technologies (the quantitative hedge fund started by James Simons of chapter 7) became concerned that Madoff ’s returns were “highly unusual” and that “none of it seems to add up.” 27 In April 2004, this Renaissance correspondence was flagged for attention by a compliance examiner at NERO during a routine examination. The OCIE and NERO conducted two separate, independent examinations of Madoff. Each examination was unaware of the other, until Madoff himself informed examiners of their mutual existence.

See also Efficient Markets Hypothesis; Homo economicus rationalization, 76, 117 480 • Index Reagan, Ronald, 12 real estate investment trusts (REITs), 267 recessions, 200 refi nancing, 299, 301, 323 reflexivity, 219 Regulation D, 344 Regulation T, 256, 368 regulatory capture, 379, 393 regulatory forbearance, 62, 71 Reinhart, Carmen, 310 Reis, Ricardo, 7 relativity, 129–130, 133, 168 Renaissance Technologies, 293, 350 Repin, Dmitry V., 92, 94 representativeness, 67–68, 69 reserpine, 88 Reserve Primary Fund, 300, 321 reward system, 87, 88 Rhode, Paul W., 39 Ricci, Umberto, 31 Richardson, Matthew, 377 Ride, Sally, 13 right hemisphere, of brain, 113–117 Rilling, James, 337 risk: aversion to, 56, 57, 60, 61, 70, 90, 91, 107, 161, 162, 203–205, 351–352, 393; behavioral, 388–394; of biotechnology investments, 401–410; changing nature of, 315; in down markets, 282, 288, 291, 292, 399; financial crisis linked to excess of, 303–305; of hedge funds, 270, 283, 288, 291, 317; of high-frequency trading, 360; in housing market, 299, 318; idiosyncratic, 198–203, 205–206, 250, 251; management of, 83, 154, 270–273, 276, 282, 283, 288, 291, 305, 369–370, 377–378, 388–390; mea surement of, 371, 376, 386–387; misperceptions of, 62, 70, 82–85, 91, 108, 323; of reliance on trust, 344; reproductive, 194, 200, 201, 202, 205, 220; reward vs., 2, 17, 108, 203, 249–250, 253, 258–263, 268, 277, 282, 322; systematic, 194, 199–203, 204, 205, 250–251, 348, 389; systemic, 291, 315, 317, 319, 344, 361, 366–367, 370–371, 376–378, 384–385, 387; tolerance for, 27, 57, 204, 253, 263, 332, 390; transparency of, 384–385; uncertainty vs., 53–55, 415; variance equated with, 48 “Risk, Ambiguity, and the Savage Axioms” (Ellsberg), 52–54 risk premium, 250, 251, 268–269, 276 Rizzolatti, Giacomo, 110 Roberts, Harry, 23 Robertson, Julian, 234 Robinson, Patrick, 317 robotics, 181 Robson, Arthur J., 217 Rockwell International, 13, 14, 15 Roddenberry, Gene, 395, 411, 417 Rogers, Jim, 234 Rogers Commission, 12–13 Rogoff, Ken, 310 rogue trading, 61, 70, 71, 84, 189, 305 Roll, Richard, 23 Rolls, Edmund, 157 Rosenthal, Robert, 123–124 Rosling, Hans, 258 Rusnak, John, 61 Russia, 241–244, 292 Rutherford, Ernest, 214 Sachs, Jeff rey, 411 Sacks, Oliver, 88 safety: of automobiles, 205; in aviation, 85, 321, 379–383; of buildings, 378–379 Salomon Brothers, 241 Salow, Julie, 307 Samsonite Corporation, 264, 265 Samuelson, Larry, 271 Samuelson, Paul A., 20–21, 25, 34, 42, 140, 177, 178, 206–213 Sanofi S.A., 419 S&P 500 index, 251, 252, 264, 265, 270, 273, 274, 360 S&P 1500 index, 287, 324 Sanfey, Alan, 337 Santa Fe Institute (SFI), 218 Sarao, Navinder Singh, 360 satisficing, 180, 182, 183, 185, 213, 393 Savage, Leonard Jimmie, 19–20 savings and loan crisis, 44, 321 Schlesinger, Herbert, 223 Scholes, Myron, 27, 97, 241, 356–357, 384 Schüll, Natasha Dow, 91 Schultz, Henry, 31 Schumpeter, Joseph, 219 scientific method, 313, 314 second-order false belief, 111 secure multiparty computation, 385–387 Securities and Exchange Commission (SEC), 228, 306–311, 327, 350–352, 354, 355, 359, 360, 377 Index Securities Trading of Stock (STOC) exercise, 41–43 securitization, 297, 321, 407–409 seizures, 113 self-fulfi lling prophecies, 124 self-sacrifice, 168–169, 170, 196, 336 septal area, of brain, 87, 88 serotonin, 160 sexual attraction, 105, 170 Shakers, 165–166 Shamir, Adi, 238 Shapard, John, 166–167 Shapiro, Carl, 333 Shapiro, Jeremy, 414 Sharpe, William F., 27, 250–252, 253, 263 Shaw, David E., 224, 225, 236–240, 244, 248, 277 Shiller, Robert, 314–315 Shilling, A.


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

The firm’s 20-year record of consistent positive performance (alpha) led in 2007 to the sale of a 20 percent stake to Lehman Brothers for a sum reported to be in the billions. Perhaps the most secretive, and most successful, of these high-technology firms is Renaissance Technologies, founded by Jim Simons, former head of the mathematics department at Stony Brook University. How these firms have achieved their success is not something you read in the library or on the Web. Company web sites are short and cryptic. Renaissance Technologies, for example, has removed almost everything except the address from its site, www.rentec.com. However, we can tell by its appearance at the top of electronic trade volume lists that Renaissance is keeping its machinery very active in the market. Using the Internet Archive’s Wayback Machine,10 a digital time capsule named after Mr.


pages: 394 words: 112,770

Fire and Fury: Inside the Trump White House by Michael Wolff

Affordable Care Act / Obamacare, barriers to entry, Bernie Sanders, centre right, disintermediation, Donald Trump, drone strike, Edward Snowden, Elon Musk, forensic accounting, illegal immigration, impulse control, Jeff Bezos, Jeffrey Epstein, obamacare, Peter Thiel, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ronald Reagan, Saturday Night Live, self-driving car, Silicon Valley, single-payer health, Travis Kalanick, WikiLeaks, zero-sum game

They would devote vast sums—albeit still just a small part of Bob Mercer’s many billions—to trying to build a radical free-market, small-government, home-schooling, antiliberal, gold-standard, pro-death-penalty, anti-Muslim, pro-Christian, monetarist, anti-civil-rights political movement in the United States. Bob Mercer is an ultimate quant, an engineer who designs investment algorithms and became a co-CEO of one of the most successful hedge funds, Renaissance Technologies. With his daughter, Rebekah, Mercer set up what is in effect a private Tea Party movement, self-funding whatever Tea Party or alt-right project took their fancy. Bob Mercer is almost nonverbal, looking at you with a dead stare and either not talking or offering only minimal response. He had a Steinway baby grand on his yacht; after inviting friends and colleagues on the boat, he would spend the time playing the piano, wholly disengaged from his guests.

., 2, 8, 26–27, 41, 54, 90, 93, 212–13, 222 Nooyi, Indra, 88–89 North American Free Trade Agreement (NAFTA), 77 North Atlantic Treaty Organization (NATO), 99 North Korea, 291–93, 297 Nunberg, Sam, 11, 13, 16, 22, 144, 237–38, 248, 282, 291, 300 Nunes, Devin, 170 Obama, Barack, 27, 35–36, 41–45, 54, 61–63, 67, 90, 101, 104, 128, 164, 187, 215, 250, 269, 295 birth certificate and, 62, 295 DOJ and, 94–96, 210, 279 executive orders and, 61 farewell speech, 36 Flynn and, 101 immigration and, 63 Middle East and, 6–7, 42, 183, 190, 225, 227, 231, 263–66 Russia and, 95, 151–54, 156 Trump inauguration and, 43–44 White House Correspondents’ Dinner and, 198 wiretapping and, 157–60 Obamacare repeal and replace, 72, 116–17, 164–67, 170–71, 175, 224, 283, 285, 290 Office of American Innovation, 180–81, 207 Office of Management and Budget (OMB), 116, 185, 285 O’Neill, Tip, 167 opioid crisis, 291 O’Reilly, Bill, 195–96, 222 Organization for Economic Co-Operation and Development, 271 Oscar insurance company, 72 Osnos, Evan, 154 Page, Carter, 101 Palestinians, 227, 230–32 Panetta, Leon, 27 Paris Climate Accord, 182, 238–39, 301 PayPal, 21 Pelosi, Nancy, 78 Peña Nieto, Enrique, 77–78, 228 Pence, Karen, 124, 209 Pence, Mike, 92, 95, 106–7, 123–24, 171, 209, 218, 240 Pentagon, 7, 55 Perelman, Ronald, 73, 141 Perlmutter, Ike, 141 Petraeus, David, 263–64 Pierce, Brock, 56–57 Planned Parenthood, 117 Playbook, 171 Podesta, John, 27 Politico, 171 Pompeo, Mike, 49, 51, 157, 306 populists, 6, 24, 31, 100, 113, 118, 142, 174–75, 177, 276, 301 Powell, Dina, 81–82, 145–46, 176–77, 184–88, 190, 192–94, 229, 235–36, 258, 261, 265–67, 276, 279, 285, 296, 306 Preate, Alexandra, 1, 32, 130, 207–8, 238, 249, 275, 278–79, 299 Pre-Election Presidential Transition Act (2010), 24 Price, Tom, 165–66, 171, 291 Priebus, Reince, 77, 86, 144, 146, 150, 166, 171–73, 176, 203, 205, 207, 209, 229, 238, 257, 296, 304 business councils and, 89 campaign and, 9–10, 13, 18, 112–13 chief of staff appointment and, 26, 32–34, 60, 64–65, 67–70, 109–10, 117–24, 243–44, 305 CPAC and, 127, 130–34 Flynn and, 95, 106 inauguration and, 45, 52 Obama wiretapping story and, 159–60 resignation of, 282–85, 307 Russia investigation and, 171, 211–14, 216–17, 232–34, 261–62 Scaramucci and, 270–72, 282–85 Prince, Erik, 265, 267 Private Eye magazine, 74 Producers, The (film), 15–16 Pruitt, Scott, 21 Putin, Vladimir, 7, 8, 24, 37–38, 99–102, 153, 155 Qatar, 230–31 Raffel, Josh, 142, 207, 258–59, 279 Reagan, Ronald, 26, 27, 34, 58, 90, 126–27, 144, 201, 222 Remnick, David, 154 Renaissance Technologies, 58 Republican National Committee (RNC), 10–11, 13, 26, 28, 30, 32–33, 52, 112, 119, 172, 205 Republican National Convention, 21, 26, 28, 253 Republican Party, 2, 18, 30, 40–41, 81, 86, 98, 111–12, 117–21, 128, 161–67, 171–72, 201, 290, 303 fracturing of, 179–80, 253, 283, 306, 309–10 Rhodes, Ben, 41, 154, 159, 185, 215 Rice, Susan, 7, 41, 153 Rometty, Ginni, 88 Rose, Charlie, 309 Rosen, Hillary, 78 Rosenstein, Rod, 212, 214, 216–21, 279 Ross, Wilbur, 78, 133, 229–30 Roth, Steven, 27, 141 Rove, Karl, 57, 238 Rumsfeld, Donald, 27 Russia, 24, 37–39, 92, 151–56, 160, 190–91, 236–46, 273, 303, 307–8 Bannon on, 6–7, 238–40, 278–83 Comey and, 168–70, 210–20, 242, 244–45 Don Jr.


pages: 561 words: 120,899

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy by Sharon Bertsch McGrayne

Bayesian statistics, bioinformatics, British Empire, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, double helix, Edmond Halley, Fellow of the Royal Society, full text search, Henri Poincaré, Isaac Newton, Johannes Kepler, John Markoff, John Nash: game theory, John von Neumann, linear programming, longitudinal study, meta analysis, meta-analysis, Nate Silver, p-value, Pierre-Simon Laplace, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, Robert Mercer, Ronald Reagan, speech recognition, statistical model, stochastic process, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, uranium enrichment, Yom Kippur War

Or were Bayesian concepts about uncertainty only a handy metaphor? Former Reserve Board governor Alan S. Blinder of Princeton thought the latter, and when he said so during a talk, Greenspan was in the audience and did not object. In pragmatic contrast to abstract Bayes at the Nobel ceremonies and philosophical Bayes at the Federal Reserve, the rule stands behind one of the most successful hedge funds in the United States. In 1993 Renaissance Technologies hired away from IBM a Bayesian group of voice recognition researchers led by Peter F. Brown and Robert L. Mercer. They became comanagers of RenTech’s portfolio and technical trading. For several years, their Medallion Fund, limited to former and current employees, averaged annual returns of about 35%. The fund bought and sold shares so rapidly one day in 1997 that it accounted for more than 10% of all NASDAQ trades.

