Time and time again, the evidence suggests that humans’ record in picking investments is quite poor, and there is a surge of interest in artificial intelligence for financial prediction and risk management. But should we trust our money to a robot? The answer depends on the type of investing.
Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. In this article, I contrast human and machine strengths and weaknesses in making investment decisions. The analysis reveals areas in the investment landscape where machines are already very active and those where machines are likely to make significant inroads in the next few years.
Computers are making more and more decisions for us, and increasingly so in areas that require human judgment. Driverless cars, which seemed like science fiction until recently, are expected to become common in the next 10 years.1 There is a palpable increase in machine intelligence across the touchpoints of our lives, driven by the proliferation of data feeding into intelligent algorithms capable of learning useful patterns and acting on them. We are living through one of the greatest revolutions in our lifestyles, in which computers are increasingly engaged in our lives and decision making, to a degree that it has become second nature. Recommendations on Amazon or auto-suggestions on Google are now so routine, we find it strange to encounter interfaces that don't anticipate what we want. The intelligence revolution is well under way, with or without our conscious approval or consent. We are entering the era of intelligence as a service, with access to building blocks for building powerful new applications.
A natural question to ask is how we should be thinking about the role of computers in managing our financial lives. Should we trust our money to a robot? In an era of big data and machines to make sense of it all, do machines have an inherent advantage over humans? There is a surge of interest in artificial intelligence for financial prediction and risk management. Should we pay attention? Or is this an area where human judgment and input is always essential?
There’s a lot at stake. It is estimated that global assets under management will reach $100 trillion by 2020 from their current level of $70 trillion, with roughly almost half of this asset base in the United States.2 If we assume a net revenue of 30 basis points3, this translates into a $300 billion market.
Financial experts can serve an important role in defining our financial objectives and tailoring our investments to our profiles. For example, they apply guidelines such as steering older people with limited means toward less risky investments; provide advice on diversification, implications of changing tax laws and regulation; and more. However, time and time again, the evidence suggests that humans’ record in picking investments is quite poor. There is a lot of academic literature on human biases in decision making that result in irrational decisions4, misguided overconfidence, and incorrect attribution5 that arguably makes us bad investors. In a class on systematic trading strategies that I have been teaching for more than 10 years at New York University, most students admit that despite their diligence and experience, they typically do worse than the market, often citing emotion and complexity as reasons for their underperformance. Consistent with the literature, they report trading more than they should6, holding losers too long hoping for a rebound, and exiting winners too quickly out of fear of giving back gains.7
How does one explain the performance of legendary managers such as Stanley Drukenmiller, George Soros, and Warren Buffet? Druckenmiller attributes his early career success from recognizing a few special investment opportunities. The first was the overthrow of the Shah of Iran, which argued for rising oil prices, and making a concentrated bet in going long oil and defense stocks. His and Soros’s best-known trade, however, was selling the British Pound and buying the Deutsche Mark in September 1992. This was based on the very different stances of two central banks driven by different economic states of their underlying economies, but where the two currencies were linked and something “had to give”—in this case, their linkage that forced down the British Pound. While one might uncharitably attribute this specific trade to luck, Druckenmiller’s subsequent record of zero losing years out of 30 makes it quite apparent that his prescience was exceptional in recognizing special economic situations with large upside with limited risk.
“Buffet’s alpha,” which has been long lived, in contrast derives from a more systematic and definable strategy of buying cheap, safe, quality stocks and the use of leverage obtainable to him at favorable terms through his insurance businesses and other sources.8 Figure 1, below, puts Buffet's performance in perspective.
FIG. 1. Information ratio distribution of mutual fund managers with more than 30 years of performance between 1926 and 2011.
The figure shows the information ratios [This is defined as the ratio of annualized average excess return over a benchmark (such as the S&P500) divided by the standard deviation of these returns. Buffet's alpha is reported over the S&P500.] of all actively managed equity funds in the Center for Research of Securities Prices (CRSP) mutual fund database with at least 30 years of history between 1926 and 2011.8 Buffet’s Information Ratio of roughly 0.7 stands out in this cohort and is roughly double that of the S&P500, which hovers in the range of 0.3 to 0.4. Most managers underperform the market, a pattern that has been noted in industry and academic reports over the years. Clearly, it is difficult to outperform a carefully constructed basket representing the healthiest companies across the economy. While human genius does exist, it is very hard to find the next Druckenmiller or Buffett. Might one have more success finding a good robot?
Not surprisingly, the answer is that it depends on the adequacy of various types of data. The greater the number of independent examples to learn from, and the greater the “signal” in the data, the more likely that a robot will do well. To illustrate how this plays out in investing, Figure 2, below, sketches out the investment landscape in terms of the holding periods of managers and divides this into roughly three investment styles—less than a day, days to weeks, and months to years.
FIG. 2. Investment landscape by holding period.
On the left is the high-frequency space, which is dominated by big data and fast machines. Gone are the days of day traders staring feverishly at screens to seize short-lived opportunities. At this time scale, there are large numbers of independent data samples for intelligent algorithms to discover repeated and exploitable patterns. Decisions are very frequent, in the hundreds or thousands per day, but only a small amount of risk can be allocated to each decision because the limited liquidity in the market severely constrains the size of each decision. In other words, while the signal quality is high, the size of the opportunity in this space is low.