A Study on the Application of Regression Analysis in Real-World Data: A Case of Stock Return Rates in China

Abstract

This study examines how regression methods can be used to predict stock return rates in China’s financial markets, which are characterized by rapid growth, regulatory complexities, and high retail investor participation. Traditional models like the Capital Asset Pricing Model (CAPM), which focus solely on market risk, often fail to capture the full picture of return drivers in this environment. In contrast, multi-factor models that incorporate firm-specific characteristics such as size, value, volatility, and trading volume provide a more comprehensive understanding of stock performance. Furthermore, adaptive regression approaches that update predictor variables dynamically over time demonstrate enhanced predictive accuracy by reflecting shifts in market conditions. Visual analyses underscore the limited scope of CAPM and the greater effectiveness of multi-factor models tailored to China’s market structure. The findings suggest that regression models incorporating behavioral and market-specific variables, alongside flexible adaptive techniques, offer more reliable insights into stock returns. These results hold significant implications for investors, portfolio managers, regulators, and researchers focused on emerging market dynamics.