Momentum trading is a strategy that exploits the tendency for assets that have performed well recently to continue performing well, and for assets that have performed poorly to continue performing poorly. This persistence of returns, documented across asset classes, geographies, and time periods, is one of the most robust findings in empirical finance. Momentum strategies buy recent winners and sell (or short) recent losers, profiting from the continuation of existing trends.
The evidence for momentum
Academic research has documented the momentum effect extensively since Jegadeesh and Titman's seminal 1993 paper. Stocks that have outperformed over the past 3-12 months tend to continue outperforming over the next 3-12 months. This pattern has been found in equities, bonds, currencies, commodities, and other asset classes. It persists across international markets and has been present in data going back over a century.
The effect is strongest at intermediate time horizons (3-12 months). At very short horizons (days to weeks), returns tend to reverse (mean revert). At very long horizons (3-5 years), returns also tend to reverse. The intermediate-term persistence is the window that momentum strategies target.
How momentum strategies work
A typical cross-sectional momentum strategy ranks all stocks in a universe by their returns over a lookback period (commonly 6-12 months, excluding the most recent month). The strategy buys the top decile (recent winners) and shorts the bottom decile (recent losers). Portfolios are typically rebalanced monthly.
Time-series momentum (also called trend-following) looks at each asset individually. If an asset's return over the lookback period is positive, the strategy goes long. If negative, the strategy goes short or stays flat. This approach can be applied to futures and other instruments where both long and short positions are equally accessible.
Risks and drawdowns
Momentum strategies are susceptible to momentum crashes, sudden reversals where recent losers surge and recent winners plunge. These crashes tend to occur during market transitions, such as the recovery from a bear market. The March 2009 momentum crash saw the long-short momentum factor lose over 40% in a matter of weeks as beaten-down financial stocks surged while recent winners in defensive sectors declined.
Understanding these crash dynamics is essential for risk management. Position sizing, diversification across asset classes, and drawdown-triggered de-risking can mitigate the impact of momentum crashes.
Practical example
A momentum strategy applied to the S&P 500 ranks all stocks by their 12-month return, excluding the most recent month. Each month, it buys the top 50 stocks and equally weights them. Over a 20-year backtest, the strategy returns 14% annually versus 10% for the S&P 500, but with a maximum drawdown of 35% during a momentum crash. The Sharpe ratio is 0.8, suggesting the excess return comes with meaningful additional risk.
How Tektii helps
Tektii enables traders to backtest momentum strategies with realistic execution modeling, which is particularly important because momentum portfolios are rebalanced frequently and can involve large position changes. The platform's support for portfolio-level backtesting, transaction cost modeling, and multi-instrument analysis makes it well-suited for evaluating whether a momentum strategy's theoretical edge survives real-world execution costs. Version trees allow traders to iterate on lookback periods, weighting schemes, and risk controls to find the most robust momentum implementation.