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How HFT Is Changing What We Know About the Market | Chicago Booth Review 7/9/2017 How HFT is changing what we know about the market | Chicago Booth Review How HFT is changing what we know about the market EMILY LAMBERT | JUN 18, 2015 SECTIONS FINANCE hen TV producers are looking for footage to illustrate financial news, the easiest choice is often the trading floor of an exchange, with traders gesticulating and shouting. This summer, some of those images will be confined to history. CME Group, http://reviewW.chicagobooth.edu/magazine/summer-2015/how-high-frequency-trading-is-changing-what-we-know-about-the-market?cat=markets&src=Magazine 1/15 7/9/2017 How HFT is changing what we know about the market | Chicago Booth Review the world’s biggest futures exchange, is closing almost all the Chicago pits where generations of traders have exchanged futures and options contracts with screams and hand signals. Much of the W work of those traders is now automated, executed by algorithms that place thousands of orders every second, and that race each other to reach the exchange’s servers. RECOMMENDED READING Needed quickly: Good buyers for bad debts Why you’re wrong about a future stock market collapse How Fed rate moves affect the economy This is the latest example of how electronic trading, and more recently high- frequency trading (HFT), has changed the market. But it’s not just the speed and the means of execution that have changed: the data that financial markets produce are changing what we know—or thought we knew—about financial markets. Armed with huge amounts of data, and enough computing power to make sense of them, econometricians and statisticians are revisiting and poking holes in some long-held theories about how markets work. Some of those theories were built on daily data points collected from bound books kept in libraries. But in the high-frequency era, do these hypotheses need to be updated? “There has been a proliferation of data, and that has led to a renaissance in empirical research,” says MIT’s Andrew Lo. Market practitioners may dismiss some of this work as academic exercise. After all, academics use past data to explain how the market has operated, while practitioners focus on anticipating future market movements. Smart traders use econometric models—many devised by academics, many others by practitioners—as tools “for thinking about things and exploring things, not necessarily as gospel,” says http://review.chicagobooth.edu/magazine/summer-2015/how-high-frequency-trading-is-changing-what-we-know-about-the-market?cat=markets&src=Magazine 2/15 7/9/2017 How HFT is changing what we know about the market | Chicago Booth Review Columbia University’s Emanuel Derman, author of Models. Behaving. Madly. But Lo compares the relationship between academics and market practitioners to that between scientists and engineers. When academics in finance undertake research, Wall Street engineers take their basic insights and turn them into trading strategies, meaning the research directly shapes automated trading strategies. Research being conducted by Dacheng Xiu, assistant professor of econometrics and statistics at Chicago Booth, and his collaborators illustrates the change under way. Using snapshots of data, the researchers are poking and prodding at long-held theories, including a methodology that earned its creator a Nobel Prize. The Generalized Method of Moments Econometric models tend to be highly geeky. When the University of Chicago’s Lars Peter Hansen won the Nobel Memorial Prize in Economic Sciences in 2013, many journalists struggled to explain his work, the Generalized Method of Moments (GMM). As one of Hansen’s two co-winners, Robert Schiller of Yale, explained in the New York Times, “Professor Hansen has developed a procedure . for testing rational-expectations models—models that encompass the efficient-markets model— and his method has led to the statistical rejection of many more of them.” (Eugene F. Fama, Robert R. McCormick Distinguished Service Professor of Finance at Chicago Booth, also shared the Nobel that year.) Xiu offers another way of thinking about it: the GMM provided a general framework and guidance for how to apply models to real-life data. On Wall Street, he says, traders and their firms use the GMM, or some version of it, to test theoretical models using market data. The GMM functions, therefore, as a bridge between academic theories and empirical data. One industry expert interviewed for this article (using perhaps a narrower definition of GMM than Xiu does) estimates that only half of Wall Street quants know the GMM exists, and only 5 percent of them explicitly use it. Xiu responds that the GMM has been so thoroughly adapted by the financial industry http://review.chicagobooth.edu/magazine/summer-2015/how-high-frequency-trading-is-changing-what-we-know-about-the-market?cat=markets&src=Magazine 3/15 7/9/2017 