., 52, 58, 67, 87, 103, 147, 148, 233–34 RAND Corporation, 3, 88, 119–28, 194 randomization, 109 Rapp, Elizabeth R., 166, 169 Rasmussen, Norman Carl, 179–80 reason, 4, 35–36 Reber, Rufus K., 184, 191 Reed, Lowell J., 53 Rejewski, Marián, 62 religion: Bayes and, 3–5 Laplace and, 13–14, 14–15, 19–20, 30, 36 mathematics and, 4, 5–6, 11 science and, 30 statistics and, 253–54. See also God Renaissance Technologies, 237–38 Richardson, Henry R., 187–92, 194, 195, 197–209 Robbins, Herbert, 134 Robert, Christian P., 224 robotics, 240–41, 249 Rommel, Erwin, 81 Roosevelt, Franklin D., 76 Rosenberg, Ethel and Julius, 85 Rosenberg, James A., 201, 203 Rosenbluth, Arianna and Marshall, 223 Rounthwaite, Robert, 242 Royal Academy of Sciences, 15, 16, 18, 21, 22–23, 29 Royal Society, 4, 5, 9, 10–11, 50, 56–57 Royal Statistical Society, 87, 99, 107, 232 Rubinow, Isaac M., 43 safety: of coal mines, 216–17 of nuclear energy, x, 3, 117, 178–81 of nuclear weapons, 119–28, 182–83, 189–90, 194–95 of space shuttles, x, 103, 215 satellites, 209 Saunderson, Nicholas, 9 Savage, Leonard Jimmie: on Birnbaum, 132 death of, 176 de Finetti and, 95–96 economics and, 135 epidemiology and, 116, 117 on fiducial probability, 132 on Fisher, 46 influence of, 147, 148 Lindley’s Paradox and, 133 mathematics and, 148 Mosteller and, 159 nuclear weapons and, 119–20 practical applications and, 139, 150, 156, 157, 161 probability and, 233–34 publication by, 99, 101–7 on Schlaifer, 142, 148 subjectivity and, 169, 173, 178 Tukey and, 169 at University of Chicago, 156 Savage, Richard, 102 Schlaifer, Robert: biographical details on, 140–41 business and, 141–43, 144, 145, 146–53, 168, 171 computers and, 177 conjugate priors and, 125, 148 practical applications and, 156, 157, 161 Schleifer, Arthur, Jr., 140, 147 Schneider, Stephen H., 235 Schrödinger, Erwin, 105 Schwartz, Andrew B., 249 science, 30, 167.


pages: 478 words: 126,416

Other People's Money: Masters of the Universe or Servants of the People? by John Kay

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, buy and hold, 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, disruptive innovation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial innovation, financial intermediation, financial thriller, 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, information asymmetry, intangible asset, interest rate derivative, interest rate swap, invention of the wheel, Irish property bubble, Isaac Newton, John Meriwether, light touch regulation, London Whale, Long Term Capital Management, loose coupling, low cost airline, low cost carrier, M-Pesa, market design, millennium bug, mittelstand, money market fund, moral hazard, mortgage debt, Myron Scholes, NetJets, new economy, Nick Leeson, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shock, passive investing, Paul Samuelson, 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, 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

In the end, the LTCM trades were settled profitably by the investment banks which had taken them over: a telling illustration of Keynes’s (possibly apocryphal) dictum that ‘markets can remain irrational for longer than you can stay solvent’.4 More recently, the mathematical analysis of trading patterns has enabled some algorithmic traders to make returns from minute movements in the prices of securities. The most persistently successful of these quantitative-oriented funds are the Renaissance Technologies funds of Jim Simons, which have over more than two decades earned extraordinary returns for investors while charging equally extraordinary levels of fee. Simons was a distinguished mathematician before taking to finance. The early and successful practitioners of this quantitative style could use sophisticated methods to identify recurrent patterns in data, and arbitrage anomalies in the manner of LTCM.

