RT Journal Article
SR Electronic
T1 Trending Fast and Slow
JF The Journal of Portfolio Management
FD Institutional Investor Journals
SP 103
OP 116
DO 10.3905/jpm.2021.1.312
VO 48
IS 3
A1 Eddie Cheng
A1 Nazar Kostyuchyk
A1 Wai Lee
A1 Pai Liu
A1 Chenfei Ma
YR 2022
UL https://pm-research.com/content/48/3/103.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.