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  - 2021 Dec 07
TA  - The Journal of Portfolio Management
PG  - jpm.2021.1.312
4099  - https://pm-research.com/content/early/2021/12/07/jpm.2021.1.312.short
4100  - https://pm-research.com/content/early/2021/12/07/jpm.2021.1.312.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.