Investor sentiment and optimizing traditional quantitative investments

Zheng Chen, Wenlin Li and Jia Huang

International Review of Economics & Finance, 2025, vol. 101, issue C

Abstract: Recent research highlights the interplay between investor sentiment and stock market dynamics. This study introduces an innovative approach to quantitative trading by integrating technical and fundamental analysis via textual data analysis and machine learning. Specifically, we propose a refined Moving Average Convergence Divergence (MACD) indicator by incorporating a customized investor sentiment trend factor. To assess the efficacy of this integrated methodology, we conducted an empirical analysis, focusing on the Shanghai Stock Exchange Composite Index, which has demonstrated notable short-term volatility over the past year. A rigorous comparative evaluation of trading strategies was undertaken, contrasting performance metrics before and after the integration of the sentiment-enhanced MACD. Our findings reveal that the strategies developed in this study yield substantial improvements in both profitability and the stability of quantitative stock market trading. By offering investors a novel and sophisticated approach to quantitative trading, this study contributes valuable insights and methodologies to the field of financial economics, with potential implications for both academic research and practical investment strategies.

Keywords: Unstructured data; Trend sentiment factor; Quantitative analysis; Risk-return analysis; MACD (search for similar items in EconPapers)
JEL-codes: C80 C88 G11 G40 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:101:y:2025:i:c:s1059056025003909

DOI: 10.1016/j.iref.2025.104227

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