RT Journal Article SR Electronic T1 Trending Fast and Slow JF The Journal of Portfolio Management FD Institutional Investor Journals SP jpm.2021.1.312 DO 10.3905/jpm.2021.1.312 A1 Eddie Cheng A1 Nazar Kostyuchyk A1 Wai Lee A1 Pai Liu A1 Chenfei Ma YR 2021 UL https://pm-research.com/content/early/2021/12/07/jpm.2021.1.312.abstract 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.