.: Hyperion 220 Loomis, Carol 108 lotteries 65, 66, 68, 72 Lucas, Robert 40 Lynch, Dennios 108 Lynch, Peter 108, 109 M M-Pesa 186 Maastricht Treaty (1993) 243, 250 McCardie, Sir Henry 83, 84, 282, 284 McGowan, Harry 45 Machiavelli, Niccolò 224 McKinley, William 44 McKinsey 115, 126 Macy’s department store 46 Madoff, Bernard 29, 118, 131, 132, 177, 232, 293 Madoff Securities 177 Magnus, King of Sweden 196 Manhattan Island, New York: and Native American sellers 59, 63 Manne, Henry 46 manufacturing companies, rise of 45 Marconi 48 marine insurance 62, 63 mark-to-market accounting 126, 128–9, 320n22 mark-to-model approach 128–9, 320n21 Market Abuse Directive (MAD) 226 market economy 4, 281, 302, 308 ‘market for corporate control, the’ 46 market risk 97, 98, 177, 192 market-makers 25, 28, 30, 31 market-making 49, 109, 118, 136 Markets in Financial Instruments Directive (MIFID) 226 Markkula, Mike 162, 166, 167 Markopolos, Harry 232 Markowitz, Harry 69 Markowitz model of portfolio allocation 68–9 Martin, Felix 323n5 martingale 130, 131, 136, 139, 190 Marx, Groucho 252 Marx, Karl 144, 145 Capital 143 Mary Poppins (film) 11, 12 MasterCard 186 Masters, Brooke 120 maturity transformation 88, 92 Maxwell, Robert 197, 201 Mayan civilisation 277 Meade, James 263 Means, Gardiner 51 Meeker, Mary 40, 167 Melamed, Leo 19 Mercedes 170 merchant banks 25, 30, 33 Meriwether, John 110, 134 Merkel, Angela 231 Merrill Lynch 135, 199, 293, 300 Merton, Robert 110 Metronet 159 Meyer, André 205 MGM 33 Microsoft 29, 167 middleman, role of the 80–87 agency and trading 82–3 analysts 86 bad intermediaries 81–2 from agency to trading 84–5 identifying goods and services required 80, 81 logistics 80, 81 services from financial intermediaries 80–81 supply chain 80, 81 transparency 84 ‘wisdom of crowds’ 86–7 Midland Bank 24 Milken, Michael 46, 292 ‘millennium bug’ 40 Miller, Bill 108, 109 Minuit, Peter 59, 63 Mises, Ludwig von 225 Mittelstand (medium-size business sector) 52, 168, 169, 170, 171, 172 mobile banking apps 181 mobile phone payment transfers 186–7 Modigliani-Miller theorem 318n9 monetarism 241 monetary economics 5 monetary policy 241, 243, 245, 246 money creation 88 money market fund 120–21 Moneyball phenomenon 165 monopolies 45 Monte Carlo casino 123 Monte dei Paschi Bank of Siena 24 Montgomery Securities 167 Moody’s rating agency 21, 248, 249, 313n6 moral hazard 74, 75, 76, 92, 95, 256, 258 Morgan, J.P. 44, 166, 291 Morgan Stanley 25, 40, 130, 135, 167, 268 Morgenthau, District Attorney Robert 232–3 mortality tables 256 mortgage banks 27 mortgage market fluctuation in mortgage costs 148 mechanised assessment 84–5 mortgage-backed securities 20, 21, 40, 85, 90, 100, 128, 130, 150, 151, 152, 168, 176–7, 284 synthetic 152 Mozilo, Angelo 150, 152, 154, 293 MSCI World Bank Index 135 muckraking 44, 54–5, 79 ‘mugus’ 118, 260 multinational companies, and diversification 96–7 Munger, Charlie 127 Munich, Germany 62 Munich Re 62 Musk, Elon 168 mutual funds 27, 108, 202, 206 mutual societies 30 mutualisation 79 mutuality 124, 213 ‘My Way’ (song) 72 N Napoleon Bonaparte 26 Napster 185 NASA 276 NASDAQ 29, 108, 161 National Economic Council (US) 5, 58 National Employment Savings Trust (NEST) 255 National Institutes of Health 167 National Insurance Fund (UK) 254 National Provincial Bank 24 National Science Foundation 167 National Westminster Bank 24, 34 Nationwide 151 Native Americans 59, 63 Nazis 219, 221 neo-liberal economic policies 39, 301 Netjets 107 Netscape 40 Neue Markt 170 New Deal 225 ‘new economy’ bubble (1999) 23, 34, 40, 42, 98, 132, 167, 199, 232, 280 new issue market 112–13 New Orleans, Louisiana: Hurricane Katrina disaster (2005) 79 New Testament 76 New York Stock Exchange 26–7, 28, 29, 31, 49, 292 New York Times 283 News of the World 292, 295 Newton, Isaac 35, 132, 313n18 Niederhoffer, Victor 109 NINJAs (no income, no job, no assets) 222 Nixon, Richard 36 ‘no arbitrage’ condition 69 non-price competition 112, 219 Norman, Montagu 253 Northern Rock 89, 90–91, 92, 150, 152 Norwegian sovereign wealth fund 161, 253 Nostradamus 274 O Obama, Barack 5, 58, 77, 194, 271, 301 ‘Obamacare’ 77 Occidental Petroleum 63 Occupy movement 52, 54, 312n2 ‘Occupy Wall Street’ slogan 305 off-balance-sheet financing 153, 158, 160, 210, 250 Office of Thrift Supervision 152–3 oil shock (1973–4) 14, 36–7, 89 Old Testament 75–6 oligarchy 269, 302–3, 305 oligopoly 118, 188 Olney, Richard 233, 237, 270 open market operations 244 options 19, 22 Organisation for Economic Co-operation and Development (OECD) 263 Osborne, George 328n19 ‘out of the money option’ 102, 103 Overend, Gurney & Co. 31 overseas assets and liabilities 179–80, 179 owner-managed businesses 30 ox parable xi-xii Oxford University 12 P Pacific Gas and Electric 246 Pan Am 238 Paris financial centre 26 Parliamentary Commission on Banking Standards 295 partnerships 30, 49, 50, 234 limited liability 313n14 Partnoy, Frank 268 passive funds 99, 212 passive management 207, 209, 212 Patek Philippe 195, 196 Paulson, Hank 300 Paulson, John 64, 109, 115, 152, 191, 284 ‘payment in kind’ securities 131 payment protection policies 198 payments system 6, 7, 25, 180, 181–8, 247, 259–60, 281, 297, 306 PayPal 167, 168, 187 Pecora, Ferdinand 25 Pecora hearings (1932–34) 218 peer-to-peer lending 81 pension funds 29, 98, 175, 177, 197, 199, 200, 201, 208, 213, 254, 282, 284 pension provision 78, 253–6 pension rights 53, 178 Perkins, Charles 233 perpetual inventory method 321n4 Perrow, Charles 278, 279 personal financial management 6, 7 personal liability 296 ‘petrodollars’ 14, 37 Pfizer 96 Pierpoint Morgan, J. 165 Piper Alpha oil rig disaster (1987) 63 Ponzi, Charles 131, 132 Ponzi schemes 131, 132, 136, 201 pooled investment funds 197 portfolio insurance 38 Potts, Robin, QC 61, 63, 72, 119, 193 PPI, mis-selling of 296 Prebble, Lucy: ENRON 126 price competition 112, 219 price discovery 226 price mechanism 92 Prince, Chuck 34 private equity 27, 98, 166, 210 managers 210, 289 private insurance 76, 77 private sector 78 privatisation 39, 78, 157, 158, 258, 307 probabilistic thinking 67, 71, 79 Procter & Gamble 69, 108 product innovation 13 property and infrastructure 154–60 protectionism 13 Prudential 200 public companies, conversion to 18, 31–2, 49 public debt 252 public sector 78 Q Quandt, Herbert 170 Quandt Foundation 170 quantitative easing 245, 251 quantitative style 110–11 quants 22, 107, 110 Quattrone, Frank 167, 292–3 queuing 92 Quinn, Sean 156 R railroad regulation 237 railway mania (1840s) 35 Raines, Franklin 152 Rajan, Raghuram 56, 58, 79, 102 Rakoff, Judge Jed 233, 294, 295 Ramsey, Frank 67, 68 Rand, Ayn 79, 240 ‘random walk’ 69 Ranieri, Lew 20, 22, 106–7, 134, 152 rating agencies 21, 41, 84–5, 97, 151, 152, 153, 159, 249–50 rationality 66–7, 68 RBS see Royal Bank of Scotland re-insurance 62–3 Reagan, Ronald 18, 23, 54, 59, 240 real economy 7, 18, 57, 143, 172, 190, 213, 226, 239, 271, 280, 288, 292, 298 redundancy 73, 279 Reed, John 33–4, 48, 49, 50, 51, 242, 293, 314n40 reform 270–96 other people’s money 282–5 personal responsibility 292–6 principles of 270–75 the reform of structure 285–92 robust systems and complex structures 276–81 regulation 215, 217–39 the Basel agreements 220–25 and competition 113 the origins of financial regulation 217–19 ‘principle-based’ 224 the regulation industry 229–33 ‘rule-based’ 224 securities regulation 225–9 what went wrong 233–9 ‘Regulation Q’ (US) 13, 14, 20, 28, 120, 121 regulatory agencies 229, 230, 231, 235, 238, 274, 295, 305 regulatory arbitrage 119–24, 164, 223, 250 regulatory capture 237, 248, 262 Reich, Robert 265, 266 Reinhart, C.M. 251 relationship breakdown 74, 79 Rembrandts, genuine/fake 103, 127 Renaissance Technologies 110, 111, 191 ‘repo 105’ arbitrage 122 repo agreement 121–2 repo market 121 Reserve Bank of India 58 Reserve Primary Fund 121 Resolution Trust Corporation 150 retirement pension 78 return on equity (RoE) 136–7, 191 Revelstoke, first Lord 31 risk 6, 7, 55, 56–79 adverse selection and moral hazard 72–9 analysis by ‘ketchup economists’ 64 chasing the dream 65–72 Geithner on 57–8 investment 256 Jackson Hole symposium 56–7 Kohn on 56 laying bets on the interpretation of incomplete information 61 and Lloyd’s 62–3 the LMX spiral 62–3, 64 longevity 256 market 97, 98 mitigation 297 randomness 76 socialisation of individual risks 61 specific 97–8 risk management 67–8, 72, 79, 137, 191, 229, 233, 234, 256 risk premium 208 risk thermostat 74–5 risk weighting 222, 224 risk-pooling 258 RJR Nabisco 46, 204 ‘robber barons’ 44, 45, 51–2 Robertson, Julian 98, 109, 132 Robertson Stephens 167 Rockefeller, John D. 44, 52, 196 Rocket Internet 170 Rogers, Richard 62 Rogoff, K.S. 251 rogue traders 130, 300 Rohatyn, Felix 205 Rolls-Royce 90 Roman empire 277, 278 Rome, Treaty of (1964) 170 Rooney, Wayne 268 Roosevelt, Franklin D. v, 25, 235 Roosevelt, Theodore 43–4, 235, 323n1 Rothschild family 217 Royal Bank of Scotland 11, 12, 14, 24, 26, 34, 78, 91, 103, 124, 129, 135, 138, 139, 211, 231, 293 Rubin, Robert 57 In an Uncertain World 67 Ruskin, John 60, 63 Unto this Last 56 Russia defaults on debts 39 oligarchies 303 Russian Revolution (1917) 3 S Saes 168 St Paul’s Churchyard, City of London 305 Salomon Bros. 20, 22, 27, 34, 110, 133–4 ‘Salomon North’ 110 Salz Review: An Independent Review of Barclays’ Business Practices 217 Samuelson, Paul 208 Samwer, Oliver 170 Sarkozy, Nicolas 248, 249 Savage, L.J. 67 Scholes, Myron 19, 69, 110 Schrödinger’s cat 129 Scottish Parliament 158 Scottish Widows 26, 27, 30 Scottish Widows Fund 26, 197, 201, 212, 256 search 195, 209, 213 defined 144 and the investment bank 197 Second World War 36, 221 secondary markets 85, 170, 210 Securities and Exchange Commission (SEC) 20, 64, 126, 152, 197, 225, 226, 228, 230, 232, 247, 292, 293, 294, 313n6 securities regulation 225–9 securitisation 20–21, 54, 100, 151, 153, 164, 169, 171, 222–3 securitisation boom (1980s) 200 securitised loans 98 See’s Candies 107 Segarra, Carmen 232 self-financing companies 45, 179, 195–6 sell-side analysts 199 Sequoia Capital 166 Shad, John S.R. 225, 228–9 shareholder value 4, 45, 46, 50, 211 Sharpe, William 69, 70 Shell 96 Sherman Act (1891) 44 Shiller, Robert 85 Siemens 196 Siemens, Werner von 196 Silicon Valley, California 166, 167, 168, 171, 172 Simon, Hermann 168 Simons, Jim 23, 27, 110, 111–12, 124 Sinatra, Frank 72 Sinclair, Upton 54, 79, 104, 132–3 The Jungle 44 Sing Sing maximum-security gaol, New York 292 Skilling, Jeff 126, 127, 128, 149, 197, 259 Slim, Carlos 52 Sloan, Alfred 45, 49 Sloan Foundation 49 small and medium-size enterprises (SMEs), financing 165–72, 291 Smith, Adam 31, 51, 60 The Wealth of Nations v, 56, 106 Smith, Greg 283 Smith Barney 34 social security 52, 79, 255 Social Security Trust Fund (US) 254, 255 socialism 4, 225, 301 Société Générale 130 ‘soft commission’ 29 ‘soft’ commodities 17 Soros, George 23, 27, 98, 109, 111–12, 124, 132 South Sea Bubble (18th century) 35, 132, 292 sovereign wealth funds 161, 253 Soviet empire 36 Soviet Union 225 collapse of 23 lack of confidence in supplies 89–90 Spain: property bubble 42 Sparks, D.L. 114, 283, 284 specific risk 97–8 speculation 93 Spitzer, Eliot 232, 292 spread 28, 94 Spread Networks 2 Square 187 Stamp Duty 274 Standard & Poor’s rating agency 21, 99, 248, 249, 313n6 Standard Life 26, 27, 30 standard of living 77 Standard Oil 44, 196, 323n1 Standard Oil of New Jersey (later Exxon) 323n1 Stanford University 167 Stanhope 158 State Street 200, 207 sterling devaluation (1967) 18 stewardship 144, 163, 195–203, 203, 208, 209, 210, 211, 213 Stewart, Jimmy 12 Stigler, George 237 stock exchanges 17 see also individual stock exchanges stock markets change in organisation of 28 as a means of taking money out of companies 162 rise of 38 stock-picking 108 stockbrokers 16, 25, 30, 197, 198 Stoll, Clifford 227–8 stone fei (in Micronesia) 323n5 Stone, Richard 263 Stora Enso 196 strict liability 295–6 Strine, Chancellor Leo 117 structured investment vehicles (SIVs) 158, 223 sub-prime lending 34–5, 75 sub-prime mortgages 63, 75, 109, 149, 150, 169, 244 Summers, Larry 22, 55, 73, 119, 154, 299 criticism of Rajan’s views 57 ‘ketchup economics’ 5, 57, 69 support for financialisation 57 on transformation of investment banking 15 Sunday Times 143 ‘Rich List’ 156 supermarkets: financial services 27 supply chain 80, 81, 83, 89, 92 Surowiecki, James: The Wisdom of Crowds xi swap markets 21 SWIFT clearing system 184 Swiss Re 62 syndication 62 Syriza 306 T Taibbi, Matt 55 tailgating 102, 103, 104, 128, 129, 130, 136, 138, 140, 152, 155, 190–91, 200 Tainter, Joseph 277 Taleb, Nassim Nicholas 125, 183 Fooled by Randomness 133 Tarbell, Ida 44, 54 TARGET2 system 184, 244 TARP programme 138 tax havens 123 Taylor, Martin 185 Taylor Bean and Whitaker 293 Tea Party 306 technological innovation 13, 185, 187 Tel Aviv, Israel 171 telecommunications network 181, 182 Tesla Motors 168 Tetra 168 TfL 159 Thai exchange rate, collapse of (1997) 39 Thain, John 300 Thatcher, Margaret 18, 23, 54, 59, 148, 151, 157 Thiel, Peter 167 Third World debt problem 37, 131 thrifts 25, 149, 150, 151, 154, 174, 290, 292 ticket touts 94–5 Tobin, James 273 Tobin tax 273–4 Tolstoy, Count Leo 97 Tonnies, Ferdinand 17 ‘too big to fail’ 75, 140, 276, 277 Tourre, Fabrice ‘Fabulous Fab’ 63–4, 115, 118, 232, 293, 294 trader model 82, 83 trader, rise of the 16–24 elements of the new trading culture 21–2 factors contributing to the change 17–18 foreign exchange 18–19 from personal relationships to anonymous markets 17 hedge fund managers 23 independent traders 22–3 information technology 19–20 regulation 20 securitisation 20–21 shift from agency to trading 16 trading as a principal source of revenue and remuneration 17 trader model 82, 83 ‘trading book’ 320n20 transparency 29, 84, 205, 210, 212, 226, 260 Travelers Group 33, 34, 48 ‘treasure islands’ 122–3 Treasuries 75 Treasury (UK) 135, 158 troubled assets relief program 135 Truman, Harry S. 230, 325n13 trust 83–4, 85, 182, 213, 218, 260–61 Tuckett, David 43, 71, 79 tulip mania (1630s) 35 Turner, Adair 303 TWA 238 Twain, Mark: Pudd’nhead Wilson’s Calendar 95–6 Twitter 185 U UBS 33, 134 UK Independence Party 306 unemployment 73, 74, 79 unit trusts 202 United States global dominance of the finance industry 218 house prices 41, 43, 149, 174 stock bubble (1929) 201 universal banks 26–7, 33 University of Chicago 19, 69 ‘unknown unknowns’ 67 UPS delivery system 279–80 US Defense Department 167 US Steel 44 US Supreme Court 228, 229, 304 US Treasury 36, 38, 135 utility networks 181–2 V value discovery 226–7 value horizon 109 Van Agtmael, Antoine 39 Vanderbilt, Cornelius 44 Vanguard 200, 207, 213 venture capital 166 firms 27, 168 venture capitalists 171, 172 Vickers Commission 194 Viniar, David 204–5, 233, 282, 283, 284 VISA 186 volatility 85, 93, 98, 103, 131, 255 Volcker, Paul 150, 181 Volcker Rule 194 voluntary agencies 258 W wagers and credit default swaps 119 defined 61 at Lloyd’s coffee house 71–2 lottery tickets 65 Wall Street, New York 1, 16, 312n2 careers in 15 rivalry with London 13 staffing of 217 Wall Street Crash (1929) 20, 25, 27, 36, 127, 201 Wall Street Journal 294 Wallenberg family 108 Walmart 81, 83 Warburg 134 Warren, Elizabeth 237 Washington consensus 39 Washington Mutual 135, 149 Wasserstein, Bruce 204, 205 Watergate affair 240 ‘We are the 99 per cent’ slogan 52, 305 ‘We are Wall Street’ 16, 55, 267–8, 271, 300, 301 Weber, Max 17 Weill, Sandy 33–4, 35, 48–51, 55, 91, 149, 293, 314n40 Weinstock, Arnold 48 Welch, Jack 45–6, 48, 50, 52, 126, 314n40 WestLB 169 Westminster Bank 24 Whitney, Richard 292 Wilson, Harold 18 windfall payments 14, 32, 127, 153, 290 winner’s curse 103, 104, 156, 318n11 Winslow Jones, Alfred 23 Winton Capital 111 Wolfe, Humbert 7 The Uncelestial City 1 Wolfe, Tom 268 The Bonfire of the Vanities 16, 22 women traders 22 Woodford, Neil 108 Woodward, Bob: Maestro 240 World Bank 14, 220 World.Com bonds 197 Wozniak, Steve 162 Wriston, Walter 37 Y Yellen, Janet 230–31 Yom Kippur War (1973) 36 YouTube 185 Z Zurich, Switzerland 62


pages: 472 words: 117,093

Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Massive amounts of technology have been deployed to automate the work of actually buying the assets once the decisions have been made (and then keeping track of them over time), but these decisions were almost always made by minds, not machines. This started to change in the 1980s when pioneers like Jim Simons (one of the most accomplished mathematicians of his generation) and David Shaw (a computer scientist) founded, respectively, Renaissance Technologies and D. E. Shaw to use machines to make investment decisions. These companies sifted through large amounts of data, built and tested quantitative models of how assets’ prices behaved under different conditions, and worked to substitute code and math for individual judgment about what and when to buy. The best of these “quant” firms built up spectacular track records. D. E. Shaw had over $40 billion under management in October 2016, and its Composite Fund generated 12% annualized returns in the decade leading up to 2011.

