Momentum Versus Factor Momentum: Which Dominates?
New research shows that investors can profit by exploiting “momentum” – the notion that stocks or factors that experienced good performance will continue to do so, and vice versa.
The momentum effect is one of the most persistent, pervasive and robust anomalies documented. In our book, Your Complete Guide to Factor-Based Investing, Andrew Berkin and I presented the empirical evidence of a momentum premium across stocks, bonds, commodities and currencies. Recently, factor momentum has received much attention from researchers.
The empirical research on factor momentum, including the 2018 studies, “Factor Momentum Everywhere,” and “Is there Momentum in Factor Premia? Evidence from International Equity Markets”; the 2019 studies, “Factor Momentum and the Momentum Factor,” and “Factor Momentum”; the 2021 study, “Is Factor Momentum More than Stock Momentum?”; the 2022 studies, “Momentum-Managed Equity Factors,” and “Factor Momentum in the Chinese Stock Market,” have examined whether momentum can be found in factors as well, and found:
- Time-series (trend) factor momentum has been a pervasive property of factors – a strategy that buys the recent top-performing factors and sells poor-performing factors achieved significant investment performance above and beyond traditional stock momentum.
- Factor momentum explained all forms of individual stock momentum – stock momentum strategies indirectly timed factors; they profited when the factors remained autocorrelated and crashed when those autocorrelations broke down.
- Demonstrating pervasiveness, factor momentum has been a global phenomenon.
- Factor momentum could have been captured by trading almost any set of factors.
- Industry momentum stemmed from factor momentum.
- The value-added induced by factor management via short-term momentum was a robust empirical phenomenon that survived transaction costs and carried over to multifactor portfolios – while managing factors based on last month’s momentum increased turnover, the increase in turnover induced by timing did not outweigh the benefits. In addition, turnover could have been reduced using a smoothed version of the timing signal, and timing still would have yielded significant benefits.
These findings led Nusret Cakici, Christian Fieberg, Daniel Metko and Adam Zaremba, authors of the November 2022 study, “Factor Momentum Versus Stock Price Momentum: A Revisit,” to further examine the global evidence to determine whether stock momentum subsumed factor momentum or vice versa. They considered two versions of factor momentum: in empirical anomalies and in principal components (PC), a statistical technique for reducing the dimensionality of a dataset, accomplished by linearly transforming the data into a new coordinate system where most of the variation in the data can be described with fewer dimensions than the initial data. Their data sample included up to 95 years of returns (though coverage of international markets began no earlier than February 1983) on 145 anomalies across 51 countries. Following is a summary of their findings:
- There was a powerful factor momentum effect in the U.S. market.
- Momentum in anomalies was not limited to the United States, as the “winner” factors outperformed their “loser” factors’ counterparts in multiple countries regardless of the implementation details.
- However, the factor momentum could not explain stock momentum internationally – in either its empirical or PC form. The five-factor (market, size, value, profitability and investment) model of Fama and French extended with the empirical factor momentum captured no more than 19% to 44% of the price momentum profits – the majority of the returns remained unexplained.
- Although PC analysis rendered stock momentum in the U.S. insignificant, internationally, stock price momentum better explained factor momentum than vice versa.
Their findings led the authors to conclude that internationally, the momentum effect remains an essential and distinct anomaly, which cannot be captured by simply timing other factors.
Despite this long history, some have questioned the usefulness of momentum in practical money management because its higher turnover could lead to excessive transaction costs. Using nearly $1 trillion of live trading data from a large institutional money manager across 19 developed equity markets over the period from 1998 to 2011, Andrea Frazzini, Ronen Israel, and Tobias Moskowitz, authors of the paper “Trading Costs of Asset Pricing Anomalies,” measured the real-world transaction costs facing an arbitrageur and applied them to the momentum strategy. Following is a summary of their findings:
- Actual trading costs are low enough to allow for the potential scale of these strategies to be much larger than previous studies would indicate. The reason is that prior studies computed trading costs for average investors, which are about 10 times higher than the costs estimated for a large arbitrageur using more sophisticated strategies, such as algorithmic trading programs.
- Strategies designed to reduce transaction costs can substantially increase net returns and capacity, without incurring significant style drift. The institutional money manager in the study had been running long-only momentum indices since July 2009. The actual realized price impact costs in these funds had been just 8.0, 18.2, and 5.9 basis points in large-cap, small-cap, and international momentum funds, respectively. This is in line with, but slightly lower than, the estimates from historical trading data. Based on the data, the authors estimated that capacity for long/short strategies of size, value, and momentum are $103 billion, $83 billion, and $52 billion among U.S. securities, respectively, and $156 billion, $190 billion, and $89 billion, respectively, for global securities.
Frazzini, Israel, and Moskowitz concluded that these strategies are robust, implementable, and sizeable. In addition, there are three strategies investors can use to reduce trading costs. The first strategy calls for limiting trading to stocks with low expected transaction costs. The second is to reduce rebalancing frequencies (at the expense of some staleness in the signals on which the strategies are based). This technique is popular among large institutional money managers. For example, the AQR Momentum indices from AQR Capital Management LLC, which are designed to track the momentum strategy with limited trading costs, are rebalanced quarterly instead of monthly. The third strategy is to lower turnover by introducing a buy/hold range, holding stocks that would no longer be bought. This strategy has long been used by Dimensional Fund Advisors (among others) for its small-cap funds and is also used now in MSCI indices. Robert Novy-Marx and Mihail Velikov, authors of the study “A Taxonomy of Anomalies and Their Trading Costs” found that well-designed momentum strategies do survive transactions costs.
There is empirical evidence demonstrating that factor momentum informs the cross-section of returns and has generated alpha relative to existing asset pricing models. While the evidence is strongest in the U.S., momentum in factors has also existed internationally (though factor momentum might not subsume momentum in international markets). The evidence has led firms like Alpha Architect and AQR Capital Management, leaders in factor investing, to incorporate factor momentum into some of their strategies. For example, AQR recently added cross-sectional stock market factor momentum to its general managed-futures strategy. Individual investors can utilize this information without incurring additional costs by incorporating factor momentum into trading decisions. For example, when rebalancing, they can delay purchases of assets with negative factor momentum and delay sales of assets with positive factor momentum.
Larry Swedroe is head of financial and economic research for Buckingham Wealth Partners.
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