CHAPTER 1
Why Does Quant Trading Matter?

John is a quant trader running a mid-sized hedge fund. He completed an undergraduate degree in mathematics and computer science at a top school in the early 1990s. John immediately started working on Wall Street trading desks, eager to capitalize on his quantitative background. After seven years on the Street in various quant-oriented roles, John decided to start his own hedge fund. With partners handling business and operations, John was able to create a quant strategy that recently was trading over $1.5 billion per day in equity volume. More relevant to his investors, the strategy made money on 60 percent of days and 85 percent of months—a rather impressive accomplishment.

Despite trading billions of dollars of stock every day, there is no shouting at John's hedge fund, no orders being given over the phone, and no drama in the air; in fact, the only sign that there is any trading going on at all is the large flat-screen television in John's office that shows the strategy's performance throughout the day and its trading volume. John can't give you a fantastically interesting story about why his strategy is long this stock or short that one. While he is monitoring his universe of thousands of stocks for events that might require intervention, for the most part he lets the automated trading strategy do the hard work. What John monitors quite carefully, however, is the health of his strategy and the market environment's impact on it. He is aggressive about conducting research on an ongoing basis to adjust his models for changes in the market that would impact him.

Across from John sits Mark, a recently hired partner of the fund who is researching high-frequency trading. Unlike the firm's first strategy, which only makes money on 6 out of 10 days, the high-frequency efforts Mark and John are working on target a much more ambitious task: looking for smaller opportunities that can make money every day. Mark's first attempt at high-frequency strategies already makes money nearly 95 percent of the time. In fact, their target for this high-frequency business is even loftier: They want to replicate the success of those firms whose trading strategies make money every hour, maybe even every minute, of every day. Such high-frequency strategies can't accommodate large investments, because the opportunities they find are small, fleeting. The technology required to support such an endeavor is also incredibly expensive, not only to build, but also to maintain. Nonetheless, they are highly attractive for whatever capital they can accommodate. Within their high-frequency trading business, John and Mark expect their strategy to generate returns of about 200 percent a year, possibly much more.

Per the FT, quoting Hedge Fund Research's report, quants managed over $900 billion in assets at the end of October 2017,1 nearly double the level from 2010, with continued inflows since. Aurum put the number a bit under half that amount in 2022, but even $445 billion is a significant sum, representing about 14 percent of the total assets under management they estimated are in hedge funds (and making quant the second largest category of hedge funds).2 It is clear that quants are substantial players in the market, and that they're not only here to stay, but growing.

Not all quants are successful, however. It seems that once every decade or so, quant traders cause—or at least are perceived to cause—markets to move dramatically because of their failures, though we have only about four datapoints, the most recent from 2010, at which to point. The most obvious instance is, of course, Long Term Capital Management (LTCM), which nearly (but for the intervention of Federal Reserve banking officials and a consortium of Wall Street banks) brought the financial world to its knees. Although the world markets survived, LTCM itself was not as lucky. The firm, which averaged 30 percent returns after fees for four years, lost nearly 100 percent of its capital in the debacle of August–October 1998 and left many investors both skeptical and afraid of quant traders. Never mind that it is debatable whether this was a quant trading failure or a failure of human judgment in risk management, nor that it's questionable whether LTCM was even a quant trading firm at all. It was staffed by PhDs and Nobel Prize-winning economists, and that was enough to cast it as a quant trading outfit, and to make all quants “guilty by association.”

Not only have quants been widely panned because of LTCM, but they have also been blamed (probably unfairly) for the crash of 1987 and (quite fairly) for the eponymous quant liquidation of 2007, the latter having severely impacted many quant shops. Even some of the largest names in quant trading suffered through August 2007's quant liquidation. For instance, Goldman Sachs' largely quantitative Global Alpha Fund was down an estimated 40 percent in 2007 after posting a 6 percent loss in 2006.3 In less than a week during August 2007, many quant traders lost between 10 and 40 percent in a few days, though some of them rebounded strongly for the remainder of the month.

A best-selling nonfiction book by a former Wall Street Journal reporter even attempted to cast the blame for the massive financial crisis that came to a head in 2008 on quant trading. There were gaps in his logic large enough to drive an 18-wheeler through, but the popular perception of quants has never been positive. And this is all before high-frequency trading (HFT) came into the public consciousness in 2010, after the “Flash Crash” on May 10th of that year. Ever since then, various corners of the investment and trading world have tried very hard to assert that quants (this time, in the form of HFTs) are responsible for increased market volatility, instability in the capital markets, market manipulation, front-running, and many other evils. We will look into HFT and the claims leveled against it in greater detail in Chapter 16, but any quick search of the internet will confirm that quant trading and HFT have left the near-total obscurity they enjoyed for decades and entered the mainstream's thoughts on a regular basis.

There was also the Flash Crash on May 6, 2010, during which the U.S. stock market lost some 7 percent in a mere 15 minutes, with about $1 trillion in market capitalization vanishing. Eight large cap companies, including Accenture and Exelon, fell to $0.01 per share—an exceedingly low price. Twenty minutes later, most of the loss had been recovered. Quants were widely blamed for the incident, most notably by Michael Lewis, in Flash Boys.

More recently, but less significantly, Bloomberg published an article on November 30, 2023, entitled, “Oil's Wild Ride Is Driven by a Disruptive Band of Bot Traders,” which claimed that the trend-following quant strategies add to volatility (and point only to oil prices increasing due to such pressure) by engaging in what humans have always done—follow trends. I am certain that there were no algorithms behind the bubble in tulips in Holland, nor in the roaring 1920s in the U.S. But, yes, let's blame the quants. As an apropos error in reporting, the authors quote a quant from Cayler Capital. While they correctly categorize Cayler as a Commodity Trading Advisor (CTA, for short, and a type of institution that is distinguished only by its trading of futures on behalf of clients—not by being systematic in so doing), they lump his firm in with trend followers. Even more ironically, this article merely recounts an anecdote in which the portfolio manager decided not to intervene in his models, which happened to be positioned correctly for the Russian invasion of Ukraine, vis-à-vis oil prices.4

Leaving aside the spectacular successes and failures of quant trading, and all the ills for which quant trading is blamed by some, there is no doubt that quants cast an enormous shadow over the capital markets virtually every trading day. Across U.S. equity markets, a significant, and rapidly growing, proportion of all trading is done through algorithmic execution, one footprint of quant strategies. (Algorithmic execution is the use of computer software to manage and “work” an investor's buy and sell orders in electronic markets.) Although this automated execution technology is not the exclusive domain of quant strategies—any trade that needs to be done, whether by an index fund or a discretionary macro trader, can be worked using execution algorithms—certainly a substantial portion of all algorithmic trades are done by quants. Furthermore, quants were both the inventors of, and primary innovators of, algorithmic trading engines. A mere five such quant traders account for about 1 billion shares of volume per day, in aggregate, in the United States alone. It is worth noting that not one of these is well known to the broader investing public, even now, after all the press surrounding high-frequency trading. As of 2017, algorithmic trading—which to be clear, represents only the execution of trades, not whether the determinant of that investment decision came via a human utilizing a trading algorithm or a systematic investing strategy utilizing potentially the same kind of algorithm—accounted for about 70 percent of equity trading, 50 percent of futures trading, 40 percent of options trading, 25 percent of foreign exchange...