Quantitative investing consists of establishing a set of rules (perhaps with help from a computer) and having a computer carry them out.
There are at least two principal forms of quantitative investing. The first might be called “systematic factor investing.” The process goes like his:
- The manager conducts an examination of a period in history, which shows that superior returns were associated with certain “factors.” Factors are attributes that characterize securities, such as value, quality, size and momentum. Perhaps in a given period the stocks that did best were characterized by strong value, high quality, large capitalizations and recent appreciation (or “momentum”). Thus the manager concludes that his portfolio should consist of stocks that rank high in those factors. (Of course those factors don’t always lead to above average returns; if things change, growth, low quality, smallness and recent underperformance might be associated with superior returns instead.)
- The manager instructs its computer to search for securities that offer the most of those factors for the money. Thus, for example, the computer might search for value based on measures including price/earnings ratio, enterprise value/EBITDA ratio, price/book ration and price/free cash flow ratio, as well as industry-specific metrics such as the ratio of price to reserves for oil companies.
- Then the manager tells the companies in what proportion to weight the search criteria, and the computer proceeds systematically to populate the portfolio with securities that deliver the optimal mix of the factors.
- Finally, the computer is instructed to assess the attendant risk. The portfolio is optimized, constraining even the most attractive components in order to limit the representation of individual stocks and perhaps industries, as well as the risk introduced by likely correlations among the stocks. The portfolio is formulaically derived according to the rules, usually without human intervention.
The end product of this process is a portfolio that, according to the algorithm, will deliver the highest expected return with the least risk (under the assumption that the factors associated with superior returns in the past will continue to be so associated in the future, and that assets will be volatile and correlated as in the past).
The other main form of quantitative investing is “statistical arbitrage” or “stat arb.” For the most part, the stat arb computer responds to disequilibria between the price of one stock and the prices of other stocks or the market as a whole, and it acts on the assumption that the relationships will revert to normal. The predictions made are profitable only slightly more often than not, but if you do it often enough and on enough leverage, stat arb can produce meaningful returns on equity. Among the lessons learned from Long-Term Capital Management in the late 1990s were that (a) the opportunities for stat arb are limited in size, (b) the capital directed at it must likewise be limited, (c) the leverage employed must be reasonable in order for the investor to survive those periods when historic relationships and probabilities fail to hold, and (d) likewise, it’s important to appropriately hedge out the market’s overall directional risk.
Quantitative investors program their computers to emulate behavior that was profitable in the past or that is expected to be profitable in the future. In other words, they set rules or formulas for their computers to live by. The key question is whether, in a competitive, dynamic and interconnected arena like investing, the route to profitability can be captured in a formula, and whether changes in the investment environment (perhaps caused by the very implementation of the formula) won’t negate the formula’s effectiveness.
Quantitative investing corrects many of the shortcomings of active management. It can do much of what people do, generally without making “human mistakes.” It can handle infinitely more data. It excludes emotion; it never buys on euphoria or sells in panic. It never forgets to rebalance: to sell the things that are expensive and buy the things that are cheap.
Now for limitations. Like passive investing, quantitative investing is also a free-riding strategy: it profits from disequilibria caused by others. The supply of “nickels and dimes” is limited to the extent of those disequilibria, and thus only a limited amount of capital can be run this way to great advantage. There has to be a reason why the best quant firm—Renaissance Technologies—has returned all outside capital from its flagship Medallion Fund; if an investment approach is infinitely scalable, by definition it’s never economic to limit the capital under management.
And there are bigger-picture questions: Can quantitative investing make superior qualitative decisions? And can it invest for the long term? This brings us to a quotation from sociologist William Bruce Cameron, although many people attribute it to Albert Einstein: not everything that can be counted counts, and not everything that counts can be counted.
Computers can do an unmatched job dealing with the things that can be counted: things that are quantitative and objective, but many other things—qualitative, subjective things—count for a great deal, and it’s doubtful that computers can do what the very best investors do:
- Can they sit down with a CEO and figure out whether he’s the next Steve Jobs?
- Can they listen to a bunch of venture capital pitches and know which is the next Amazon?
- Can they look at several new buildings and tell which one will attract the most tenants?
- Can they predict the outcome of a bankruptcy reorganization where the parties may have motivations other than economic maximization?
Further, quantitative investing’s emphasis on profiting from short-term dislocations leaves a lot more to be mined. So much of investing these days considers only short run that there’s great scope for superior active investors to make value-additive decisions concerning the long run. There’s no reason to believe computers can make these in a superior way.
The greatest investors aren’t necessarily better than others at arithmetic, accounting or finance; their main advantage is that they see merit in qualitative attributes and/or in the long run that average investors miss. And if computers miss them too, it’s doubtful that the best few percent of investors will be retired anytime soon.
Computers, artificial intelligence and big data will help investors know more and make better quantitative decisions. But until computers have creativity, taste, discernment and judgment, there is likely to still be a role for investors with alpha.
Finally, similar to index investing: if the day comes when intelligent machines run all the money, won’t they (a) see everything the same, (b) reach the same conclusions, (c) design the same portfolios, and thus (d) perform the same? What, then, will be the route to superior performance? Humans with superior insight.