Value and Momentum Factors across Asset Classes

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.

Fundamental reason

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.

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Markets Traded
bonds, equities, REITs

Financial instruments
ETFs, funds, futures

Confidence in anomaly's validity

Backtest period from source paper

Notes to Confidence in Anomaly's Validity

Indicative Performance

Period of Rebalancing

Notes to Indicative Performance

per annum

Notes to Period of Rebalancing

Estimated Volatility

Number of Traded Instruments

Notes to Estimated Volatility

data from exhibit 4

Notes to Number of Traded Instruments

Maximum Drawdown

Complexity Evaluation
Simple strategy

Notes to Maximum drawdown

Notes to Complexity Evaluation

Sharpe Ratio

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 yied 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 un-biased 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.

Source paper
Blitz, Vliet: Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes
- Abstract

In this paper we examine global tactical asset allocation (GTAA) strategies across a broad range of asset classes. Contrary to market timing for single asset classes and tactical allocation across similar assets, this topic has received little attention in the existing literature. Our main finding is that momentum and value strategies applied to GTAA across twelve asset classes deliver statistically and economically significant abnormal returns. For a long top-quartile and short bottom-quartile portfolio based on a combination of momentum and value signals we find a return exceeding 9% per annum over the 1986-2007 period. Performance is stable over time, also present in an out-of-sample period and sufficiently high to overcome transaction costs in practice. The return cannot be explained by implicit beta exposures or the Fama French and Carhart hedge factors. We argue that financial markets may be macro inefficient due to insufficient ‘smart money’ being available to arbitrage mispricing effects away.

Other papers
Asness, Moskowitz, Pedersen: Value and Momentum Everywhere
- Abstract

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
- Abstract

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
- Abstract

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
- Abstract

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
- Abstract

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
- Abstract

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
- Abstract

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.

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