Forecasting Stock Market Volatility via Causal Reasoning

Abstract

Studies have shown that Internet financial news has become an important reference for investors in investment behavior. In order to simulate trading experiments that mimic the real stock market, this paper develops a stock volatility prediction model based on causal reasoning. It also gathers and cleans news and stock market data from the Internet, such as opening price, closing price, and change. The findings of the study indicate that the level of stock market volatility can be significantly influenced by online financial news. The proposed model can analyze the effects of news and stock market data in an explainable manner.

Keywords

• causal inference
• summary generation
• sentiment value calculation
• stock volatility prediction
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