., 252–75 overall evaluation criterion, 51 Overstock.com, 290 Owen, Ivan, 273, 274 Owen, Jennifer, 274n ownership, contracts and, 314–15 Page, Larry, 233 PageRank, 233 Pahlka, Jennifer, 163 Painting Fool, The, 117 Papa John’s Pizza, 286 Papert, Seymour, 73 “Paperwork Mine,” 32 Paris, France, terrorist attack (2015), 55 Parker, Geoffrey, 148 parole, 39–40 Parse.ly, 10 Paulos, John Allen, 233 payments platforms, 171–74 peer reviews, 208–10 peer-to-peer lending, 263 peer-to-peer platforms, 144–45, 298 Peloton, 177n Penthouse magazine, 132 People Express, 181n, 182 Perceptron, 72–74 Perceptrons: An Introduction to Computational Geometry (Minsky and Papert), 73 perishing/perishable inventory and O2O platforms, 186 and revenue management, 181–84 risks in managing, 180–81 personal drones, 98 perspectives, differing, 258–59 persuasion, 322 per-transaction fees, 172–73 Pew Research Center, 18 p53 protein, 116–17 photography, 131 physical environments, experimentation in development of, 62–63 Pindyck, Robert, 196n Pinker, Steven, 68n piracy, of recorded music, 144–45 Plaice, Sean, 184 plastics, transition from molds to 3D printing, 104–7 Platform Revolution (Parker, Van Alstyne, and Choudary), 148 platforms; See also specific platforms business advantages of, 205–11 characteristics of successful, 168–74 competition between, 166–68 and complements, 151–68 connecting online and offline experience, 177–98; See also O2O (online to offline) platforms consumer loyalty and, 210–11 defined, 14, 137 diffusion of, 205 economics of “free, perfect, instant” information goods, 135–37 effect on incumbents, 137–48, 200–204 elasticity of demand, 216–18 future of companies based on, 319–20 importance of being open, 163–65; See also open platforms and information asymmetries, 206–10 limits to disruption of incumbents, 221–24 multisided markets, 217–18 music industry disruption, 143–48 network effect, 140–42 for nondigital goods/services, 178–85; See also O2O (online to offline) platforms and perishing inventory, 180–81 preference for lower prices by, 211–21 pricing elasticities, 212–13 product as counterpart to, 15 and product maker prices, 220–21 proliferation of, 142–48 replacement of assets with, 6–10 for revenue management, 181–84 supply/demand curves and, 153–57 and unbundling, 145–48 user experience as strategic element, 169–74 Playboy magazine, 133 Pliny the Elder, 246 Polanyi, Michael, 3 Polanyi’s Paradox and AlphaGo, 4 defined, 3 and difficulty of comparing human judgment to mathematical models, 42 and failure of symbolic machine learning, 71–72 and machine language, 82 and problems with centrally planned economies, 236 and System 1/System 2 relationship, 45 Postmates, 173, 184–85, 205 Postmates Plus Unlimited, 185 Postrel, Virginia, 90 Pratt, Gil, 94–95, 97, 103–4 prediction data-driven, 59–60 experimentation and, 61–63 statistical vs. clinical, 41 “superforecasters” and, 60–61 prediction markets, 237–39 premium brands, 210–11 presidential elections, 48–51 Priceline, 61–62, 223–24 price/pricing data-driven, 47; See also revenue management demand curves and, 154 elasticities, 212–13 loss of traditional companies’ power over, 210–11 in market economies, 237 and prediction markets, 238–39 product makers and platform prices, 220 supply curves and, 154–56 in two-sided networks, 213–16 Principia Mathematica (Whitehead and Russell), 69 print media, ad revenue and, 130, 132, 139 production costs, markets vs. companies, 313–14 productivity, 16 products as counterpart to platforms, 15 loss of profits to platform providers, 202–4 pairing free apps with, 163 platforms’ effect on, 200–225 threats from platform prices, 220–21 profitability Apple, 204 excessive use of revenue management and, 184 programming, origins of, 66–67 Project Dreamcatcher, 114 Project Xanadu, 33 proof of work, 282, 284, 286–87 prose, AI-generated, 121 Proserpio, Davide, 223 Prosper, 263 protein p53, 116–17 public service, 162–63 Pullman, David, 131 Pullum, Geoffrey, 84 quantitative investing firms (quants), 266–70 Quantopian, 267–70 Quinn, Kevin, 40–41 race cars, automated design for, 114–16 racism, 40, 51–52, 209–10 radio stations as complements to recorded music, 148 in late 1990s, 130 revenue declines (2000–2010), 135 Ramos, Ismael, 12 Raspbian, 244 rationalization, 45 Raymond, Eric, 259 real-options pricing, 196 reasoning, See System 1/System 2 reasoning rebundling, 146–47 recommendations, e-commerce, 47 recorded music industry in late 1990s, 130–31 declining sales (1999-2015), 134, 143 disruption by platforms, 143–48 Recording Industry Association of America (RIAA), 144 redlining, 46–47 Redmond, Michael, 2 reengineering, business process, 32–35 Reengineering the Corporation (Hammer and Champy), 32, 34–35, 37 regulation financial services, 202 Uber, 201–2, 208 Reichman, Shachar, 39 reinforcement learning, 77, 80 Renaissance Technologies, 266, 267 Rent the Runway, 186–88 Replicator 2 (3D printer), 273 reputational systems, 209–10 research and development (R&D), crowd-assisted, 11 Research in Motion (RIM), 168 residual rights of control, 315–18 “Resolution of the Bitcoin Experiment, The” (Hearn), 306 resource utilization rate, 196–97 restaurants, robotics in, 87–89, 93–94 retail; See also e-commerce MUEs and, 62–63 Stripe and, 171–74 retail warehouses, robotics in, 102–3 Rethinking the MBA: Business Education at a Crossroads (Datar, Garvin, and Cullen), 37 revenue, defined, 212 revenue management defined, 47 downsides of, 184–85 O2O platforms and, 193 platforms for, 181–84 platform user experience and, 211 problems with, 183–84 Rent the Runway and, 187 revenue-maximizing price, 212–13 revenue opportunities, as benefit of open platforms, 164 revenue sharing, Spotify, 147 reviews, online, 208–10 Ricardo, David, 279 ride services, See BlaBlaCar; Lyft; Uber ride-sharing, 196–97, 201 Rio Tinto, 100 Robohand, 274 robotics, 87–108 conditions for rapid expansion of, 94–98 DANCE elements, 95–98 for dull, dirty, dangerous, dear work, 99–101 future developments, 104–7 humans and, 101–4 in restaurant industry, 87–89 3D printing, 105–7 Rocky Mountain News, 132 Romney, Mitt, 48, 49 Roosevelt, Teddy, 23 Rosenblatt, Frank, 72, 73 Rovio, 159n Roy, Deb, 122 Rubin, Andy, 166 Ruger, Ted, 40–41 rule-based artificial intelligence, 69–72, 81, 84 Russell, Bertrand, 69 Sagalyn, Raphael, 293n Saloner, Garth, 141n Samsung and Android, 166 and Linux, 241, 244 sales and earnings deterioration, 203–4 San Francisco, California Airbnb in, 9 Craigslist in, 138 Eatsa in, 87 Napster case, 144 Postmates in, 185 Uber in, 201 Sanger, Larry, 246–48 Sato, Kaz, 80 Satoshi Nakamoto Institute, 304 scaling, cloud and, 195–96 Schiller, Phil, 152 Schumpeter, Joseph, 129, 264, 279, 330 Scott, Brian, 101–2 second machine age origins of, 16 phase one, 16 phase two, 17–18 secular trends, 93 security lanes, automated, 89 Sedol, Lee, 5–6 self-checkout kiosks, 90 self-driving automobiles, 17, 81–82 self-justification, 45 self-organization, 244 self-selection, 91–92 self-service, at McDonald’s, 92 self-teaching machines, 17 Seychelles Trading Company, 291 Shanghai Tower, 118 Shapiro, Carl, 141n Shaw, David, 266 Shaw, J.


pages: 431 words: 132,416

No One Would Listen: A True Financial Thriller by Harry Markopolos

backtesting, barriers to entry, Bernie Madoff, buy and hold, call centre, centralized clearinghouse, correlation coefficient, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, family office, financial thriller, fixed income, forensic accounting, high net worth, index card, Long Term Capital Management, Louis Bachelier, offshore financial centre, Ponzi scheme, price mechanism, quantitative trading / quantitative finance, regulatory arbitrage, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, statistical arbitrage, too big to fail, transaction costs, your tax dollars at work

The SEC branch chief who handled this complaint pointed out that MARHedge was a respected industry publication, but “not one that she believed the Commission usually received.” And just like all of our submissions, this was lost in the bureaucracy. The fact that Madoff’s scam was widely known in the industry and easy to rip apart was proved in 2004 by an SEC compliance examiner. As Kotz reported, while conducting a routine examination of Renaissance Technologies LLC, this investigator discovered e-mails between executives of that fund that professionally analyzed Madoff’s strategy and returns and concluded there was no way to explain Madoff’s activities. As one of those executives told Kotz, “This is not rocket science.” The reason they had not notified the SEC was that all the information they relied on to reach these conclusions was readily available to the SEC.

See also trade tickets Options Option transactions Order flow and market intelligence payments for Organized crime Over-the-counter market Parkway Capital Payne, Gerald Pearlman, Lou Penna, Nick Personal danger Petters, Tom Pharmaceutical fraud Picower, Jeffrey Plaintiff’s firms Plan administrators Police department Ponzi, Charles Ponzi schemes examples of Harry Markopolos describes to SEC human damage from mechanics of new money requirements Weisman on Ponzi scheme vs. front-running Potemkin trading desk (front) Professional ethics Proof, legal vs. mathematical Prospect Capital Putnam Investments Quants (quantitative analysts) Qui tam cases Qui tam provisions Rampart Investment Management Company Rampart Option Management System Rampart Option Statistical Advantage Rating agencies Red flags Regulatory corruption Reid, Douglas Realtors Renaissance Technologies LLC Reporting Reverse engineering Rewards Ricciardi, Walter Rich, Mark Richards, Lori Risk assumption Roosevelt, Franklin Delano Rosenthal, Stu Royal Bank of Canada Royalty Russian default Russian mafia S&P 500 S&P 500 options Sailing Scannell, Peter Schadt, Rudi Schapiro, Mary Schulman, Diane Schumer, Chuck Schwager, Jack Secrecy Securities and Exchange Commission (SEC): 2005 submission disclosure audits BDO mishandles Harry Markopolos filing Bernie Madoff and bounty program changes at Chuck Schumer call to in congressional hearings criminal investigation damage by inaction of danger from disregards Harry Markopolos complaint Division of Enforcement actions double standard examination team excuses first report to Harry Markopolos meets Garrity Harry Markopolos on Harry Markopolos reports fraud to Harry Markopolos visits ignores Harry Markopolos complaint impact on incompetence of informal inquiry Inspector General Inspector General findings Inspector General investigation Inspector General review investigation of Bernie Madoff jurisdictional problems liability of MARHedge reporting market timing complaint New York regional office New York office incompetence Office of Economic Analysis origins of post BM arrest cover-up powers of regulatory priorities rejects Harry Markopolos market timing investigation resignations from sovereign immunity and negligence systemic incompetence visits Madoff warnings from others whistleblower program See also Cheung, Meaghan; Garrity,Mike; Kotz, David; Manion, Edward; Ward, Grant: Securities and Exchange Commission (SEC) teams: accounting audit enforcement examination inspection investigative Securities Exchange Company Security Segel, Jim Seghers, Conrad Self-regulation Senate Banking Committee Sennen (sailing vessel) Sherman, Brad Short volatility 60 Minutes Skilling, Jeff Slatkin, Reed Social networking Société Générale Sokobin, Jonathan Sorkin, Ira Lee Sovereign immunity and negligence Spitzer, Eliot Split-strike conversion strategy State Street Corporation Steiber, Heide Stein, Ben Stock picking Strategy analysis Structured products Subsidization theory Suh, Simona Suicides Sutton, Willie Tax havens Taxpayers Against Fraud.


pages: 545 words: 137,789

How Markets Fail: The Logic of Economic Calamities by John Cassidy

"Robert Solow", Albert Einstein, Andrei Shleifer, anti-communist, asset allocation, asset-backed security, availability heuristic, bank run, banking crisis, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black-Scholes formula, Blythe Masters, Bretton Woods, British Empire, business cycle, capital asset pricing model, centralized clearinghouse, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, corporate raider, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Daniel Kahneman / Amos Tversky, debt deflation, different worldview, diversification, Elliott wave, Eugene Fama: efficient market hypothesis, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, George Akerlof, global supply chain, Gunnar Myrdal, Haight Ashbury, hiring and firing, Hyman Minsky, income per capita, incomplete markets, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Nash: game theory, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kickstarter, laissez-faire capitalism, Landlord’s Game, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, Mikhail Gorbachev, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, Naomi Klein, negative equity, Network effects, Nick Leeson, Northern Rock, paradox of thrift, Pareto efficiency, Paul Samuelson, Ponzi scheme, price discrimination, price stability, principal–agent problem, profit maximization, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, RAND corporation, random walk, Renaissance Technologies, rent control, Richard Thaler, risk tolerance, risk-adjusted returns, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, technology bubble, The Chicago School, The Great Moderation, The Market for Lemons, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, unorthodox policies, value at risk, Vanguard fund, Vilfredo Pareto, wealth creators, zero-sum game

Nervous hedge funds were calling other Wall Street firms and asking them to take over their derivatives trades with Bear in return for a fee, but on Tuesday, Goldman Sachs sent an e-mail to hedge funds warning them it would no longer agree to do this. Rumors circulated that Credit Suisse had done the same thing. To the hedge fund community, it appeared that the rest of the Street was giving up on Bear. Many big funds, including Renaissance Technologies and D.E. Shaw, started pulling money out of Bear, as did some of Bear’s individual clients. The firm was also having difficulty raising funding in the repo market, an obscure but immensely important place, where financial firms borrow money on an overnight basis by selling some of their assets to other firms and agreeing to repurchase them the following day. (“Repo” is short for “repurchase.”)

