Author(s)

Zhengrui Lv

Corresponding Author

Zhengrui Lv

Abstract

This study aims to investigate and optimize the quantitative stock selection strategy based on the multi-factor model and conduct empirical tests in the context of emerging markets. As emerging markets evolve and expand, traditional investment strategies encounter increased challenges and risks. Meanwhile, quantitative stock selection has gained investors' attention due to its scientific and systematic approach. This study begins by defining and constructing a multi-factor stock selection model with six basic factors: market, scale, value, momentum, quality, and volatility. It uses advanced statistical methods and machine learning techniques to tune the model parameters and optimizes the factor weights through genetic algorithms. This study backtracks historical data for backtesting and uses forward-looking out-of-sample tests to verify the stock selection effect of the model. The research indicates that the optimized multi-factor model can reliably generate positive excess returns in multiple emerging markets, taking into account transaction costs and liquidity constraints. This finding validates the model's applicability and reliability in a diversified market environment. By empirical tests, this study also reveals the differences in factor returns in emerging markets and provides a powerful reference for investors to formulate more accurate investment strategies. The findings of this research are significant for understanding investment opportunities in emerging markets and enhancing the theoretical application of quantitative investment models.

Keywords

Quantitative stock selection; Multi-factor model; Emerging markets; Model optimization