Surprise! Factor Betas Don’t Deliver Factor Alphas
We show that factors constructed from fundamental characteristics have earned high returns, while those constructed from statistical betas have earned returns close to zero.
By choosing investment strategies to intentionally create exposure to factor betas, investors may be gaining exposure to uncompensated risks.
When designing factor-based investment strategies, investors should seek exposure to the fundamental characteristics that define a factor and use statistical measures of factor betas to manage factor risks.
Seeking to obtain exposure to factor betas is a misguided means to obtain the returns available from factor investing.
Vitali Kalesnik is the corresponding author.
When choosing among factor investing strategies, investors benefit from understanding the key difference between the two principal ways of measuring factor exposures: characteristics and betas. Characteristics measure the fundamental tilts of a portfolio by aggregating financial metrics including value measures such as price-to-book and price-to-earnings ratios; profitability measures such as earnings-to-book and earnings-to-assets ratios; and momentum measures defined by stock price trends. Betas measure the co-movement of the returns of a strategy and predefined factor portfolios. We find that characteristics are better predictors of returns, whereas betas are better providers of information useful for managing factor risks.
The characteristics-based approach follows fundamental investment principles supported by empirical evidence that demonstrates factors provide persistent sources of return premia. The beta-based approach of measuring factor exposures follows the theoretical argument that these factor premia exist to compensate for exposure to factor risks. These two approaches, however, are not mutually exclusive. Because both characteristics and betas likely represent incomplete proxies for underlying factor exposures, each provides information complementary to the other.
Characteristics directly measure fundamental factor exposures and thus better predict future returns. Portfolios constructed using characteristics information create exposures to factor risks. By measuring return co-movements, betas provide investors information useful in the management of these factor risks.
To inform investors about the differences between fundamental characteristics and factor betas, we compare and contrast the historical returns and risks provided by factors constructed using characteristics and betas. We answer these practical questions: How have portfolios based on these two approaches to constructing factors performed? How should investors use both characteristics and beta information to build higher-performing portfolios?
We show that characteristics are better predictors of stock returns than betas are. Our findings demonstrate that this conclusion generally holds for a wide variety of factors (not only for the most commonly examined) and across international markets. Return predictions alone, however, provide incomplete information. Because investors also wish to measure and manage risks, we examine how the two approaches to factor construction compare in their ability to predict factor risks.
We demonstrate that investors’ objectives inform the practical use of characteristics and betas. An investor who faces few constraints—that is, an investor not bound by tracking error—may use both information sources to create portfolios that earn higher Sharpe ratios than those they could create from either set of factor information alone. Investors may buy or overweight firms with attractive characteristics to attain high returns, and then sell or underweight firms that have similar betas (but unattractive characteristics) to reduce risks. We explain how an investor mindful of tracking error can pursue the same idea, but do so in moderation, thus limiting factor beta exposure relative to a benchmark.