President’s Economic Policy Advisory Board Priceline Prices and Production (Hayek) Prince, Charles “Chuck” Princeton University Institute for Advanced Study Principles of Economics (Marshall) Principles of Political Economy (Mill) prisoner’s dilemma “Problem of Social Cost, The” (Coase) productivity agricultural growth of, random fluctuations in wages and Proud Decades, The (Diggins) Prudential Securities Quantum Fund Quarterly Journal of Economics, The Quesnay, François Rabin, Matt Radner, Roy Rajan, Raghuram G. Ramsey, Frank Rand, Ayn RAND Institute Random Walk Down Wall Street, A (Malkiel) random walk theory Ranieri, Lewis rational expectations theory RBS Greenwich Capital Reader’s Digest Reagan, Ronald reality-based economics RealtyTrac Reinhart, Vincent Renaissance Technologies “Report on Social Insurance and Allied Services” (Beveridge) Republican Party Reserve Primary Fund residential mortgage-backed securities (RMBSs) Resolution Trust Corporation Review of Economic Studies, The Revolution (Anderson) Revolutionary era Ricardo, David Rigas, John RiskMetrics Roach, Stephen S. Road to Serfdom, The (Hayek) Robbins, Lionel Robinson, Joan Rochester, University of Rockefeller, John D.


pages: 419 words: 130,627

Last Man Standing: The Ascent of Jamie Dimon and JPMorgan Chase by Duff McDonald

bank run, Blythe Masters, Bonfire of the Vanities, centralized clearinghouse, collateralized debt obligation, conceptual framework, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Exxon Valdez, financial innovation, fixed income, G4S, housing crisis, interest rate swap, Jeff Bezos, John Meriwether, Kickstarter, laissez-faire capitalism, Long Term Capital Management, margin call, market bubble, money market fund, moral hazard, negative equity, Nelson Mandela, Northern Rock, profit motive, Renaissance Technologies, risk/return, Rod Stewart played at Stephen Schwarzman birthday party, Saturday Night Live, sovereign wealth fund, statistical model, Steve Ballmer, Steve Jobs, technology bubble, The Chicago School, too big to fail, Vanguard fund, zero-coupon bond, zero-sum game

“I’ve been around long enough to sense a very serious problem,” Geithner told him. “If he’s worried, Alan needs to call me.” Schwartz did call Geithner the next day to brief him, but maintained that he hoped to find a solution without the Fed’s help. He was kidding himself. By this point things were moving so quickly that there was little for him and his colleagues to do but watch the money stream out the door. That morning, the hedge fund Renaissance Technologies took $5 billion out of Bear’s prime brokerage arm. In the afternoon, another hedge fund, D.E. Shaw & Co., did the same thing. At the day’s end, Bear had just $5.9 billion of cash on hand, and the company’s stock price was plummeting. Gary Parr was working the phones, trying to find what he called a “validating” investor—an outsider who could lend credence to the idea that Bear was still viable.

., 216 Prudential Insurance, 19 Prudential Securities, 177, 182 public-private investment program (PPIP), 314 Purcell, Phil, 84–85, 112, 115, 182 Puth, David, 218 Quattrone, Frank, 164 Rabobank, 243, 244 Rand, Ayn, 86 Ranieri, Lewis, 212 Rattner, Steven, 223 Real Deal, The (Weill), 34, 45, 56, 74, 77, 115, 120, 128–29, 164, 220 real estate, 57–58, 109, 154, 155, 205–15, 219, 223–28, 232–38, 246, 251–52, 266, 275, 277, 290, 297–98, 302–3, 306, 309, 311, 322–23 recessions, economic, 20, 45, 52, 86, 152, 306, 318 Redmond, Andrea, 146, 186 Redstone, Sumner, 243 Reed, John, 98–142, 160–61 Reinhard, J. Pedro, 235 Renaissance Technologies, 245 Republic National Bank, 19 Reserve Primary Fund, 283–84 “Rethinking Glass-Steagall,” 87 Risk, 217 risk arbitrage, 27, 92, 93, 96, 97, 107–14, 187–91, 209–14 RJR Nabisco, 52 Roach, Stephen, 223 Robert Fleming Holdings, 172 Robinson, James, 19, 20, 21, 24, 25, 28, 59, 60, 101 Robinson Humphrey, 20 Rockefeller, David, 203, 222 Rockefeller, John D., Jr., 3 Rockefeller, John D., Sr., 3, 202 Rogers, Brian, 320 Rohatyn, Felix, 315 Roosevelt, Theodore, 288 Rose, Charlie, 195, 231, 271, 308 Rotella, Steve, 291, 297 Roubini, Nouriel, 223 Rubin, Robert, 87–88, 103, 108, 140, 161, 167, 244, 315 Russell Reynolds, 145, 146 SAC Capital Partners, 268 Safra, Edward, 19 Salomon Brothers, 91–97, 146 Salomon Smith Barney, 51, 56, 63, 72, 75–85, 91–97, 98, 101–11, 114, 118, 121–25, 127, 149, 192, 194, 219 Saudi Aramco, 187 Savannah Electric and Power Co., 87 Saxon Capital, 209 Scharf, Charlie, 41–42, 58, 59, 77, 84, 120, 139, 149, 150, 166, 186, 233, 234, 239, 242, 246, 250–51, 255, 290, 291, 292–95, 297 Schorr, Glenn, 239 Schumer, Charles, 133, 316 Schutz, Anton, 222 Schwartz, Alan, 166, 225, 243–47, 249–50, 253, 257, 258, 261, 264, 270 Schwarzman, Stephen, 221, 234, 244, 310 Securities and Exchange Commission (SEC), 17, 53, 135, 219, 248, 268, 310–11 Senate Banking Committee, 266 September 11, 2001 terrorist attacks, 89, 153, 162, 252 Shakespeare, William, 136 Shapiro, Marc, 145, 147 Shearson American Express, 16, 18, 19–20, 21, 41, 44, 46, 49, 59–61, 86 Shearson Hammill, 1, 2, 16 Shearson Hayden Stone, 18 Shearson Lehman Brothers, 56, 59–61, 63, 66–75, 78, 172 Shearson Loeb Rhoades, 18–19, 20 Shinsei Bank, 142 Shipley, Walter V., 36, 171, 203 Simons, James, 322 Simpson Thacher & Bartlett, 290, 293 Smith, Clair, 6 Smith, Gordon, 186, 239, 278 Smith Barney Harris Upham & Co., 48, 49, 51 Smith Barney Shearson, 59–61, 62, 66–75, 78–83, 107–12, 137–38 Solomon, Peter, 103 Sorkin, Andrew Ross, 261 Soros, George, 138, 309 SouthTrust, 178, 197 Spector, Warren, 166, 224–25, 226, 241 Spitzer, Eliot, 164–65, 251–52 Staley, Jes, 90, 186, 194–95, 239, 254, 255, 274, 278, 315, 326 Standard & Poor’s 500, 169, 182, 231, 265 Standard Chartered, 209 Stavis, Rob, 108 Steel, Bob, 300 Steinberg, Saul, 17 Stern, David, 181 stock market, ix–xi, 16, 27–35, 45–46, 49, 51–55, 62, 66, 84, 86, 87, 92, 114, 125, 154–56, 166, 169, 172–75, 182–83, 196–97, 204, 206, 230–32, 238, 240–41, 254–55, 267, 275, 276, 283 structured investment vehicles (SIVs), 209–10, 229–30, 235, 236, 237, 309 subprime lending, 30–47, 209–15, 223–37, 246, 275, 290, 302 Sullivan, Barry, 147 Sweeney, Theresa, 61–62, 72, 83, 84, 121–22, 126, 133–34, 139, 141 Swenson, David, 167 Tannin, Matthew, 223 Taylor, George “Beau,” 218 Tearing Down the Walls (Langley), 18, 123, 161, 194 Tett, Gillian, 210, 212, 236, 283 Texas Commerce Bank, 145, 171, 180, 203 Texas Pacific Group (TPG), 291, 297 Thain, John, 244, 276, 282, 300 TheStreet.com, 126–27 Thompson, John, 202 Thomson, Todd, 111, 201 Time, 179, 196, 207 Tonucci, Paolo, 281–82 Trammel Crow, 158 Travelers Group, 57–59, 63, 70–77, 80–81, 84, 89–99, 102–3, 107, 112, 125, 165, 200–201, 220 Treasury bonds, 21, 91, 109, 308 Treasury Department, U.S., ix–xi, 203, 244, 247, 308, 327–28 Tribbett, Charles, III, 146 Trillion Dollar Meltdown, The (Morris), 227 Troubled Assets Relief Program (TARP), 294, 300–301, 308, 312, 313–16, 318 “Trusted Lieutenant, The” (Dimon), 140 Tsai, Gerry, 47–49, 51, 58, 115 Tufts University, 6, 8, 22 Tully, Daniel, 84–85, 135 Tully, Shawn, 207 Turner, Ted, 52 Twain, Mark, 237 UBS, 214, 289 United Airlines, 52, 168 Upton, Robert, 247 Urwin, Jeff, 270 USA Today, 197, 199 US Banker, 151 Vallas, Paul, 153 Vanity Fair, 226, 268 Viacom, 69–70 Visa, 305 Volcker, Paul, 20, 86, 316 Volland, Bob, 31–32, 34, 36 Vonder Linden, George, 50, 51, 77 Wachovia, 146, 162, 178, 197, 238, 298, 299, 300, 306 Wall Street, 16 Wall Street Journal, 36–37, 39, 73, 81, 82, 159, 163, 204, 207, 224, 228, 230, 258, 310–11, 318 Wal-Mart, 30, 303–4 Warner, Douglas “Sandy,” 90, 172 Washington Mutual (WaMu), x, 235, 246, 289–98, 300, 306–7 Wasserstein, Bruce, 221 Way, Alva, 21 Weill, Joan Mosher, 7, 17, 28–29, 65, 75, 77, 96, 97, 119, 127–28, 134, 161, 184 Weill, Marc, 7, 78, 96–97 Weill, Sanford I.: ambition of, 18–19, 42–43, 66–67, 75–77 autobiography of, 34, 45, 56, 74, 77, 115, 120, 128–29, 164, 220 in Baltimore, 34, 35–40, 59 as deal maker, 21–34, 43, 44–55, 57, 59–61, 89–94, 98–101, 130–32, 170, 172, 175, 178–79, 294 Weill, Sanford I.


pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

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

Another effect is that some hedge funds stop reporting when they experience poor performance, leading to a “survivorship bias.” A bias pulling in the opposite direction arises from the fact that the most successful hedge funds often do not report to the databases. These funds value their privacy and do not need any additional exposure to clients; they may in fact be closed to new investments due to limited capacity. Hence, the databases exclude some of the most impressive track records, such as that of Renaissance Technologies. When all these biases are taken into account, the evidence suggests that trading skill does exist among the best hedge funds and the best mutual funds, especially when considering performance before fees. Furthermore, some researchers find evidence of performance persistence, meaning that the top managers continue to be the top managers more often than not, but the persistence is not strong, and asset allocators should be careful of chasing performance, pulling money out at the bottom and investing at the peak rather than focusing on the manager’s long-term record, process, and team.3 The evidence also suggests that the biases in many estimates of hedge fund returns are very large—beware!

