Swedroe: Momentum In Factors

May 31, 2019

Momentum is the tendency for assets that have performed well (poorly) in the recent past to continue to perform well (poorly) in the future, at least for a short period of time.

In 1997, Mark Carhart, in his study “On Persistence in Mutual Fund Performance,” was the first to use momentum, together with the three Fama–French factors (market beta, size and value), to explain mutual fund returns.

Initial research on momentum was published by Narasimhan Jegadeesh and Sheridan Titman, authors of the 1993 study “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.”

Momentum is most commonly defined as the last 12 months of returns excluding the most recent month (in other words, months two through 12 of the past year). The most recent month is excluded, as it tends to show a reversal, which some have attributed to microstructure (trading) effects.

The momentum factor is the average return of the top 30% of stocks minus the average return of the bottom 30% as ranked by this measure. The momentum factor is referred to as UMD, or up minus down. Note that UMD is a relative (cross-sectional) measure of momentum, unlike trend-following (time series) momentum, which is an absolute measure.

Both types of momentum have been found to be persistent across time and economic regimes; pervasive around the globe and across stocks, industries, bonds, commodities and currencies; robust to various definitions; and have well-documented behavioral explanations for why they have persisted even after publication. And with the use of patient trading strategies, they survive transaction costs.

Factor Momentum Study

Tarun Gupta and Bryan Kelly contribute to the literature on momentum with their November 2018 paper “Factor Momentum Everywhere.”

They built and analyzed a large collection of 65 characteristic-based factors that are widely studied in the academic literature, including a variety of valuation ratios (e.g., earnings/price, book/market); factor exposures (e.g., betting against beta); size, investment and profitability metrics (e.g., market equity, sales growth, return on equity); idiosyncratic risk measures (e.g., stock volatility and skewness); and liquidity measures (e.g., Amihud illiquidity, share volume and bid-ask spread).

Following is a summary of their findings:

  • Individual factors exhibit robust time series momentum, being positive for 59 of the 65 factors, and significantly positive in 49 cases.
  • Robust momentum behavior among the common factors is responsible for a large fraction of the covariation among stocks.
  • A portfolio strategy that buys the recent top-performing factors and sells poor-performing factors achieves significant investment performance above and beyond traditional stock momentum.
  • On a stand-alone basis, factor momentum outperforms stock momentum, industry momentum, value and other commonly studied investment factors in terms of Sharpe ratio.
  • While factor momentum and stock momentum are correlated, they are also complementary—factor momentum earns an economically large and statistically significant alpha after controlling for stock momentum and expenses.
  • Demonstrating pervasiveness, factor momentum is a global phenomenon—it manifests equally strongly outside the U.S.—in a large global (ex. U.S.) sample, and Europe and Pacific region subsamples.

Gupta and Kelly further found that because of stock momentum’s especially strong hedging benefit with respect to value, there is a significant benefit to combining factor momentum, stock momentum and value in the same portfolio.

A combined strategy that averages one-month time series momentum of all factors earns an annual Sharpe ratio of 0.84, exceeding the performance of any individual factor’s time series momentum. In tests of robustness, it performs similarly well with longer formation windows. For example, the strategy’s Sharpe ratio is 0.70 when based on previous 12-month factor performance and remains at 0.72 with a five-year look-back window.

Gupta and Kelly also studied the performance of cross-sectional momentum. They found that cross-sectional (CS) momentum and time series (TS) momentum have similar behavior. The Sharpe ratios of CS momentum are slightly inferior to TS momentum, and CS momentum has slightly smaller alphas with respect to the equal-weighted portfolio of raw factors, but their performance patterns are otherwise closely aligned.

Gupta and Kelly concluded: “Our findings of momentum among equity factors support the conclusion that factor momentum is a pervasive phenomenon in financial markets.”

Summary

Given the risks of data mining, before investing in a factor, one should be confident there is sufficient evidence to support the belief that the premium found in the historical record is likely to be predictive of future results.

To gain that confidence, you should have evidence that the premium was statistically significant and unique/independent as well as that it showed persistence, pervasiveness, robustness, implementability (survive transactions costs) and has risk-based or behavioral explanations for why it should be expected to continue.

The study by Gupta and Kelly provides evidence of persistence, pervasiveness, robustness and implementability. The authors also showed that momentum is not subsumed by other factors—it’s a unique factor that adds diversification benefits.

Thus, their findings should provide investors greater confidence that increasing exposure to momentum (both cross-sectional and time-series) is likely to improve portfolio efficiency.

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 130 independent registered investment advisors throughout the country.

Find your next ETF

CLEAR FILTER