A Python-based Empirical Study of Markowitz's Portfolio Theory in the Chinese Stock Market

Abstract

Markowitz's portfolio theory serves as a pivotal framework in investment science, offering investors a scientifically robust approach to asset allocation. This paper conducts an empirical analysis of the theory within the context of the Chinese stock market, utilising Python for data manipulation and computation. A selection of five stocks was made based on pre-established criteria, and Python was employed to examine their correlation for portfolio construction viability. Subsequently, Python facilitated the identification of the portfolio with the maximum Sharpe ratio and the minimum variance, as well as the delineation of the efficient frontier. Results from the empirical study confirm the applicability and utility of Markowitz's portfolio theory for Chinese investors, thereby substantiating its significant relevance in China's financial market. Based on the findings, investors are advised to diversify their portfolios following the guidelines of the efficient frontier to optimize returns while managing risks. Moreover, considering the complexities of the Chinese stock market, harnessing computational tools like Python can grant an analytical edge, enabling more informed decision-making. It's also pivotal for investors to routinely review and adjust portfolio compositions in line with market dynamics, ensuring they stay aligned with the principles of the efficient frontier.