See also portfolio rebalance rule rebate rate, 79, 117 recall risk, 117–18 recovery rate in case of default, 260, 260n redemption notice periods, 75 reflexivity, Soros on, 200–204, 202f, 206 regressions: estimating, 32–33; predictive, 50–53 Regulation FD (Fair Disclosure), 129 relative valuation, 93 relative-value trades, 8; across asset classes, 261; on cross-country interest rate differences, 250; Griffin on, 287; mortgage-related, 261; on volatility, 262. See also arbitrage Renaissance Technologies, 23 replicating portfolio, 234–35, 237, 239–40 repo (repurchase agreement), 80 repo lenders, 76 repo rate, 80, 245–46, 245f, 248; general collateral (GC), 245, 245f; interest-rate swaps and, 259–60 required rate of return (discount rate), 89–90, 100, 102 residual income (RI), 92–93 residual income model, 92–93, 92n, 97 residual reversal strategies, 153 return, 27–29; Chanos on shorting opportunities and, 128; of highly shorted stocks, 121; of major asset classes, 176–83.


pages: 170 words: 49,193

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It) by Jamie Bartlett

Ada Lovelace, Airbnb, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, Dominic Cummings, Donald Trump, Edward Snowden, Elon Musk, Filter Bubble, future of work, gig economy, global village, Google bus, hive mind, Howard Rheingold, information retrieval, Internet of things, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta analysis, meta-analysis, mittelstand, move fast and break things, move fast and break things, Network effects, Nicholas Carr, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Jobs, Steven Levy, strong AI, TaskRabbit, technological singularity, technoutopianism, Ted Kaczynski, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator

In 2008, for example, analysts working for Barack Obama assigned a pair of scores to every voter in the country that predicted how likely they were to cast a ballot, and whether they supported his campaign.7 Hillary Clinton, too, had an extremely sophisticated system of targeting voters online.8 Every election now is a mini arms race. And this time the Republican Party turned to a company, Cambridge Analytica, in order to get the edge on the opposition. It was not a coincidental choice. One of Cambridge Analytica’s key investors is the billionaire businessman and Trump backer Robert Mercer, a famously reclusive computer programmer who made his fortune as co-chief executive of the New York-based hedge fund, Renaissance Technologies. RenTech, as it is known, uses big data and sophisticated algorithms to predict trends in global markets and place winning bets on them. In this world tiny gains, a fraction of a per cent here or there, can yield huge rewards. In 2013 Cambridge Analytica was set up as an offshoot of a company called ‘Strategic Communications Laboratories’ (SCL), which had extensive experience in branding and influencing public opinion, specialising in military and intelligence psychological operations, or ‘psy-ops’ – tasks like persuading young men not to join Al-Qaeda.


pages: 209 words: 53,175

The Psychology of Money: Timeless Lessons on Wealth, Greed, and Happiness by Morgan Housel

"side hustle", airport security, Amazon Web Services, Bernie Madoff, business cycle, computer age, coronavirus, discounted cash flows, diversification, diversified portfolio, Donald Trump, financial independence, Hans Rosling, Hyman Minsky, income inequality, index fund, invisible hand, Isaac Newton, Jeff Bezos, Joseph Schumpeter, knowledge worker, labor-force participation, Long Term Capital Management, margin call, Mark Zuckerberg, new economy, Paul Graham, payday loans, Ponzi scheme, quantitative easing, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, Stephen Hawking, Steven Levy, stocks for the long run, the scientific method, traffic fines, Vanguard fund, working-age population

Effectively all of Warren Buffett’s financial success can be tied to the financial base he built in his pubescent years and the longevity he maintained in his geriatric years. His skill is investing, but his secret is time. That’s how compounding works. Think of this another way. Buffett is the richest investor of all time. But he’s not actually the greatest—at least not when measured by average annual returns. Jim Simons, head of the hedge fund Renaissance Technologies, has compounded money at 66% annually since 1988. No one comes close to this record. As we just saw, Buffett has compounded at roughly 22% annually, a third as much. Simons’ net worth, as I write, is $21 billion. He is—and I know how ridiculous this sounds given the numbers we’re dealing with—75% less rich than Buffett. Why the difference, if Simons is such a better investor? Because Simons did not find his investment stride until he was 50 years old.


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method, Thomas Davenport

Suffice it to say here that any organization or individual involved with quantitative models should regularly review them to ensure that they still make sense and still fit the data—and if not, change them. By regularly, we mean at least every year or so, unless there is reason to examine them more quickly. In some settings, models need to be changed much more frequently. For example, if you’re basing financial trades on the model, you probably need to examine them very often. James Simons, the proprietor of Renaissance Technologies, runs one of the world’s largest hedge funds and changes his models all the time. He hires professors, code breakers, and statistically minded scientists and engineers. Since its inception in March 1988, Simons’s flagship $3.3 billion Medallion Fund, which traded everything from soybean futures to French government bonds, has amassed annual returns of 35.6 percent. For the eleven full years ending December 1999, Medallion’s cumulative returns were an eye-popping 2,478.6 percent.


pages: 280 words: 73,420

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

algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, buy and hold, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K

Clearly, a lot of people thought high-frequency trading (HFT) was a path to quick and easy profits. The general investment public had no idea that this market version of the Invasion of the Body Snatchers was under way. Some of the biggest players in the high-frequency trading sector were not household names: They were proprietary trading firms such as Getco and Tradebot and hedge funds such as Millennium, DE Shaw, WorldQuant, and Renaissance Technologies. Others were household names, but investors hadn’t paid much attention to their forays into mechanized trading because it was a relatively small portion of their earnings and they did not break out the numbers in their annual reports. Goldman Sachs, which had become notorious in the public’s eyes, owing to its role in the collapse of the mortgage market, had a sizable high-frequency trading desk.


pages: 192 words: 75,440

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel

backtesting, barriers to entry, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, family office, fixed income, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, merger arbitrage, offshore financial centre, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stocks for the long run, systematic trading, unpaid internship, value at risk, yield curve, yield management

Despite some notable collapses—Amaranth Advisors in 2006 being one of the most noteworthy—most foresee continued growth for the industry. Table 1.1 Top Ten Single–Manager Hedge Fund Firms (as of July 2007) FIRM LOCATION AUM (BLNS) JPMorgan Asset Management New York $56.20 Goldman Sachs Asset Management New York $39.98 D. E. Shaw Group New York $34.00 Bridgewater Associates Westport, CT $32.10 Och-Ziff Capital Management New York $29.20 Renaissance Technologies Corp. East Setauket, NY $29.20 Farallon Capital Management San Francisco $26.06 Barclays Global Investors San Francisco $23.00 Man Investments Limited London $21.13 Tudor Investment Corporation Greenwich, CT $20.96 a Source: Absolute Return magazine, used with permission by HedgeFund Intelligence. Copyright 2007. a Including JPMorgan Asset Management ($19.50 bln) and Highbridge Capital ($36.70 bln).


pages: 302 words: 86,614

The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds by Maneet Ahuja, Myron Scholes, Mohamed El-Erian

activist fund / activist shareholder / activist investor, Asian financial crisis, asset allocation, asset-backed security, backtesting, Bernie Madoff, Bretton Woods, business process, call centre, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, en.wikipedia.org, family office, fixed income, high net worth, interest rate derivative, Isaac Newton, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, merger arbitrage, Myron Scholes, NetJets, oil shock, pattern recognition, Ponzi scheme, quantitative easing, quantitative trading / quantitative finance, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, systematic trading, zero-sum game

Photo credit: Bridgewater Associates, LP Tim Wong (right) on the trading floor at the Man Group’s London headquarters. Photo credit: Michael Austen, Report and Accounts for 2011 Pierre LaGrange in the boardroom at the Man Group’s London Headquarters. Photo credit: Michael Austen, Report and Accounts for 2011 During the Committee on Oversight and Government Reform Hearing on “Hedge Funds and the Financial Markets,” George Soros, Soros Fund Management, LLC (left), James Simons, President Renaissance Technologies (center), and John Paulson, President, Paulson & Co (right), testify on Capitol Hill, November 13, 2008. Photo credit: (c) Daniel Rosenbaum/The New York Times/Redux Hedge fund managers, experts and lobbyists appear before the House Financial Services Committee in Washington on Tuesday, March 13, 2007. From left: E. Gerald Corrigan, Goldman Sachs & Company; Kenneth D. Brody, Taconic Capital Advisors LLC; James S.


pages: 620 words: 214,639

House of Cards: A Tale of Hubris and Wretched Excess on Wall Street by William D. Cohan

asset-backed security, call centre, collateralized debt obligation, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Deng Xiaoping, diversification, Financial Instability Hypothesis, fixed income, Hyman Minsky, Irwin Jacobs, John Meriwether, Long Term Capital Management, margin call, merger arbitrage, money market fund, moral hazard, mortgage debt, mutually assured destruction, Myron Scholes, New Journalism, Northern Rock, Renaissance Technologies, Rod Stewart played at Stephen Schwarzman birthday party, savings glut, shareholder value, sovereign wealth fund, too big to fail, traveling salesman, Y2K, yield curve

We were putting out fires every day, all day every day, for firms that wouldn't take assignments, or wouldn't take our name in the foreign exchange markets, or wherever, either localized within departments or at the corporate level. I'd have done the same thing.” At least two major hedge funds began pulling their cash from the firm. “The prime brokerage withdrawals began in earnest that Tuesday night,” Boyd explained. “I think the two big funds that kicked it all off were Renaissance Technologies—Jim Simons's $30 billion fund. I had two or three people tell me that he pulled, and I think he had $20 billion [at Bear]—and I think Highbridge did, too.” (Since September 2004, JPMorgan Chase has controlled Highbridge Capital Management, a hedge fund with around $35 billion in capital.) To be sure, the cash was theirs and they were entitled to it at any moment, and this cash was not to be confused with Bear's own corporate cash, which was in a separate, segregated account.

He was clearly getting messages that there were accounts that were beginning to withdraw their money, and then the meeting disbanded.” Another banker at the meeting remembered seeing Minikes afterward, too. “Minikes was going crazy because he's looking at his Black-Berry, seeing the clearing accounts, just like pull, pull, pull, pull, pull. He said, ‘Alan, we've got to do something.’” Word spread quickly that hedge fund giant D. E. Shaw & Co. had pulled $5 billion of its cash and that Renaissance Technologies, the giant hedge fund run by James Simons, was following suit. In the previous two months, S3 Partners, headed by Robert Sloan, had moved $25 billion out of Bear's prime brokerage. The $18 billion in cash was leaving the building, and fast. “You had Mike Minikes interrupt Schwartz and say, ‘Do you have any idea what's going on? We're watching our entire franchise walk out the door. All our customers are pulling their money,’” recalled Friedman.


pages: 312 words: 91,538

The Fear Index by Robert Harris

algorithmic trading, backtesting, banking crisis, dark matter, family office, Fellow of the Royal Society, fixed income, Flash crash, God and Mammon, high net worth, implied volatility, mutually assured destruction, Neil Kinnock, Renaissance Technologies, speech recognition

He had never had any intention of moving his family to Switzerland – not that he told them that, or even acknowledged it to himself. But the truth was, domesticity was a stock that no longer suited his portfolio. He was bored with it. It was time to sell up and move on. He decided they should call themselves Hoffmann Investment Technologies in a nod to Jim Simons’s legendary quant shop, Renaissance Technologies, over in Long Island: the daddy of all algorithmic hedge funds. Hoffmann had objected strongly – the first time Quarry had encountered his mania for anonymity – but Quarry was insistent: he saw from the start that Hoffmann’s mystique as a mathematics genius, like that of Jim Simons, would be an important part of selling the product. AmCor agreed to act as prime brokers and to let Quarry take some of his old clients with him in return for a reduced management fee and ten per cent of the action.


pages: 342 words: 99,390

The greatest trade ever: the behind-the-scenes story of how John Paulson defied Wall Street and made financial history by Gregory Zuckerman

1960s counterculture, banking crisis, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, financial innovation, fixed income, index fund, Isaac Newton, Long Term Capital Management, margin call, Mark Zuckerberg, Menlo Park, merger arbitrage, mortgage debt, mortgage tax deduction, Ponzi scheme, Renaissance Technologies, rent control, Robert Shiller, Robert Shiller, rolodex, short selling, Silicon Valley, statistical arbitrage, Steve Ballmer, Steve Wozniak, technology bubble, zero-sum game

So many doubts had been raised about Bear Stearns’' health, though, that the accounts never would return to the investment bank. As the meeting broke up, one hedge-fund executive said to a friend, “"Shit, Bear’'s really in trouble.”" Chatter about the meeting began to circulate as soon as the executives returned to their firms. It was a dagger in the staggering investment bank’'s heart. Soon a rash of hedge funds pulled money out of Bear Stearns, including a $5 billion shift by hedge fund Renaissance Technologies. Tempers flared within Bear Stearns as the investment bank’'s shares plunged and its cash dwindled. The firm’'s CEO, Alan Schwartz, tried to calm various executives. During one meeting, though, Michael Minikes, a sixty-five-year-old veteran, abruptly cut off his boss. “"Do you have any idea what is going on?”" Minikes asked. “"Our cash is flying out the door. Our clients are leaving us.”"


pages: 261 words: 103,244

Economists and the Powerful by Norbert Haring, Norbert H. Ring, Niall Douglas

"Robert Solow", accounting loophole / creative accounting, Affordable Care Act / Obamacare, Albert Einstein, asset allocation, bank run, barriers to entry, Basel III, Bernie Madoff, British Empire, buy and hold, central bank independence, collective bargaining, commodity trading advisor, corporate governance, creative destruction, credit crunch, Credit Default Swap, David Ricardo: comparative advantage, diversified portfolio, financial deregulation, George Akerlof, illegal immigration, income inequality, inflation targeting, information asymmetry, Jean Tirole, job satisfaction, Joseph Schumpeter, Kenneth Arrow, knowledge worker, law of one price, light touch regulation, Long Term Capital Management, low skilled workers, mandatory minimum, market bubble, market clearing, market fundamentalism, means of production, minimum wage unemployment, moral hazard, new economy, obamacare, old-boy network, open economy, Pareto efficiency, Paul Samuelson, pension reform, Ponzi scheme, price stability, principal–agent problem, profit maximization, purchasing power parity, Renaissance Technologies, rolodex, Sergey Aleynikov, shareholder value, short selling, Steve Jobs, The Chicago School, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, ultimatum game, union organizing, Vilfredo Pareto, working-age population, World Values Survey

