TY - JOUR T1 - Trending Fast and Slow JF - The Journal of Portfolio Management SP - 103 LP - 116 DO - 10.3905/jpm.2021.1.312 VL - 48 IS - 3 AU - Eddie Cheng AU - Nazar Kostyuchyk AU - Wai Lee AU - Pai Liu AU - Chenfei Ma Y1 - 2022/01/31 UR - https://pm-research.com/content/48/3/103.abstract N2 - 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. ER -