Value and Momentum Are a Powerful Combo


Two of the most powerful explanatory factors in finance are value and momentum. Research on both has been published for more than 20 years. However, it was not until recently that the two have been studied in combination and across markets.

The study “Value and Momentum Everywhere” by Clifford Asness, Tobias Moskowitz and Lasse Pedersen, which appeared in the June 2013 issue of The Journal of Finance, examined the value and momentum factors across eight markets and asset classes (individual stocks in the United States, the U.K., continental Europe and Japan, as well as country equity index futures, government bonds, currencies and commodity futures). Following is a summary of their findings:

  • There are significant return premiums to value and momentum in every asset class. The value premium was persistent in every stock market, with the strongest performance in Japan. The momentum premium was also positive in every market, especially in Europe, although statistically insignificant in Japan.

  • Value strategies are positively correlated with other value strategies across otherwise-unrelated markets. Momentum strategies are positively correlated with other momentum strategies globally. This persistence assuages data-mining concerns.

  • Value and momentum are negatively correlated with each other within and across asset classes. The negative correlation between value and momentum within each asset class is consistent and averages -0.49. For stocks alone, the correlation averaged -0.60. Value and momentum’s negative correlation and high positive expected returns implies that a simple combination of the two is much closer to the efficient frontier than either strategy alone. Combining value and momentum strategies results in improved Sharpe ratios.

  • There’s significant evidence that liquidity risk is negatively related to value and positively related to momentum globally across asset classes. The implication in this case is that part of the negative correlation between value and momentum is driven by opposite-signed exposure to liquidity risk. However, liquidity risk can only explain a small fraction of the value and momentum return premiums and co-movement.

The authors offered this explanation for why momentum loads positively on liquidity risk and value loads negatively: “A simple and natural story might be that momentum represents the most popular trades, as investors chase returns and flock to the assets whose prices appreciated most recently. Value, on the other hand, represents a contrarian view. When a liquidity shock occurs, investors engaged in liquidating sell-offs (due to cash needs and risk management) will put more price pressure on the most popular and crowded trades, such as high momentum securities, as everyone runs for the exit at the same time, while the less crowded contrarian/value trades will be less affected.”

More Evidence

We now have a second paper examining the two factors together. Victor Haghani and Richard Dewey, authors of “A Case Study for Using Value and Momentum at the Asset Class Level,” which appears in the Spring 2016 issue of The Journal of Portfolio Management, also found that combining the value and momentum factors can offer both higher expected returns and lower risk than when they are used independently.

The authors explain that the benefit comes primarily from value and momentum’s tendency to operate over different time horizons. They write: “The negative correlation arises from value investing’s reliance on reversion to fair value (i.e., negative autocorrelation), while momentum investing is predicated on divergence from the mean (i.e., positive autocorrelation). Often, momentum acts as a check on value, discouraging an investor from buying before a bottom or selling before a peak.”

Their study covered the period 1975 through 2013, and included data from the following 12 asset classes: U.S. equities, U.K. equities, Europe ex-U.K. equities, Japan equities, Pacific ex-Japan equities, Canada equities, emerging market equities, U.S. REITs, commodities (as represented by the GCSI), U.S. nominal Treasurys, U.S. investment-grade credit and 90-day Treasury bills. Importantly, all the preceding asset classes are liquid and can be accessed with low-cost vehicles. Unlike much research, the authors built long-only a portfolio, which forced them to choose “fair value” centering points for their valuation signal.

They note: “Intuitively, these centering points should be thought of as asset valuations that provide fair compensation for bearing the risk associated with a specific asset class.” At the end of each month, Haghani and Dewey derive valuation signals for each asset class. If the signal was above (below) that centering point, signaling undervaluation (overvaluation), they increased (decreased) the allocation relative to its baseline weight in the subsequent month, and vice versa.

The authors explained: “There is no consensus in the literature or by practitioners on the ideal metric or level for measuring valuation in each asset class. In deriving our centering points, we attempted to balance common sense practitioner metrics with the findings in the asset-pricing literature. In an effort to reduce bias, we tried to select these centering points ex-ante and did not change or optimize them at any point in the research.”

Different Portfolio Construction Approaches
Haghani and Dewey constructed four portfolios at the end of each month: baseline (no adjustment), value, momentum and value plus momentum. Rather than give equal weight to each asset class, they followed approximately a 65%/35% (equities/bonds) portfolio construction method. They also constructed a portfolio that gave equal weight to each asset class: 20% equities, 20% real estate, 20% commodities, 20% bonds and 20% T-bills.

The following represents a basic summary of their construction methodology. For their baseline portfolio, they rebalanced at the end of each month. For valuation measures, they scaled exposure by the asset’s valuation, giving higher weight to the cheaper assets. And they increased an asset’s weight by half if the momentum signal was positive, and decreased the weight by half if it was negative (then repeated the process monthly).

Following is a summary of the authors’ findings:

  • Valuation-based scaling of asset allocations produces a return that exceeds a static-weight portfolio by 0.86% per annum.

  • Momentum-based scaling of asset allocations produces a return that exceeds a static-weight portfolio by 1.55% per annum, almost twice as large as the benefit from scaling value.

  • The combination of these two portfolio adjustments produces a return that exceeds the static-weight portfolio by 2.66% per annum.

  • The dynamically scaled portfolios also produce higher Sharpe ratios and reduce risk as measured by maximum drawdowns.

  • There was no asset class for which value and momentum scaling together diminished returns.

  • The outperformance was particularly high in bear markets, at 6.51% per annum.

  • Correlations between value and momentum were negative in every case, with the exception of Japanese equities, demonstrating that value and momentum are complementary in portfolios.

Additional Observations
Haghani and Dewey observed that their portfolios didn’t employ leverage (explicitly or implicitly through the use of futures or derivatives); didn’t take short positions; and didn’t allow for the significant concentration of risk in a small subset of the asset classes. This makes their results very relevant for the practitioner, who often faces similar restrictions.

They also noted that when they extended their study back to 1926, using a more limited set of assets, the results were consistent. And finally, while their study ignored transaction costs, turnover was not high enough to significantly impact the results, especially since, as mentioned above, all the investments they examined can be made in low-cost, highly liquid vehicles.

The annual turnover for the baseline portfolio was roughly 15%, arising from the monthly rebalancing back to fixed weights. Turnover for the baseline-plus-value portfolio was roughly 70%, and turnover for the baseline-plus-value-plus-momentum portfolio was just more than 100% per annum.

Haghani and Dewey concluded: “Using simple measures of valuation and momentum to dynamically adjust asset allocation has historically produced superior investment returns compared to a more static investment strategy…. We find that a strategy employing value and momentum together provides higher quality returns than using either value or momentum alone. This can be attributed to negative correlation and the general complementary nature of value and momentum.”

Increasingly, we see mutual funds, such as those from AQR and Bridgeway, combining the two factors in one fund strategy. (Full disclosure: My firm, Buckingham, recommends AQR and Bridgeway funds in constructing client portfolios.)

This commentary originally appeared July 18 on

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