A year earlier, they had made nearly double that amount. This is made possible because, unlike mutual funds but like investment funds, hedge funds typically charge a very substantial “performance fee” of between 10 and 50 percent of profits for any profits exceeding a “hurdle” rate (e.g. the amount one might earn if one left the investment in a bank account). Top of the list in 2008 was James Simons of Renaissance Technologies at US$2.5 billion, which he made with his 5 percent management fee and a 44 percent share of the profits. Three more hedge fund managers made more than a billion dollars in 2008. The wealthy and moneyed institutions that invest in hedge funds do not fare quite as well. Economists have looked into what returns investors in various kinds of hedge funds get after fees are paid. The essence is that it is not such a privilege to be able to invest in a hedge fund.


pages: 311 words: 99,699

Fool's Gold: How the Bold Dream of a Small Tribe at J.P. Morgan Was Corrupted by Wall Street Greed and Unleashed a Catastrophe by Gillian Tett

accounting loophole / creative accounting, asset-backed security, bank run, banking crisis, Black-Scholes formula, Blythe Masters, break the buck, Bretton Woods, business climate, business cycle, buy and hold, collateralized debt obligation, commoditize, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, easy for humans, difficult for computers, financial innovation, fixed income, housing crisis, interest rate derivative, interest rate swap, Kickstarter, locking in a profit, Long Term Capital Management, McMansion, money market fund, mortgage debt, North Sea oil, Northern Rock, Renaissance Technologies, risk tolerance, Robert Shiller, Robert Shiller, Satyajit Das, short selling, sovereign wealth fund, statistical model, The Great Moderation, too big to fail, value at risk, yield curve

The cost of buying insurance against a default by Bear, with credit default swaps, began to spiral upwards. A year earlier, the annual price of insuring $10 million of Bear bonds had been well under $100,000. By March 10, it was well over $600,000. Officials at Goldman Sachs and Credit Suisse circulated internal emails warning about the counterparty risk posed by Bear, and when news of those leaked out, investors became even more nervous. Then a large hedge fund called Renaissance Technologies pulled its accounts out of Bear, and a snowball of rumors of Bear’s demise was set in motion. Frantically, the senior Bear managers hunted for ways to stop the leakage. On March 5, Bear’s cash holdings, on paper, topped $20 billion, and even on March 10 they were $18 billion, but by March 11, Bear’s funds had dropped to $10 billion. If Bear had been a commercial bank, it could have gone to the Federal Reserve for a loan, as the large commercial banks enjoy “lender of last resort facilities” and can always ask the Fed for funds in a crisis, as long as they have collateral.


pages: 304 words: 99,836

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

always be closing, asset allocation, Black Swan, bonus culture, break the buck, collateralized debt obligation, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, delayed gratification, East Village, fixed income, Flash crash, glass ceiling, Goldman Sachs: Vampire Squid, high net worth, information asymmetry, London Interbank Offered Rate, mega-rich, money market fund, new economy, Nick Leeson, quantitative hedge fund, Renaissance Technologies, short selling, Silicon Valley, Skype, sovereign wealth fund, Stanford marshmallow experiment, statistical model, technology bubble, too big to fail

Like Cliff Asness, Helga had an economics PhD from Chicago. She spoke with her fellow geniuses at other banks and hedge funds and deduced that the quant funds seemed to be falling victim to their own success: there were just too many of them using the exact same model. It wasn’t just AQR and Global Alpha that used the model. There were other big funds run by PhDs working with variations on Cliff’s special sauce: there was James Simons’s Renaissance Technologies, and there was D. E. Shaw, among many smaller imitators. As a result of all these companies working off similar models, investment opportunities in heavily capitalized mainstream companies were becoming crowded, so the computers were increasingly seeking out more illiquid and less widely held investments. The more out of the way the security, the fewer buyers and sellers for it, so it can be hard to unwind one of these investments.


The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, delayed gratification, discovery of DNA, Donald Trump, Douglas Engelbart, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, John Conway, John Markoff, John von Neumann, Mark Zuckerberg, Minecraft, natural language processing, Netflix Prize, Norbert Wiener, orbital mechanics / astrodynamics, PageRank, pattern recognition, prediction markets, randomized controlled trial, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra

Back in the 1980s, when I was consulting for Morgan Stanley on neural network models of stock trading, I met David Shaw, a computer scientist who specialized in designing parallel computers. On leave of absence from Columbia University, Shaw was working as a quantitative analyst, or “quant,” in the early days of automated trading. He would go on to start his own investment management firm on Wall Street, the D. E. Shaw Group, and he is now a multibillionaire. The D. E. Shaw Group has been highly successful, but not as successful as another hedge fund, Renaissance Technologies, which was founded by James Simons, a distinguished mathematician and former chair of the Mathematics Department at Stony Brook University. Simons made $1.6 billion in 2016 alone, and this wasn’t even his best year.20 Called “the best physics and mathematics department in the world,”21 Renaissance “avoids hiring anyone with even the slightest whiff of Wall Street bona fides.”22 14 Chapter 1 High Latency versus position timeline Latency Traditional long-term investment Algorithmic trading Low HFT Short Long How long position held Figure 1.6 Machine learning is driving algorithmic trading, which is faster than traditional long-term investment strategies and more deliberate than high-frequency trading (HFT) in stock markets.


pages: 416 words: 106,532

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond by Chris Burniske, Jack Tatar

Airbnb, altcoin, asset allocation, asset-backed security, autonomous vehicles, bitcoin, blockchain, Blythe Masters, business cycle, business process, buy and hold, capital controls, Carmen Reinhart, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, disintermediation, distributed ledger, diversification, diversified portfolio, Donald Trump, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, financial innovation, fixed income, George Gilder, Google Hangouts, high net worth, Jeff Bezos, Kenneth Rogoff, Kickstarter, Leonard Kleinrock, litecoin, Marc Andreessen, Mark Zuckerberg, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, packet switching, passive investing, peer-to-peer, peer-to-peer lending, Peter Thiel, pets.com, Ponzi scheme, prediction markets, quantitative easing, RAND corporation, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ross Ulbricht, Satoshi Nakamoto, Sharpe ratio, Silicon Valley, Simon Singh, Skype, smart contracts, social web, South Sea Bubble, Steve Jobs, transaction costs, tulip mania, Turing complete, Uber for X, Vanguard fund, WikiLeaks, Y2K

John Paulson became the face of hedge fund billionaires who benefited from the crisis when it was revealed that he had personally earned over $1 billion from his fund management, including the Paulson Advantage Plus Fund (an event-driven fund). This fund alone ranked number one over the period of 2006 to 2008 with an annualized return of nearly 63 percent. Equally successful was James Simons’s Renaissance Technologies Medallion Fund with a return of 80 percent in 2008. Becoming a hedge fund manager became all the rage for business-minded students when it was revealed that the top 25 hedge fund managers had earned a total of $22.3 billion in 2007 and $11.6 billion in 2008.8 With numbers like these, the world of hedge funds caught the attention of the media. Investors questioned if these managers had something to do with the crash.9 They also wanted to know what they were doing differently and whether it was something they could do as well.


pages: 380 words: 118,675

The Everything Store: Jeff Bezos and the Age of Amazon by Brad Stone

airport security, Amazon Mechanical Turk, Amazon Web Services, bank run, Bernie Madoff, big-box store, Black Swan, book scanning, Brewster Kahle, buy and hold, call centre, centre right, Chuck Templeton: OpenTable:, Clayton Christensen, cloud computing, collapse of Lehman Brothers, crowdsourcing, cuban missile crisis, Danny Hillis, Douglas Hofstadter, Elon Musk, facts on the ground, game design, housing crisis, invention of movable type, inventory management, James Dyson, Jeff Bezos, John Markoff, Kevin Kelly, Kodak vs Instagram, late fees, loose coupling, low skilled workers, Maui Hawaii, Menlo Park, Network effects, new economy, optical character recognition, pets.com, Ponzi scheme, quantitative hedge fund, recommendation engine, Renaissance Technologies, RFID, Rodney Brooks, search inside the book, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, Skype, statistical arbitrage, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Thomas L Friedman, Tony Hsieh, Whole Earth Catalog, why are manhole covers round?, zero-sum game

PART I Faith CHAPTER 1 The House of Quants Before it was the self-proclaimed largest bookstore on Earth or the Web’s dominant superstore, Amazon.com was an idea floating through the New York City offices of one of the most unusual firms on Wall Street: D. E. Shaw & Co. A quantitative hedge fund, DESCO, as its employees affectionately called it, was started in 1988 by David E. Shaw, a former Columbia University computer science professor. Along with the founders of other groundbreaking quant houses of that era, like Renaissance Technologies and Tudor Investment Corporation, Shaw pioneered the use of computers and sophisticated mathematical formulas to exploit anomalous patterns in global financial markets. When the price of a stock in Europe was fractionally higher than the price of the same stock in the United States, for example, the computer jockeys turned Wall Street warriors at DESCO would write software to quickly execute trades and exploit the disparity.


pages: 289 words: 113,211

A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber

"Robert Solow", affirmative action, Albert Einstein, asset allocation, backtesting, beat the dealer, Black Swan, Black-Scholes formula, Bonfire of the Vanities, butterfly effect, commoditize, commodity trading advisor, computer age, computerized trading, disintermediation, diversification, double entry bookkeeping, Edward Lorenz: Chaos theory, Edward Thorp, family office, financial innovation, fixed income, frictionless, frictionless market, George Akerlof, implied volatility, index arbitrage, intangible asset, Jeff Bezos, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, loose coupling, margin call, market bubble, market design, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shock, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk/return, Robert Shiller, Robert Shiller, rolodex, Saturday Night Live, selection bias, shareholder value, short selling, Silicon Valley, statistical arbitrage, The Market for Lemons, time value of money, too big to fail, transaction costs, tulip mania, uranium enrichment, William Langewiesche, yield curve, zero-coupon bond, zero-sum game

Others in their ranks included Paul Tudor Jones, the founder of Tudor Investments, who, like Bacon and Robertson, has Southern roots, and George Soros, a Hungarian Jewish émigré. The list of highpowered, multibillion-dollar hedge funds expanded in the 1990s with a new generation that relied on computer power and analytical models, such as Long-Term Capital Management, D.E. Shaw, and Jim Simon’s Renaissance Technologies, and has continued to balloon to this day. 165 ccc_demon_165-206_ch09.qxd 7/13/07 2:44 PM Page 166 A DEMON OF OUR OWN DESIGN It would seem that any discussion of hedge funds should include a taxonomy describing all the types of strategies and instruments, putting everything into a neat set of boxes. I believe that doing so is not particularly informative, for reasons that I will spell out in Chapter 11.


pages: 422 words: 113,830

Bad Money: Reckless Finance, Failed Politics, and the Global Crisis of American Capitalism by Kevin Phillips

algorithmic trading, asset-backed security, bank run, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, buy and hold, collateralized debt obligation, computer age, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency peg, diversification, Doha Development Round, energy security, financial deregulation, financial innovation, fixed income, Francis Fukuyama: the end of history, George Gilder, housing crisis, Hyman Minsky, imperial preference, income inequality, index arbitrage, index fund, interest rate derivative, interest rate swap, Joseph Schumpeter, Kenneth Rogoff, large denomination, Long Term Capital Management, market bubble, Martin Wolf, Menlo Park, mobile money, money market fund, Monroe Doctrine, moral hazard, mortgage debt, Myron Scholes, new economy, oil shale / tar sands, oil shock, old-boy network, peak oil, plutocrats, Plutocrats, Ponzi scheme, profit maximization, Renaissance Technologies, reserve currency, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, shareholder value, short selling, sovereign wealth fund, The Chicago School, Thomas Malthus, too big to fail, trade route

Interested readers can flesh out these short summaries in well-known history books. What I would underscore here is that decline does not come out of the blue. There are always early warnings, dismissed as false alarms. Today’s four-decade U.S. pattern is cause for concern. ANGLO AMERICA DECLINING, ASIA RISING Some scholars date the rise of European world supremacy and the subordination of Asia to sixteenth-century explorers and renaissance technology, especially the maritime prowess of Spanish, Portuguese, Dutch, and English ships bristling with cannon manned by skilled gunners. On the sea, at least, the European advantage was too great to be overcome. Within fifteen years of the first arrival of Portuguese ships in Indian waters, so triumphant were they that the King of Portugal could style himself “Lord of the Conquest, Navigation and Commerce of Ethiopia, Arabia, Persia, and India.”7 Now we may be on the threshold of a comparable or greater turnabout.


pages: 391 words: 123,597

Targeted: The Cambridge Analytica Whistleblower's Inside Story of How Big Data, Trump, and Facebook Broke Democracy and How It Can Happen Again by Brittany Kaiser

