PT - JOURNAL ARTICLE AU - Eddie Cheng AU - Nazar Kostyuchyk AU - Wai Lee AU - Pai Liu AU - Chenfei Ma TI - Trending Fast and Slow AID - 10.3905/jpm.2021.1.312 DP - 2022 Jan 31 TA - The Journal of Portfolio Management PG - 103--116 VI - 48 IP - 3 4099 - https://pm-research.com/content/48/3/103.short 4100 - https://pm-research.com/content/48/3/103.full AB - This article develops a methodology to combine fast and slow time-series momentum signals using machine learning techniques based on market volatility. Starting with the US equity market, the authors find that the performance of a time-series momentum strategy is determined by both its responsiveness and the market volatility regime, among other factors. A decision tree gives a simple and insightful way to determine the threshold in characterizing low- and high-volatility regimes. A slow time-series momentum strategy tends to outperform a fast time-series momentum strategy when market volatility is low. The opposite tends to occur when volatility is high. This pattern of relative performance can be attributed to market-timing alpha and exists in most global equity markets, including both developed and emerging markets.