A longstanding academic theory for describing how markets work has finally died. Its impact on investment theory and practice was enormous, but has also led to some risks. The so-called efficient market hypothesis (EMH), which basically assumes that investors are rational and that it's impossible to beat the market because prices reflect all available information, has been under fire for years. And MIT's Andrew Lo, who Time Magazine named as one of the 100 most influential people in the world in 2012, finally put an end to it in his new book, “Adaptive Markets: Financial Evolution at the Speed of Thought.”
Ever since I was a graduate student in economics, I’ve been struggling with the uncomfortable observation that economic theories often don’t seem to work in practice. That goes for that most influential economic theory, the Efficient Markets Hypothesis, which holds that investors are rational decision makers and market prices fully reflect all available information, that is, the “wisdom of crowds.”
Build a better mousetrap, the saying goes, and the world will beat a path to your door. Find a way to beat the stockmarket and they will construct a high-speed railway. As investors try to achieve this goal, they draw on the work of academics. But in doing so, they are both changing the markets and the way academics understand them.
Bloomberg View columnist Barry Ritholtz interviews Andrew Lo, director of the Laboratory for Financial Engineering and the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management. Lo holds a bachelor’s degree in economics from Yale University and a doctorate in economics from Harvard University. This commentary aired on Bloomberg Radio.
Early in January in a Chicago hotel, Campbell Harvey gave a rip-snorting presidential address to the American Finance Association, the world’s leading society for research on financial economics. To get published in journals, he said, there’s a powerful temptation to torture the data until it confesses—that is, to conduct round after round of tests in search of a finding that can be claimed to be statistically significant. Said Harvey, a professor at Duke University’s Fuqua School of Business: “Unfortunately, our standard testing methods are often ill-equipped to answer the questions that we pose.” He exhorted the group: “We are not salespeople. We are scientists!”
It was a typical New York summer day, the kind where arriving at Goldman Sachs’ perfectly air-conditioned offices in downtown Manhattan was a blissful release from the humid weather outside. But for Gary Chropuvka it proved to be one of the worst days of his life. Mr Chropuvka worked at Goldman’s money management arm, specifically at Quantitative Investment Strategies, a division staffed by mathematicians, computer scientists and physicists. Even at Goldman, the QIS employees were considered intellectual superstars. Their prowess at decoding the signals of financial markets meant the unit managed $165bn at its peak — more than any hedge fund group. But on August 6 2007, everything unravelled. As soon as US markets started trading, the previously wildly successful automated investment algorithms coded by the QIS brainiacs went horribly awry, and losses mounted at a frightening pace.
Andrew W. Lo of the Massachusetts Institute of Technology was named winner of the top $2,500 prize in the Bernstein Fabozzi/Jacobs Levy Awards. Mr. Lo, a professor of finance at the Sloan School of Management and director of the MIT Laboratory for Financial Engineering, was honored for his article “What is an Index?”
Es besteht eine große Kluft zwischen Daytrading/technischer Analyse und der akademischen Welt. Dies läßt sich auf die effiziente Markthypothese reduzieren, über die Sie hier mehr erfahren können. Für viele Akademiker sind Daytrader Krach machende Trader, und die technische Analyse ist bestenfalls Voodoo. Demzufolge gibt es zwischen diesen beiden Fronten keinen Austausch. Daytrader sind auf Ihre Tradingcharts fixiert. Professoren der Finanzwissenschaft labern vor Ihren Studenten, während sie versuchen, Ihre Forschungen in renommierten Fachzeitschriften unterzubringen. Der Ideenaustausch ist jedenfalls gering.
Andrew Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management and director of the MIT Laboratory for Financial Engineering. In his research, he straps sensors to traders and watches how their pulses and body temperatures change when markets dive or trades go bad. The technology could be used elsewhere in a bank to potentially address problems before they escalate.
AlphaGo, an artificially intelligent (AI) software program, defeated the world champion of an ancient board game called Go on March 15, 2016. The game is immensely complex, with a total combination of possible moves numbering several hundred orders of magnitude more than the number of atoms in the universe. Winning the series four-to-one, AlphaGo’s victory showcased significant advances in AI’s ability to recognise and learn obscure patterns, and adapt strategies.