TITLE:
Can Machine Learning Unlock the Continuous Alpha? Empirical Study Based on China A-Share Market
AUTHORS:
Ya Lin, Rendao Ye
KEYWORDS:
Quantitative Investment, Efficient Market Hypothesis, Machine Learning, Alpha Return
JOURNAL NAME:
Open Journal of Business and Management,
Vol.9 No.5,
September
16,
2021
ABSTRACT: With the development of fintech and artificial intelligence, machine
learning algorithms are widely used in quantitative investment. Based on the
listed companies in China A-share market from February 2005 to July 2020,
quantitative stock selection models with machine learning algorithms are
established to obtain continuous alpha returns. The results show that machine
learning algorithms can effectively identify the relationship between factors
and returns and then improve the performance of the quantitative stock
selection model. China A-share market is a weak-form efficient market. By
mining the factors that are not fully digested by the market, continuous alpha
returns can be obtained. The ensemble algorithms represented by the extremely
randomized tree (ET) and light gradient boosting machine (LGBM) perform best in
stock market prediction.