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.
Highlights
The Efficient Markets Hypothesis (EMH) is a theoretical cornerstone in modern financial economics (Zhang et al, 2016). Malkiel & Fama (1970) systematically elaborated EMH and divided markets into three types by the availability of information: strong-form EMH, semi-strong-form EMH and weak-form EMH
The results show that machine learning algorithms can effectively identify the relationship between factors and returns and improve the performance of the quantitative stock selection model
The value of an investment is derived from the present value of all the cash flows generated over the life of the investment, so an accurate judgment of the future value of the asset is the key to achieving excess investment returns
Summary
The Efficient Markets Hypothesis (EMH) is a theoretical cornerstone in modern financial economics (Zhang et al, 2016). Malkiel & Fama (1970) systematically elaborated EMH and divided markets into three types by the availability of information: strong-form EMH, semi-strong-form EMH and weak-form EMH. Quantitative investment model is an innovative form of financial technology that combines computer technology and securities price prediction It can improve asset management efficiency and investment performance. Quantitative stock selection models with machine learning algorithms are established based on the listed companies in China A-share market. The innovation points of this paper are as follows It enriches the empirical research on alpha returns in China A-share market and provides empirical support for the weak-form EMH. It combines the machine learning algorithms and the classical multi-factor model in quantitative stock selection, which improves the utilization efficiency of factor information and the performance of the investment model. The performance of 16 machine learning algorithms in the quantitative stock selection models is compared, which enriches the academic research in the new composite field
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