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.
The momentum effect is one of the most pervasive asset pricing anomalies documented in the ﬁnancial literature: Stocks with the highest returns over the past six to 12 months continue to deliver above-average returns in the subsequent period.
Mark Carhart, in his 1997 study “On Persistence in Mutual Fund Performance,” was the first to use cross-sectional (or relative) momentum, together with the three Fama-French factors (market beta, size and value), to explain mutual fund returns.
Initial research on cross-sectional 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.”
As my co-author Andrew Berkin and I show in our book, “Your Complete Guide to Factor-Based Investing,” the evidence supporting the momentum factor (both cross-sectional and time-series, or absolute, momentum) and premium is persistent across time, pervasive around the globe and across asset classes, robust to various definitions, and implementable. We also provide the well-documented behavioral explanations for the factor’s existence.
A Look At Fundamental Momentum
Dashan Huang, Huacheng Zhang and Guofu Zhou contribute to the literature on momentum with their March 2017 study “Twin Momentum.” Based on seven major fundamental variables (return on equity, return on assets, earnings per share, cash-based operating proﬁtability, accrual-based operating proﬁtability, gross proﬁtability and net payout ratio) and their moving averages, they constructed a measure of fundamental implied return (FIR) to capture the expectation of future stock returns.
Similar to price momentum, fundamental momentum is formed by buying stocks in the top FIR quintile and selling stocks in the bottom FIR quintile. And, as with price momentum, the formation period is the previous 12 months excluding the most recent month. The authors’ study covered the period April 1976 through September 2015.
Following is a summary of their findings:
- Fundamental momentum has strong forecasting power on future returns.
- Fundamental momentum earns an average return of 0.88% per month, comparable to price momentum (0.93% per month).
- Fundamental momentum delivers a monthly average return of 1.2%, 0.8% and 0.6% formed within the lowest, middle and highest past return quintile, respectively; price momentum earns a monthly average return of 1.6%, 0.9% and 1% formed within the lowest, middle and highest FIR quintile, respectively.
- Fundamental momentum and price momentum are complementary, with correlation of just 0.14.
- While profits from both types of momentum arrive at their maximum level 12 months after portfolio formation, price momentum profits subsequently revert, while fundamental profits do not.
- Fundamental momentum has a higher chance of generating positive returns. It has a positive skew of 0.7 versus the negative skew of price momentum (-1.2). Twin momentum basically neutralizes the negative skew risk of price-only momentum.
- Fundamental momentum is mainly driven by cash ﬂow shocks, whereas price momentum is driven by cash ﬂow shocks (reductions in expected earnings) and discount rate shocks (increases in the risk premium investors require).
- While research has demonstrated a decaying pattern for premiums, including for price momentum, there has been no decay in the fundamental momentum premium.
- Twin momentum had a negative exposure to market beta in each of the ﬁve-factor asset pricing models the authors tested and a correlation of -0.14 with market beta. Thus, twin momentum can serve as a hedge for the market portfolio.
- As a test of robustness, they found the results using fundamental momentum were basically unchanged when FIR was constructed based on three years instead of one year. And while weaker, the results were still positive at five years. In further tests, they also found similar results when adding more fundamental variables.
Twin Momentum Strategy
In addition, Huang, Zhang and Zhou constructed a twin momentum strategy that would simultaneously exploit both price information and fundamental information. The strategy takes long positions in stocks in the top past return and FIR quintiles and short positions in stocks that lie in the bottom past return and FIR quintiles.
They write: “The resulting twin momentum strategy earned an average return of 2.16% per month, more than doubling that of price momentum (0.93%) or fundamental momentum (0.88%). Moreover, its standard deviation is only 8.34%, compared favorably to that of price momentum (6.78%). Its monthly Sharpe ratio is 0.26, which is much higher than that of both price momentum (0.16) and fundamental momentum (0.14), as well as the market portfolio (0.13) in our sample period.”
The authors also found that their results could not be explained by any of the current (three-, four- or five-factor) asset pricing models. They write: “The twin momentum monthly alphas are always economically large and statistically signiﬁcant.” They also showed that the alphas came from both the long and short positions; thus, it’s unlikely explained by high arbitrage costs alone.
They did find that the turnover of twin momentum is very high—about 88% per month—comparable to that of price momentum. However, breakeven transaction costs (the costs high enough to offset the premium) are about 2.5%.
That is well above the costs that would be expected, especially with patient trading strategies. The transaction costs required to make the proﬁtability of twin momentum insigniﬁcant at the 5% level are 1.60%, again, higher than would be expected in live strategies. Thus, the returns cannot be explained solely by high transaction costs.
In addition, the strategy is not explained by the size effect—twin momentum works in all size groups.
For example, as the authors explain, “in the megacap group (excluding ﬁrms below the 80 percentile of all ﬁrms), twin momentum earns a monthly average return of 1.42% and its monthly alpha is at least 0.63%, signiﬁcant at the conventional level.”
Support For Fundamental Momentum
In his February 2015 NBER paper, “Fundamentally, Momentum Is Fundamental Momentum,” Robert Novy-Marx presents the evidence demonstrating that momentum in stock prices is not an independent anomaly. Instead, it’s driven by fundamental momentum. As Novy-Marx writes, it’s “a weak expression of earnings momentum, reflecting the tendency of stocks that have recently announced strong earnings to outperform, going forward, stocks that have recently announced weak earnings.” Following is a summary of Novy-Marx’s findings:
- Momentum in firm fundamentals, i.e., earnings momentum, explains the performance of strategies based on price momentum. It holds for both large and small stocks.
- Measures of earnings surprise subsume past performance in cross-sectional regressions of returns on firm characteristics, and the time-series performance of price momentum strategies is fully explained by their covariances (a measure of how much two random variables change together) with earnings momentum strategies. The data was statistically significant at the 5% level.
- Controlling for earnings surprises when constructing price momentum strategies significantly reduces their performance, without reducing their high volatilities.
- Controlling for past performance when constructing earnings momentum strategies reduces their volatilities and eliminates the crashes strongly associated with momentum of all types, without reducing the strategies’ high average returns.
- Earnings momentum subsumes even volatility-managed momentum strategies. Price momentum strategies that invest more aggressively when volatility is low have Sharpe ratios twice as large as the already high Sharpe ratios observed on their conventional counterparts.
Novy-Marx’s study provides support for the findings of Huang, Zhang and Zhou, and provides insights indicating that with equities, there could be a better way to exploit the anomaly than by using a price-only momentum strategy.
It’s worth noting that AQR Capital includes fundamental momentum in its multistyle funds, which incorporate momentum as one of its three factors (value and quality are the other two). The firm uses three measures of fundamental momentum: analyst revisions, earnings momentum and margin growth. However, unlike Huang, Zhang and Zhou’s twin momentum strategy, AQR overweights price momentum relative to fundamental momentum.
The preceding results demonstrate fundamental momentum is different from price momentum. It also exists not only in the past winner stocks, but also in the past loser stocks.
Huang, Zhang and Zhou concluded that “although fundamentals are shown to matter … it will be valuable to apply the twin momentum strategy to other markets, such as bond, commodity, and currency markets, to see whether the predictive power of fundamentals are understated relative to the traditional price momentum.” (Note that this would help to meet the pervasive requirement that Andrew Berkin and I established in our aforementioned book.)
Finally, they added that “it will be of interest to examine whether and how much a twin momentum factor, with a role similar to the popular price momentum factor, can explain various stock and mutual fund returns as well as existing anomalies.”
This commentary originally appeared August 4 on ETF.com
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