EC Do Portfolio Factors Or Characteristics Drive Expected Returns?

This article examines a somewhat over-looked, but important, discussion that raged among academic researchers in the late 1990’s and early 2000’s. The topic: factors versus characteristics.

What do you mean, “Factors versus characteristics?”

We often highlight that the value premium can be explained by either risk and/or mispricing. A core aspect of the risk argument is that a portfolio’s factor “loading,” or covariance, on a specific factor (e.g., Fama and French HML value factor) represent a proxy for some unobserved systematic risk. The characteristics argument claims that value firms earned a higher expected return simply because they have higher B/M ratios (which may be independent of systematic risk).

The evidence from the primary set of papers (here and here) under discussion strongly suggests that investors should focus on characteristics, not factors:

As we’ll show, the debate is arguably not as clear as the chart above seems to suggest, and academics have continued to argue over the interpretation of the value premium for almost 25 years now. Why? The argument driving the value premium underlies core arguments related to how markets work — a fundamental question we still don’t completely understand.

A summary of each argument is described below:(1)

  • The risk theory says that Value stocks are inherently riskier due to a loading on an unknown distress factor. Since they are riskier, investors should demand a higher rate of return for taking on additional risk. Under this model, the value premium will continue to exist as long as investor risk preferences stay the same; however, if investor risk preferences change and they are willing to take on additional risk for a lower rate of return (unlikely!), the value premium would go away.(2).
  • The LSV behavioral theory is a mispricing theory which claims that investors incorrectly extrapolate past growth rates (behavioral error) and cause value stocks to be under-priced, and growth stocks to be over-priced–this causes the future higher (lower) returns to value (growth) stocks.(3). Under this model, the value premium will continue to exist as long as investors continue to make behavioral errors and the limits to arbitrage exist–we give a deep explanation of this model here. However, if investors stop making behavioral errors and the limits to arbitrage disappear, the value premium would go away.(4)
  • The characteristic theory claims that characteristics explain the cross-section of stock returns and that value firms had higher returns due to having a certain characteristic, such as a high B/M ratio. What are the implications for this model? Directly from the last paragraph of the 1997 Daniel and Titman here: “Another possibility is that investors consistently held priors that size and book-to-market ratios were proxies for systematic risk and, as a result, attached higher discount rates to stocks with these characteristics. … If this is the case, then the patterns we have observed in the data should not be repeated in the future.”(5)
  • So what’s the answer? A new paper entitled, “Interpreting Factor Models,” says it best:

    We argue that tests of reduced-form factor models and horse races between “characteristics” and “covariances’” cannot discriminate between alternative models of investor beliefs.

    In short, nobody really knows.

    We would argue that there is a mix of all three ideas going on at the same time. As more investors learn about an investment approach that previously had higher returns (value investing), the expected returns to that approach may diminish in the future (implication of the characteristic theory), if arbitrage constraints are minimal. (6)

    The rest of the article is broken into three sections:

  • A dive into the characteristic versus risk debate.
  • An examination of what this means for a long-only value investor
  • A review of live portfolios with a breakdown of factors and characteristics
  • Let’s dig into the debate on factors or characteristics.

    Do Portfolio Factors or Characteristics Drive Expected Returns?

    Most in finance are aware of the value premium: Historically, cheap stocks have outperformed expensive stocks (based on some price to fundamental). Below is a chart from a value premium:which highlights the performance of cheap vs. expensive portfolio simulations from 1963 to 2013.

    One of the original papers on the value premium topic was Fama and French in 1993. In their paper, they find that there are three factors (the market, size, and value proxied by Mkt_rf, SMB, and HML) that explain the cross-section of stock returns. More important to this discussion, they suggest that value is a risk factor, as value firms covary with one another. In their story, the covariance matters, and is why the loading on HML is important.(7)

    But what are some implications of this theory? Directly from FF 1993:

    The regression slopes and the historical average premiums for the factors can then be used to estimate the (unconditional) expected return for the portfolio.

    In other words, the HML loading can be used to estimate future expected returns. A higher loading implies a higher amount of risk, which implies a higher rate of expected return. This is exactly the type of interpretation we often hear from DFA advisors, many of whom have been taught by Fama and French.

    But the factor approach (and the reliance on regression slopes to make decisions) is not a panacea and has plenty of holes like any other approach.

    We have discussed in detail, an alternative explanation to risk being the driver of the value premium is the LSV behavioral theory. The LSV theory suggests that investors over extrapolate data, causing prices to sometimes deviate from fundamentals and limits to arbitrage prevent these mispricings from being “arbitraged” away.

    It is important, for this discussion, to note that the argument between the risk and LSV behavior camps does not focus on whether or not the return premia of value and small firms can be explained by a factor model–they agree it can. However, they argue whether the factors represent an economically relevant risk or mispricing that is costly to exploit.

    However, there is a 3rd explanation for the value premium, which is also a “mispricing” based argument–the premium is simply driven by characteristics (not covariances, or factors). In other words, your HML loading does NOT matter, but your portfolio book-to-market ratio does matter.

    Why Characteristics Matter: The Theory

    This theory was proposed by Kent Daniel and Sheridan Titman in 1997, in their heretitled “Evidence on the Characteristics of Cross Sectional Variation in Stock Returns.”(8)(9) This is a well-written paper. The introduction very clearly states what the paper will test, and what they find:

    In contrast, this article addresses the more fundamental question of whether the return patterns of characteristic-sorted portfolios are really consistent with a factor model at all. Specifically, we ask (1) whether there really are pervasive factors that are directly associated with size and book-to-market; and (2) whether there are risk premia associated with these factors. In other words, we directly test whether the high returns of high book-to-market and small size stocks can be attributed to their factor loadings.

    Our results indicate that (1) there is no discernible separate risk factor associated with high or low book-to-market (characteristic) firms, and (2) there is no return premium associated with any of the three factors identified by Fama and French (1993), suggesting that the high returns related to these portfolios cannot be viewed as compensation for factor risk…

    Once we control for firm characteristics, expected returns do not appear to be positively related to the loadings on the market, HML, or SMB factors.

    So what this paper is trying to argue is that after controlling for characteristics, factor exposures (i.e., factor “loadings”) don’t tell us much about expected returns. In other words, if an investor is calculating “HML loadings” as a means to tell them something about future returns, they should stop this practice. Instead, investors should identify the book-to-market ratio of their portfolio, which will be much more predictive of expected returns.

    Print Friendly, PDF & Email

    Author: Travis Esquivel

    Travis Esquivel is an engineer, passionate soccer player and full-time dad. He enjoys writing about innovation and technology from time to time.

    Share This Post On

    Submit a Comment

    Your email address will not be published. Required fields are marked *