The momentum strategy buys assets with the strongest past return (12-month or 1-month) and expects them to outperform assets with the lowest past return. Value strategy buys assets that are fundamentally cheap and intends to gain on the assets’ reversion to their long-term means. The combined long-short strategy allows the investor to secure market-neutral exposure to gains from both anomalies.
Several different approaches to this basic strategy exist. We present the Blitz and Vliet strategy as an example, and more strategies are mentioned in the “Other papers” section.
Value and momentum strategies are very well documented by academics. These strong anomalies could be used together to enhance a portfolio’s profitability.
Using value and momentum on asset classes and not just inside one asset class can also increase strategy robustness.
Simple trading strategy
Create an investment universe containing investable asset classes (could be US large-cap, mid-cap stocks, US REITS, UK, Japan, Emerging market stocks, US treasuries, US Investment grade bonds, US high yield bonds, Germany bonds, Japan bonds, US cash) and find a good tracking vehicle for each asset class (best vehicles are ETFs or index funds). Momentum ranking is done on price series. Valuation ranking is done on adjusted yield measure for each asset class. E/P (Earning/Price) measure is used for stocks, and YTM (Yield-to-maturity) is used for bonds. US, Japan, and Germany treasury yield are adjusted by -1%, US investment-grade bonds are adjusted by -2%, US High yield bonds are adjusted by -6%, emerging markets equities are adjusted by -1%, and US REITs are adjusted by -2% to get unbiased structural yields for each asset class. Rank each asset class by 12-month momentum, 1-month momentum, and by valuation and weight all three strategies (25% weight to 12m momentum, 25% weight to 1-month momentum, 50% weight to value strategy). Go long top quartile portfolio and go short bottom quartile portfolio.
Asness, Moskowitz, Pedersen: Value and Momentum Everywhere
Value and momentum ubiquitously generate abnormal returns for individual stocks within several countries, across country equity indices, government bonds, currencies, and commodities. We study jointly the global returns to value and momentum and explore their common factor structure. We find that value (momentum) in one asset class is positively correlated with value (momentum) in other asset classes, and value and momentum are negatively correlated within and across asset classes. Liquidity risk is positively related to value and negatively to momentum, and its importance increases over time, particularly following the liquidity crisis of 1998. These patterns emerge from the power of examining value and momentum everywhere simultaneously and are not easily detectable when examining each asset class in isolation.
Wang: Applying Value and Momentum Across Asset Classes in a Quantitative Tactical Asset Allocation Framework
We present a concise quantitative method for combining value and momentum strategies in a tactical asset allocation framework by directly comparing the attractiveness of valuations across a broad range of asset classes. Our broad and diverse publicly traded asset classes include public equity, investment grade and high yield bonds, cash, Treasury Inflation Protected Securities (TIPS), commodity and real estate. We refine the basic yield approach to valuation by standardizing the value signal using the Z-score. By tactically adjusting the weight of each asset class based on its perceived value and momentum signals, our model shows significant improvement in overall portfolio performance.
Bhansali, Davis, Dorsten, Rennison: Carry and Trend in Lots of Places
Investors intuitively know two fundamental principles of investing: (1) Don’t fight the trend, (2) Don’t pay too much to hold an investment. But do these simple principles actually lead to superior returns? In this paper we report the results of an empirical study covering twenty major markets across four asset classes, and an extended sample period from 1960 to 2014. The results confirm overwhelmingly that having the trend and carry in your favor leads to significantly better returns, on both an absolute and a risk-adjusted basis. Furthermore, this finding appears remarkably robust across samples, including the period of rising interest rates from 1960 to 1982. In particular, we find that while carry predicts returns almost unconditionally, trend-following works far better when carry is in agreement. We believe that this simple two-style approach will continue to be an important insight for building superior investment portfolios.
Baz, Granger, Harvey, Le Roux, Rattray: Dissecting Investment Strategies in the Cross Section and Time Series
We contrast the time-series and cross-sectional performance of three popular investment strategies: carry, momentum and value. While considerable research has examined the performance of these strategies in either a directional or cross-asset settings, we offer some insights on the market conditions that favor the application of a particular setting.
Cooper, Mitrache, Priestley: A Global Macroeconomic Risk Explanation for Momentum and Value
Value and momentum returns and combinations of them are explained by their loadings on global macroeconomic risk factors across both countries and asset classes. These loadings describe why value and momentum have positive return premia and why they are negatively correlated. The global macroeconomic risk factor model also performs well in summarizing the cross section of various additional asset classes. The findings identify the source of the common variation in expected returns across asset classes and countries suggesting that markets are integrated.
Cherkezov, Lohre, Protchenko, Raol: Investing in a Multi-Asset Multi-Factor World
In this article, we advance the use of factor investing across multiple asset classes. It turns out that style factors well established in the equity domain – such as value, momentum or quality – do extend to other asset classes as well. Even more so, multi-asset multi-factors significantly expand the investment opportunity set relative to a traditional multi-asset universe. Seeking to exploit this potential, we put forward an innovative diversified risk parity strategy that is designed to strive for maximum diversification in the multi-asset multi-factor world. To illustrate the strategy’s merits, we investigate its stylized facts vis-à-vis more standard allocation approaches.
Ilmanen, Israel, Moskowitz, Thapar, Wang: Factor Premia and Factor Timing: A Century of Evidence
We examine four prominent factor premia – value, momentum, carry, and defensive – over a century from six asset classes. First, we verify their existence with a mass of out-of-sample evidence across time and asset markets. We find a 30% drop in estimated premia out of sample, which we show is more likely due to overfitting than informed trading. Second, probing for potential underlying sources of the premia, we find little reliable relation to macroeconomic risks, liquidity, sentiment, or crash risks, despite adding five decades of global economic events. Finally, we find significant time-variation in factor premia that are mildly predictable when imposing theoretical restrictions on timing models. However, significant profitability eludes a host of timing strategies once proper data lags and transactions costs are accounted for. The results offer support for time-varying risk premia models with important implications for theory seeking to explain the sources of factor returns.