Trend Following and Momentum Strategies › Summary

Trend following and momentum strategies exploit the tendency of asset prices to exhibit persistence, buying recent winners and selling recent losers. This chapter covered the two main variants of momentum investing and their practical implementation:

Time-series momentum compares an asset's current price to its own history, generating long or short signals based on the direction of recent returns. Moving average crossovers and breakout systems are common implementations. CTA strategies apply time-series momentum across diversified portfolios of futures contracts, benefiting from low correlation to traditional assets and the ability to profit in both rising and falling markets.

Cross-sectional momentum ranks assets relative to their peers, going long the top performers and short the bottom performers. The Jegadeesh-Titman framework established that buying 6- to 12-month winners and selling losers generates significant excess returns across equity markets, time periods, and asset classes.

Behavioral explanations for momentum include investor underreaction to new information, herding behavior, and positive feedback trading. Risk-based explanations suggest momentum returns compensate for crash risk and other systematic exposures. The truth likely involves elements of both.

Momentum crashes represent the strategy's most severe risk, with sudden reversals capable of erasing years of profits in weeks. Volatility scaling, diversification, and dynamic hedging can mitigate but not eliminate this risk. Strategy crowding presents an additional concern as momentum becomes more widely adopted.

Combining time-series and cross-sectional momentum reduces dependence on either approach alone. In the next chapter, we will see how momentum combines with other factors such as value, quality, and low volatility in systematic long-short equity strategies.