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Forecasting Korean Stock Returns with Machine Learning

Noh, Hohsuk ; Jang, Hyuna ; Yang, Cheol‐Won

Asia-Pacific Journal of Financial Studies, 2023, 52(2), , pp.193-241 [Periódico revisado por pares]

Richmond: 한국증권학회

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  • Título:
    Forecasting Korean Stock Returns with Machine Learning
  • Autor: Noh, Hohsuk ; Jang, Hyuna ; Yang, Cheol‐Won
  • Assuntos: Emerging markets ; Gradient boosting machine ; Liquidity ; Machine learning ; Neural network ; Portfolio performance ; Random forest ; Securities markets ; Stock returns ; Variable importance ; Variables ; 경영학
  • É parte de: Asia-Pacific Journal of Financial Studies, 2023, 52(2), , pp.193-241
  • Notas: This work is supported by the IREC research fund of the Institute of Banking and Finance from Seoul National University in 2020. We are grateful to Hyoung Gu Kang (discussant) and the participant of 2021 IREC seminar for their helpful comments.
    https://onlinelibrary.wiley.com/doi/10.1111/ajfs.12419
  • Descrição: This paper aims to evaluate the predictive power of financial variables by using various machine learning methods. An analysis is conducted on data for the Korean stock market, which is representative of emerging markets, over 32 years from 1987 to 2018. The study shows that median regression is  a more efficient tool than mean regression in the presence of potential heterogeneity of stocks, significantly improving performance in terms of average realized monthly return. This suggests that median regression can have better predictive performance in emerging markets where there are likely to be outliers. Additionally, a gradient boosting machine (GBM) is found to be better than a traditional linear model both in prediction accuracy and portfolio performance. The hedged return from GBM is on average 2.89% per month with an annualized Sharpe ratio of 0.93 in the median regression. The neural network (NN) is also tested and shown to perform best when the number of hidden layers is two or three. Finally, we evaluatea list of predictor variables with various measures of variable importance. Variables of risk, price trend and liquidity are found to serve as important predictors.
  • Editor: Richmond: 한국증권학회
  • Idioma: Coreano;Inglês
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