Albert Einstein, Amazon Mechanical Turk, Asian financial crisis, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Burning Man, call centre, centre right, Chelsea Manning, clean water, cognitive dissonance, crony capitalism, Dominic Cummings, Donald Trump, Edward Snowden, Etonian, haute couture, illegal immigration, Julian Assange, Mark Zuckerberg, Menlo Park, Nelson Mandela, off grid, open borders, Renaissance Technologies, Robert Mercer, rolodex, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, Snapchat, statistical model, the High Line, the scientific method, WikiLeaks, young professional

He had been a brilliant IBM data scientist whose early work was largely in artificial intelligence. He built the first algorithms that enabled computers to read human speech, and he authored or coauthored many of IBM’s early papers on “Watson,” its famous computing system. Bob left IBM and went on to become the first person to use predictive modeling in the stock market, which gave rise to his status as a hedge fund baron. His fund, Renaissance Technologies, based on Long Island, was and still is the most successful hedge fund in the world, with included assets of over $25 billion.5 Bekah, one of Bob’s three daughters with his wife, Susan, was the most politically active, and took control of the family’s conservative giving strategies. Now, what more could a conservative donor family want than a media baron to produce messaging and a data science company to target those messages to their audience?


pages: 458 words: 134,028

Microtrends: The Small Forces Behind Tomorrow's Big Changes by Mark Penn, E. Kinney Zalesne

addicted to oil, affirmative action, Albert Einstein, Ayatollah Khomeini, Berlin Wall, big-box store, call centre, corporate governance, David Brooks, Donald Trump, extreme commuting, Exxon Valdez, feminist movement, glass ceiling, God and Mammon, Gordon Gekko, haute couture, hygiene hypothesis, illegal immigration, immigration reform, index card, Isaac Newton, job satisfaction, labor-force participation, late fees, life extension, low cost airline, low skilled workers, mobile money, new economy, RAND corporation, Renaissance Technologies, Ronald Reagan, Rosa Parks, Rubik’s Cube, stem cell, Stephen Hawking, Steve Jobs, Superbowl ad, the payments system, Thomas L Friedman, upwardly mobile, uranium enrichment, urban renewal, War on Poverty, white picket fence, women in the workforce, Y2K

Key articles in the development of this chapter, from which some of the anecdotes are drawn, include Speed Weed, “POPSCI Goes to Hollywood,” Popular Science, January 2007; and Jackie Burrell, “Number Mania TV Shows Go on Integer Alert,” Contra Costa Times (CA), May 31, 2006. The very wealthy math major is James Simons, former math major and math professor, and as of 2007 the head of his own hedge fund, Renaissance Technologies Corporation. XV. International Mini-Churched The New Yorker cover was Saul Steinberg’s A View of New York from 9th Avenue, and originally appeared on March 29, 1976. The data on churchgoing in France and Germany come from Robert Manchin, “Religion in Europe: Trust Not Filling the Pews,” the Gallup Poll for European Commission’s Eurobarometer survey, September 21, 2004. The first Peter Berger reference can be found in an article that was extremely helpful to this entire piece: Toby Lester, “Oh, Gods!


pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

Albert Einstein, Atul Gawande, backtesting, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

In general, higher-frequency trading succumbs to declining profit potential against nondeclining transaction costs. You might consider trading a chart with a long enough time scale that transaction costs are a minor factor— something like a daily price chart, going back a year or two.” 375 C He’s barely rated a mention in the nation’s most important newspapers, but pay close attention to what Institutional Investor wrote about him… “Jim Simons [president of Renaissance Technologies and operator of the Medallion Fund] may very well be the best money manager on earth.” Long Island Business News 376 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets Toby Crabel has made a 180-degree turn from discretionary to systematic trading. In the early days, he used discretion to devise the systemgenerated signals and to decide whether or not to take the trade signals.


pages: 538 words: 147,612

All the Money in the World by Peter W. Bernstein

Albert Einstein, anti-communist, Berlin Wall, Bill Gates: Altair 8800, call centre, Charles Lindbergh, corporate governance, corporate raider, creative destruction, currency peg, David Brooks, Donald Trump, estate planning, family office, financial innovation, George Gilder, high net worth, invisible hand, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, job-hopping, John Markoff, Long Term Capital Management, Marc Andreessen, Martin Wolf, Maui Hawaii, means of production, mega-rich, Menlo Park, Mikhail Gorbachev, new economy, Norman Mailer, PageRank, Peter Singer: altruism, pez dispenser, popular electronics, Renaissance Technologies, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sand Hill Road, school vouchers, Search for Extraterrestrial Intelligence, shareholder value, Silicon Valley, Silicon Valley startup, stem cell, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, the new new thing, Thorstein Veblen, too big to fail, traveling salesman, urban planning, wealth creators, William Shockley: the traitorous eight, women in the workforce

The mansion building in Greenwich follows an extraordinary surge in the wealth of hedge fund managers in this country. Since the turn of the twenty-first century, they have outearned everyone on Wall Street. Seventeen of the eighty-three financiers on the 2006 Forbes 400 list founded hedge funds, compared to just five in 2001. According to Alpha magazine3, the highest paid, James H. Simons, earned $1.7 billion in 2006 running his Renaissance Technologies corporation from his office on Third Avenue in New York City. But he’s hardly the only manager of a hedge fund to pay himself a billion-dollar salary: Ken Griffin and Edward Lampert also made over a billion in 2006. And others, like Steven Cohen, George Soros, and Stanley Druckenmiller, regularly take home hundreds of millions a year. In an age when the salaries of baseball players and movie stars are common knowledge, there’s hardly a household name among the moneymen, who are the best-paid professionals of them all.


Investment: A History by Norton Reamer, Jesse Downing

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Berlin Wall, Bernie Madoff, break the buck, Brownian motion, business cycle, buttonwood tree, buy and hold, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, dogs of the Dow, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, Gordon Gekko, Henri Poincaré, high net worth, index fund, information asymmetry, interest rate swap, invention of the telegraph, James Hargreaves, James Watt: steam engine, joint-stock company, Kenneth Rogoff, labor-force participation, land tenure, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, margin call, means of production, Menlo Park, merger arbitrage, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Network effects, new economy, Nick Leeson, Own Your Own Home, Paul Samuelson, pension reform, Ponzi scheme, price mechanism, principal–agent problem, profit maximization, quantitative easing, RAND corporation, random walk, Renaissance Technologies, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Sand Hill Road, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spinning jenny, statistical arbitrage, survivorship bias, technology bubble, The Wealth of Nations by Adam Smith, time value of money, too big to fail, transaction costs, underbanked, Vanguard fund, working poor, yield curve

First, the S&P 500 simply represents a single asset class (US equities), and hedge funds often have exposure to a wider array of asset classes. Second, hedge funds typically have a different (and usually lower) beta than the S&P 500 by virtue of hedging or having exposure to lower correlated Highest-paid hedge fund managers 2013, in millions 1. David Tepper Appaloosa Mgmt. 2. Steven A. Cohen SAC Capital Advisors 2,400 3. John Paulson Paulson & Co. 2,300 4. James H. Simons Renaissance Technologies 2,200 5. Kenneth C. Griffin Citadel 950 6. Israel A. Englander Millennium Mgmt. 850 7. Leon G. Cooperman Omega Advisors 825 8. Lawrence M. Robbins Glenview Capital Mgmt. 750 9. Daniel S. Loeb Third Point 700 10. Ray Dalio Bridgewater Associates 600 Paul Tudor Jones II Tudor Investment Corp. 600 12. Johnathon S. Jacobson Highfields Capital Mgmt. 500 13. Robert Citrone Discovery Capital Mgmt. 475 14.


pages: 598 words: 172,137

Who Stole the American Dream? by Hedrick Smith

Affordable Care Act / Obamacare, Airbus A320, airline deregulation, anti-communist, asset allocation, banking crisis, Bonfire of the Vanities, British Empire, business cycle, business process, clean water, cloud computing, collateralized debt obligation, collective bargaining, commoditize, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, David Brooks, Deng Xiaoping, desegregation, Double Irish / Dutch Sandwich, family office, full employment, global supply chain, Gordon Gekko, guest worker program, hiring and firing, housing crisis, Howard Zinn, income inequality, index fund, industrial cluster, informal economy, invisible hand, Joseph Schumpeter, Kenneth Rogoff, Kitchen Debate, knowledge economy, knowledge worker, laissez-faire capitalism, late fees, Long Term Capital Management, low cost airline, low cost carrier, manufacturing employment, market fundamentalism, Maui Hawaii, mega-rich, MITM: man-in-the-middle, mortgage debt, negative equity, new economy, Occupy movement, Own Your Own Home, Paul Samuelson, Peter Thiel, Plutonomy: Buying Luxury, Explaining Global Imbalances, Ponzi scheme, Powell Memorandum, Ralph Nader, RAND corporation, Renaissance Technologies, reshoring, rising living standards, Robert Bork, Robert Shiller, Robert Shiller, rolodex, Ronald Reagan, shareholder value, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Steve Jobs, The Chicago School, The Spirit Level, too big to fail, transaction costs, transcontinental railway, union organizing, Unsafe at Any Speed, Vanguard fund, We are the 99%, women in the workforce, working poor, Y2K

• The seventy-four people at the pinnacle each made $50 million or more in 2009, while recession was squeezing millions of American families. In this economic stratosphere, the average income was $518.8 million—$10 million a week. • In 2008, the year of financial collapse, half a dozen hedge fund managers each made more than $1 billion: David Tepper of Appaloosa Management, $4 billion; George Soros, $3.3 billion; James Simons of Renaissance Technologies, $2.5 billion; and John Paulson, $2.3 billion. In 2007, Paulson had already made nearly $4 billion by betting against the housing market; in 2010, he made $5 billion more by betting on rising commodity prices and the recovery of America’s big banks, thanks to a taxpayer bailout. The Geography of Richistan Translating these astounding numbers into human terms, Wall Street Journal reporter Robert Frank wrote a travelogue to the exotic domain of “Richistan.”


pages: 626 words: 167,836

The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

"Robert Solow", 3D printing, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, creative destruction, David Graeber, David Ricardo: comparative advantage, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, falling living standards, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, Gini coefficient, Hyperloop, income inequality, income per capita, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, labor-force participation, labour mobility, Loebner Prize, low skilled workers, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Norbert Wiener, oil shock, On the Economy of Machinery and Manufactures, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, social intelligence, speech recognition, spinning jenny, Stephen Hawking, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

As Joel Mokyr points out, “If inventions were dated according to the first time they occurred to anyone, rather than the first time they were actually constructed, this period may indeed be regarded just as creative as the Industrial Revolution. But the paddlewheel boats, calculating machines, parachutes, fountain pens, steam-operated wheels, power looms, and ball bearings envisaged in this age—interesting as they are to the historian of ideas—had no economic impact because they could not be made practical.”68 The best that can be said about Renaissance technology in economic terms is that it paved the way for one of humanity’s most important technological breakthroughs to date: the steam engine. The science of the steam engine started with Galileo and his secretary Evangelista Torricelli, who developed the first barometer. In 1648, Torricelli discovered that the atmosphere has weight. A number of subsequent experiments by Otto von Guericke in 1655 showed that the weight of air can be used to do work: von Guericke found that if air is pumped out of a cylinder, this pushes the piston down into it, allowing it to lift a load of weights.


pages: 496 words: 174,084

Masterminds of Programming: Conversations With the Creators of Major Programming Languages by Federico Biancuzzi, Shane Warden

Benevolent Dictator For Life (BDFL), business intelligence, business process, cellular automata, cloud computing, commoditize, complexity theory, conceptual framework, continuous integration, data acquisition, domain-specific language, Douglas Hofstadter, Fellow of the Royal Society, finite state, Firefox, follow your passion, Frank Gehry, general-purpose programming language, Guido van Rossum, HyperCard, information retrieval, iterative process, John von Neumann, Larry Wall, linear programming, loose coupling, Mars Rover, millennium bug, NP-complete, Paul Graham, performance metric, Perl 6, QWERTY keyboard, RAND corporation, randomized controlled trial, Renaissance Technologies, Ruby on Rails, Sapir-Whorf hypothesis, Silicon Valley, slashdot, software as a service, software patent, sorting algorithm, Steve Jobs, traveling salesman, Turing complete, type inference, Valgrind, Von Neumann architecture, web application

Prior to joining Xerox, Warnock held key positions at Evans & Sutherland Computer Corporation, Computer Sciences Corporation, IBM, and the University of Utah. Warnock holds B.S. and M.S. degrees in mathematics and a PhD in electrical engineering all from the University of Utah. Peter Weinberger has been at Google New York since the middle of 2003, working on various projects that handle or store large amounts of data. Before that (from the time that AT&T and Lucent split apart), Peter was at Renaissance Technologies, a fabulously successful hedge fund (for which he takes no credit at all), where he started as Head of Technology, responsible for computing, software, and information security. The last year or so, he escaped all that and worked on a trading system (for mortgage-backed securities). Until AT&T and Lucent split, he was in Computer Science Research at Bell Labs in Murray Hill. Before ending up in management, Peter worked on databases, AWK, network filesystems, compiling, performance and profiling, and no doubt some other Unix stuff.