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Web Semantic Analysis of Investor Sentiment, Short Trading, and Stock Market Volatility

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  • Guobin Fang

    (Anhui University of Finance and Economics, China)

  • Xuehua Zhou

    (Anhui University of Finance and Economics, China)

Abstract

This paper develops a novel investor sentiment index using a MF-DFM based on ten years' worth of daily trading data of the CSI 300 index. By employing a TVP-SV-VAR model, it examines the interplay between investor sentiment, short trading, and stock market volatility. Furthermore, the study explores time-varying nonlinear effects under different market conditions. The findings reveal that investor sentiment heightens market volatility, while an increase in short trading volume tends to reduce it. The impact of investor sentiment on short trading volume is multifaceted and varies depending on the scenario. Further analysis suggests that investor sentiment and stock market volatility can reinforce each other in bull markets. In contrast, bear markets see investors strategically using securities lending for short trading and risk hedging, which indicates a substitution effect between the stock market and the securities lending market. This study has implications for promoting the healthy development of financial markets.

Suggested Citation

  • Guobin Fang & Xuehua Zhou, 2024. "Web Semantic Analysis of Investor Sentiment, Short Trading, and Stock Market Volatility," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-35, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-35
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    References listed on IDEAS

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