Can Machine Learning Unlock the Continuous Alpha? Empirical Study Based on China A-Share Market

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.

Fuzzy Decision-Making Analysis of Quantitative Stock Selection in VR Industry Based on Random Forest Model

“…In addition, more studies on quantitative stock selection models have used classifiers such as GRU neural network models or integrated tree models [12][13][14], and the most comprehensive one is the Stacking method, which combines the abovementioned neural networks, gradient boosting trees, and XGBoost to form a new algorithmic model, RGXB-Stacking stock selection model, and the research results show that this model has significantly better back-testing effect on constituent stock data than other models [15][16][17]. Based on the above analysis of existing research, it is found that most of the research on quantitative stock selection nowadays is only at the level of optimizing the selected impact factors, and there is no good solution for the relationship between a large number of impact factors and the expected return of the model [18]. Therefore, the study extracts the random forest model algorithm from the Stacking method and conducts an empirical analysis to verify the role of the random forest model in quantitative stock selection by taking the VR industry [19][20][21][22], which is developing rapidly in today's society, as the research object [23][24][25].…”

Analysis of the Effectiveness of Alpha Strategy for Optimized Multi-Factor Models

“…He found that combining the Alpha strategy with quantitative stock selection and hedging the spot stock portfolio with stock index futures can achieve higher returns [9]. Li also verified the effectiveness of the Alpha strategy in the A-share market through a multi-factor model [10]. Ji analyzed the sources of returns and reasons for drawdowns in the Alpha strategy.…”