Similar to some better-known factors, such as size and value, time-series momentum historically has demonstrated above-average excess returns. Also called trend momentum, it is measured by a portfolio long assets that have had recent positive returns, and short assets that have had recent negative returns.
Time-series momentum differs from the traditional (cross-sectional) momentum factor, which considers an asset’s recent performance only relative to other assets. The academic evidence suggests that inclusion of a strategy targeting time-series momentum in a portfolio improves the portfolio’s risk-adjusted returns.
Strategies that attempt to capture the return premium offered by time-series momentum are often called “managed futures,” as they take long and short positions in assets via futures markets—ideally in a multitude of futures markets around the globe.
Today I’ll dive into the time-series momentum factor and examine some of its specific qualities, and those that make a managed futures strategy a good portfolio diversifier.
In general, an asset that has low correlation with broad stocks and bonds provides good diversification benefits. Low or near-zero correlation between two assets means there is no relationship in their performance: If Asset A performs above average, it doesn’t tell us anything about Asset B’s expected performance relative to its average.
The addition of a low-correlation asset to a portfolio will, depending on its specific return and volatility properties, improve risk-adjusted returns by increasing the portfolio’s return, reducing the portfolio’s volatility, or both.
Research From AQR
AQR Capital Management’s Brian Hurst, Yao Hua Ooi and Lasse Pedersen contribute to the literature on time-series momentum through their June 2017 paper, “A Century of Evidence on Trend-Following Investing”—an update of their 2014 study.
In it, the authors constructed an equal-weighted combination of one-month, three-month and 12-month time-series momentum strategies for 67 markets across four major asset classes (29 commodities, 11 equity indices, 15 bond markets and 12 currency pairs) from January 1880 to December 2016. The position these one-, three- and 12-month strategies take in each market is determined by assessing the past return in that market over the relevant look-back horizon.
A positive past excess return is considered an “up” trend and leads to a long position; a negative past excess return is considered a “down” trend and leads to a short position.
In addition, each position is sized to target the same amount of volatility, both to provide diversification and to limit portfolio risk from any one market. Positions across the three strategies are aggregated each month and scaled such that the combined portfolio has an annualized ex-ante volatility target of 10%.
Volatility scaling ensures the combined strategy targets a consistent amount of risk over time, regardless of the number of markets that are traded at each point in time. The authors’ results include implementation costs based on estimates of trading costs in the four asset classes. They further assumed management fees of 2% of asset value and 20% of profits, a traditional fee for hedge funds.