Empirical Research About Quantitative Stock Picking Based on Machine Learning
Zheng Zhongbin, Fang Jinwu
Available Online 20 December 2019.
- https://doi.org/10.2991/aebmr.k.191217.026How to use a DOI?
- Machine learning, Random Forest, XGBoost, Multifactor stock selection
- This study mainly uses artificial intelligence and machine learning technology to build stock selection models to help investors choose stocks reasonably. In this paper, six machine learning models were constructed for comparison and backtesting based on the framework of the machine learning stock selection. By comparing the model classification accuracy, AUC, and other index, XGBoost and Random Forest were selected, and the portfolio was constructed. According to the analysis, the portfolio could obtain an above-average rate of return, and the portfolio obtained a net value of about 1.5 times that of the benchmark portfolio during the two-year investment test period.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zheng Zhongbin AU - Fang Jinwu PY - 2019 DA - 2019/12/20 TI - Empirical Research About Quantitative Stock Picking Based on Machine Learning BT - Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019) PB - Atlantis Press SP - 138 EP - 141 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.191217.026 DO - https://doi.org/10.2991/aebmr.k.191217.026 ID - Zhongbin2019 ER -