I’ve said several times that “you can’t beat the market,” and I’ve conjured up a host of Wall Street Icons that back that position. What follows may seem like a retraction of that firm statement, but in reality, it is not. To understand how to beat the market, you must first understand something that I said very early on in this book: “The word markets is plural.” The vast majority of investors define “the market” as the return of the S&P 500 index for a given period.
This is a common strategy for American investors, but it lacks a firm foundation in logic and smacks of elitism. US stocks make up about 50% of global equity markets, and that position declines a little every year. Much risk is country and currency specific. If we expand our idea of what a “market” is, we can find markets that offer high potential returns with not necessarily less risk, but different risk. When we divide equities into clusters based on certain factors, we find that they perform differently in different economic conditions.
In the design of experiments, scientists refer to hypothetical causal variables as factors. Anyone who has ever taken a statistics course for the social or behavioral sciences may still cringe a little at the mention of a “multifactor model.” The idea of this family of statistical techniques is to see of an equation can be derived to explain the variability of a variable (such as the return of a security) given one or more predictor variables. These predictor variables are also called factors. In economics, the term factor is used in the same general way. Factors, then, are characteristics of securities that create a class of investment vehicles that can consistently earn higher returns over time. The reason that this works is that we are not trying to beat the market; we are investing in different markets composed of different securities.
Note that these factors are not magical, and they do not defy economic theory. The reason that they offer greater returns is that they come with greater risk. If you invested in a single factor, you would be taking on more risk than you would simply invest in the S&P 500. There is no getting around the fact that investors are rewarded for taking risks and nothing else. (Unless you consider the idea of mispricing and arbitrage, but that is a rare beast that we are not likely to find).
The question of what drives stock returns has been a staple of modern finance. The oldest and most well-known model of stock returns is the Capital Asset Pricing Model (CAPM), which became a foundation of modern financial theory in the 1960s. In the CAPM, securities have only two main drivers: systematic risk and idiosyncratic risk. Systematic risk in the CAPM is the risk that arises from exposure to the market and is captured by beta, the sensitivity of a security’s return to the market. Since systematic risk cannot be diversified away, investors are compensated with returns for bearing this risk. In other words, the expected return to any stock could be viewed as a function of its beta to the market.
Later, Ross proposed a different theory of what drives stock returns. Arbitrage pricing theory (APT) holds that the expected return of a financial asset can be modeled as a function of various macroeconomic factors or theoretical market indexes. We can credit Ross with popularizing the original term factors, as the models he popularized were called “multi-factor models.” Importantly, APT, unlike the CAPM, did not explicitly state what these factors should be. Instead, the number and nature of these factors were likely to change over time and vary across markets. Thus, the challenge of building factor models became, and continues to be, a numbers game played by those steeped in statistics and data analysis.
In general, a factor can be thought of as any characteristic relating to a group of securities that is important in explaining their returns and risk. As noted in the early CAPM-related literature, the market (beta) can be viewed as the first and most important equity factor. Beyond the market factor, researchers generally look for factors that are persistent over time and have strong explanatory power over a broad range of stocks. Since, unlike stock returns, factors cannot be directly observed, there, of course, remains a vigorous debate about how to define and estimate them.
There are three main categories of factors recognized in the academic literature today: macroeconomic, statistical, and fundamental. Macroeconomic factors include measures such as surprises in inflation, surprises in GNP, surprises in the yield curve, and other measures of the macroeconomy. Statistical factor models identify factors using statistical techniques such as principal components analysis (PCA) where the factors are not pre-specified in advance.
The most widely used factors today are fundamental factors. Fundamental factors capture stock characteristics such as industry membership, country membership, valuation ratios, and technical indicators, to name a few. The most commonly discussed factors today include value, growth, size, and momentum. These have been studied for decades as part of the academic asset pricing literature and the practitioner risk factor modeling research.
One of the best-known efforts in this space came from Eugene Fama and Kenneth French in the early 1990s. Fama and French put forward a model explaining US equity market returns with three factors: the “market” (based on the traditional CAPM model), the size factor (large vs. small capitalization stocks), and the value factor (low vs. high book to market). The “Fama-French” model, which today includes Carhart’s (1997) momentum factor, has become the fundamental model in the finance literature.
We will stick to our convention of calling market return the beta factor. In the past few decades, researchers have studied a host of other stock traits, from income statement and balance sheet measures like earnings revisions and accruals to technical indicators like volatility and relative strength (momentum). The latest research has even looked at non-traditional factors like the number of “Google” hits a stock receives or the number of times it is “tweeted.”
If we limit ourselves to a handful of criteria, we can sort through the universe of 600+ factors and determine a handful that is of use to the long-term investor.
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Last Updated: 6/25/2018