Momentum in machine learning: Evidence from the Taiwan stock market

Dien Giau Bui*, De Rong Kong, Chih Yung Lin, Tse Chun Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We revisit 86 asset pricing anomalies in the Taiwan stock market and find that long-short portfolio strategies based on machine-learning methods bring substantial benefits. For example, neural networks and partial least squares generate long-short returns ranging from 1.20% to 1.50% per month. More importantly, five of the top 20 influential return predictors are momentum-related variables. This result provides novel evidence to the momentum literature given that the Taiwan stock market is one of the few exceptions to the momentum anomaly. In contrast with this conventional view, we show that momentum contributes to stock return predictability when adopting machine-learning models.

Original languageEnglish
Article number102178
JournalPacific Basin Finance Journal
StateAccepted/In press - 2023


  • Asset pricing anomalies
  • Machine learning
  • Momentum
  • Stock return predictability
  • Variable importance


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