Quantitative equity management is concerned with rigorous, disciplined approaches to help investors structure optimal portfolios to achieve the outcomes they seek. At the root of disciplined, modern investment processes are two things: risk and return. The notion of total return is obvious—price appreciation plus any dividend payments. Risk may not be so straightforward. In most quantitative approaches, risk is viewed as more akin to a roulette wheel; that is, the possible outcomes are well specified and the likelihood of each outcome is known, but in advance, an investor does not know which outcome will be realized.
In this piece, we curate the history of quantitative equity investing, which traces its origins to the development of portfolio theory and the capital asset pricing model (CAPM). In equities, some of the first quantitative approaches were aimed at confirming the theoretical predictions of the CAPM. In particular, the expected return of a risky asset depends only on the risk of that asset as measured by its beta, a covariance measure of risk. In this paradigm, all investors hold the same risky portfolio, the market portfolio of risky assets that maximizes the Sharpe ratio. At the same time, stock prices are viewed to be informationally efficient and reflecting all available information.
By the early 1980s, this simple view of the world was punctured by the discovery of stock market anomalies. Researchers discovered that variables other than beta could explain the cross section of expected returns. In particular, size and value were found to contain useful explanatory power. By the 1990s, the anomalies morphed into the mainstream as the anomalies were re-labeled as factors, and the benchmark model, at least in academic research, was a three-factor model with beta, size, and value. Concurrent with the three-factor model, other credible factors muscled their way into the credible empirical asset pricing world, including momentum, liquidity, quality, and volatility. Indeed, in 2011, the president of the American Finance Association described the proliferation of factors as a “zoo of new factors.”1 Recent work suggests using a much higher standard to accept new factors.
With diminishing acceptance of the view that capitalization-weighted indexes are optimal for all investors, factor investing has taken off in practice. Sometime these “smart factors” are called smart beta. Morningstar reported that factor investing is the fastest-growing segment of the investment management marketplace. Investors have recognized that low-cost exposure to other factors might give them superior risk/return trade-offs.
Of course, active investors are still looking for ways to improve performance over more-passive smart beta indexes. In this race, big data approaches offer the potential to grab an insight before it becomes widely known. Another promising avenue is the ability to dynamically adjust allocations to different factors based on the macroeconomic environment and investment conditions. Active managers are also exploring better ways to construct portfolios.
In short, quantitative equity management is alive and well and intellectually active as investors seek to better manage risk and return. Commercially, factor investing has taken off in the form of smart beta. Products and strategies, vetted by decades of prior and current research, are continually being developed. One might reasonably forecast that dynamic factor-timing strategies will be a growth area for the quantitative equity field. A new generation of big data approaches is developing in the field and is likely to grow as technology becomes more capable and more data are digitally available. Despite the advances in theory, modeling, and technology, the goal of quantitative equity management techniques is an old one: aiding investors to achieve more efficient and appropriate investment outcomes.
1J. Cochrane, “Presidential Address: Discount Rates,” Journal of Finance 66 (August 2011): 1047–108.