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- W4387643896 endingPage "102178" @default.
- W4387643896 startingPage "102178" @default.
- W4387643896 abstract "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." @default.
- W4387643896 created "2023-10-15" @default.
- W4387643896 creator A5050575272 @default.
- W4387643896 creator A5053377049 @default.
- W4387643896 creator A5059804355 @default.
- W4387643896 creator A5080264414 @default.
- W4387643896 date "2023-10-01" @default.
- W4387643896 modified "2023-10-15" @default.
- W4387643896 title "Momentum in machine learning: Evidence from the Taiwan stock market" @default.
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