Machine Learning-Based Research on Anomalies in China's Stock Market
- Title
- Machine Learning-Based Research on Anomalies in China's Stock Market
- Other Titles
- 머신러닝을 기반으로 중국의 주식 시장 이상현상에 관한 연구
- Author
- 유건봉
- Alternative Author(s)
- LIU JIANFENG
- Advisor(s)
- 강형구
- Issue Date
- 2023. 8
- Publisher
- 한양대학교
- Degree
- Master
- Abstract
- This paper systematically uses machine learning algorithms to assess the predictability of Chinese stock returns while analyzing a comprehensive set of anomalies. Based on the anomaly literature, 108 anomalies are constructed for the A-share market from 2000 to 2021, and eight machine learning algorithms, including ordinary least squares, lasso regression, ridge regression, elastic network regression, principal component regression, partial least squares regression, extreme gradient boosting tree, and feedforward neural network, are used to construct stock return prediction models and portfolios. Compared with previous studies on the US market, the Chinese stock market has higher predictability, with an out-of-sample R2 of 1.48% and a maximum monthly return of 2.79% for long-short portfolios. However, the machine learning models are found to be sensitive to the quality of the data and must be adequately trained and tuned to overcome the high dimensionality and avoid overfitting. This paper further examines the importance of factors in the prediction model and finds that the liquidity factor and risk factor have strong predictive power in the A-share market.
- URI
- http://hanyang.dcollection.net/common/orgView/200000684220https://repository.hanyang.ac.kr/handle/20.500.11754/187216
- Appears in Collections:
- GRADUATE SCHOOL[S](대학원) > BUSINESS ADMINISTRATION(경영학과) > Theses (Master)
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML