The stock market appears strangely indifferent to Covid-19 these days, but that wasn’t true in March, as the scale and breadth of the crisis hit home. By one measure, it was the most volatile month in stock market history; on March 16, the Dow Jones average fell almost 13 percent, its biggest one-day decline since 1987.
To some, the vertigo-inducing episode also exposed a weakness of quantitative (or quant) trading firms, which rely on mathematical models, including artificial intelligence, to make trading decisions.
Some prominent quant firms fared particularly badly in March. By mid-month, some Bridgewater Associates funds had fallen 21 percent for the year to that point, according to a statement posted by the company’s co-chairman, Ray Dalio. Vallance, a quant fund run by DE Shaw, reportedly lost 9 percent through March 24. Renaissance Technologies, another prominent quant firm, told investors that its algorithms misfired in response to the month’s market volatility, according to press accounts. Renaissance did not respond to a request for comment. A spokesman for DE Shaw could not confirm the reported figure.
The turbulence may reflect a limit with modern-day AI, which is built around finding and exploiting subtle patterns in large amounts of data. Just as algorithms that grocers use to stock shelves were flummoxed by consumers’ sudden obsession with hand sanitizer and toilet paper, those that help hedge funds wring profit from the market were confused by the sudden volatility of panicked investors.
In finance, as in all things, the best AI algorithm is only as good as the data it’s fed.
Andrew Lo, a professor at MIT and the founder and chairman emeritus of AlphaSimplex, a quantitative hedge fund based in Cambridge, Massachusetts, says quantitative trading strategies have a simple weakness. “By definition a quantitative trading strategy identifies patterns in the data,” he says.
Lo notes that March bears similarities to a meltdown among quantitative firms in 2007, in the early days of the financial crisis. In a paper published shortly after that mini-crash, Lo concluded that the synchronized losses among hedge funds betrayed a systemic weakness in the market. “What we saw in March of 2020 is not unlike what happened in 2007, except it was faster, it was deeper, and it was much more widespread,” Lo says.
Zura Kakushadze, president of Quantigic Solutions, describes the March episode as a “quant bust” in an analysis of the events posted online in April.
Kakushadze’s paper looks at one form of statistical arbitrage, a common method of mining market data for patterns that are exploited by quant funds through many frequent trades. He points out that even quant funds that employed a “dollar-neutral” strategy, meaning they bet equally on stocks rising and falling, did poorly in the rout.
In an interview, Kakushadze says the bust shows AI is “no panacea” during extreme market volatility. “I don't care whether you’re using AI, ML, or anything else,” he says. “You’re gonna break down no matter what.”
In fact, Kakushadze suggests that quant funds that use overly complex and opaque AI models may have suffered worse than others. Deep learning, a form of AI that has taken the tech world by storm in recent years, for instance, involves feeding data into neural networks that are difficult to audit. Machine learning, and especially deep learning, “can have a large number of often obscure (uninterpretable) parameters,” he writes.
Ernie Chan, managing member of QTS Capital Management, and the author of several books on machine trading, agrees that AI is no match for a rare event like the coronavirus.
“It’s easy to train a system to recognize cats in YouTube videos because there are millions of them,” Chan says. In contrast, only a few such large swings in the market have occurred before. “You can count [these huge drops] on one hand. So it’s not possible to use machine learning to learn from those signals.”
Still, some quant funds did a lot better than others during March’s volatility. The Medallion Fund operated by Renaissance Technologies, which is restricted to employees’ money, has reportedly seen 24 percent gains for the year to date, including a 9 percent lift in March.
Others are cautious about drawing conclusions about quantitative trading from March. Relationships among stocks that some firms use to craft their portfolios broke down during the month, says Ewan Kirk, president of GAM Systematic Cantab, a trading firm in the UK. “But these relationships have held true for decades,” Kirk says. “They broke down for a couple of months. Does this mean that they will never work again? Seems unlikely.”
Nir Vulkan, a professor at Oxford University who teaches a course on algorithmic trading, agrees that the “quant bust” was not in fact a bust for some firms. “Some funds were on the right side of it and have had amazing years,” he says.
Vulkan expects investors will cast a wider net for data to feed to their algorithms, in hopes of detecting signals of unusual economic activity. Some already use “alternative data” such as satellite imagery, flight and shipping information, and social media content. “The most powerful thing is these new sources of data,” he says. “Some of them are really, really, useful.”