How HFT is changing what we know about the market | Chicago Booth Review that many traders may not even realize they’re using it. The GMM was published in 1982, practically ancient history to today’s financial markets. As a tool to link theories to contemporary markets, Xiu says, it has two main limitations. First, today’s markets move far faster than they did in 1982, and a day’s worth of trading volume is many orders of magnitude larger than what it was. Today, many models are built to predict what the market will do in the next hour or minute, rather than the next decade. “Hansen’s approach is designed for long-range time series over decades, and it has to be adapted for this setting,” says Xiu. Second, though risk has always been a part of trading, the measure of risk—volatility —barely existed 30 years ago. In 1982, Nobel Prize winner Robert Engle of NYU developed the celebrated ARCH model, which described the dynamics of the volatility for the first time. In 1993 the Chicago Board Options Exchange announced real-time reporting of what would become the Volatility Index, commonly known as Wall Street’s “fear gauge.” The VIX, a measurement of the implied volatility of S&P 500 index options, shows how volatile the options market expects the stock market to be in the next 30 days. Since the CBOE in 2003 revised the methodology it uses to calculate the VIX, multiple firms have launched exchange-traded volatility derivatives, and investors have embraced those contracts enthusiastically. To use the GMM, academics and traders now often have to make strong assumptions, such as assuming that volatility follows a specific pattern or can be perfectly estimated. Xiu and Duke University’s Jia Li propose a way to tweak the GMM to make it more applicable to contemporary markets. They have created a version that, in homage to the original, they call the Generalized Method of Integrated Moments (GMIM). And they’ve been taking it out for an empirical test-drive using some of the highest- frequency data collected by exchanges and available from data vendors. The CBOE updates VIX data every 15 seconds, while transaction prices of futures and stocks http://review.chicagobooth.edu/magazine/summer-2015/how-high-frequency-trading-is-changing-what-we-know-about-the-market?cat=markets&src=Magazine 4/15 7/9/2017 How HFT is changing what we know about the market | Chicago Booth Review have been time-stamped down to every second. Data updated every millisecond have only recently become available to academics, Xiu says, and he also plans to use them. Fischer Black’s leverage hypothesis Before developing the GMIM, Xiu was part of a team revisiting the late economist Fischer Black’s influential 1976 hypothesis regarding leverage. In the stock market, a stock’s volatility tends to move higher when the stock price moves down—particularly in indexes such as the S&P 500. Black believed that the negative relationship between an asset’s volatility and its return could be explained by a company’s debt- to-equity ratio. When the price of General Motors shares declines, for example, the volatility of the shares rises. Intuitively, it makes sense: the more leveraged a company is, the more volatile its shares are likely to be. But “to study various financial theories about the leverage effect, one would relate the leverage effect to macroeconomic variables and firm characteristics, which are typically updated at a monthly or quarterly frequency,” write Xiu and the University of Montreal’s Ilze Kalnina. They wanted to evaluate the well-known hypothesis using data from far smaller periods of time. As their research notes, volatility is estimated rather than precisely measured. A common strategy for overcoming that obstacle has been to create preliminary estimates of volatility over small windows of time, then compute the correlation between those estimates and the investment’s returns. The approach, however, introduces a lot of statistical noise over what econometricians consider short periods, such as a month or a quarter. Even with years of data, the correlation remains insignificant. Xiu and Kalnina replaced preliminary estimates of volatility with data from two sources: stock prices or index futures, and high-frequency observations available for the VIX or an alternate volatility instrument. Overall, they conclude, there is evidence that Black’s leverage hypothesis still holds: a company’s debt-to-equity ratio helps http://review.chicagobooth.edu/magazine/summer-2015/how-high-frequency-trading-is-changing-what-we-know-about-the-market?cat=markets&src=Magazine 5/15 7/9/2017 How HFT is changing what we know about the market | Chicago Booth Review explain the relationship between volatility and returns. But they also find there could be other factors at work, including credit risk and liquidity risk. The debt-to-equity ratio is “not necessarily the dominant variable,” Xiu says.
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