AMethod for Analyzing Stock Price Volatility Based on Market Sentiment

Abstract: In recent years, the Chinese stock market has exhibited frequent abnor mal fluctuations due to major capital operations and shifts in international economic policies, posing significant challenges to investors’ decision-making. This paper intro duces a method for analyzing stock price volatility grounded in system dynamics principles. By constructing a large-scale dataset of Chinese stock commentary, we developed a precise sentiment analysis model tailored to Chinese stock reviews and introduced a novel public opinion influence coefficient model, significantly enhancing the correlation between stock price volatility and public sentiment data. In the realm of natural language processing, our approach represents a key advancement in under standing the impact of market sentiment on stock market behavior, offering robust theoretical and empirical support. Additionally, to promptly detect abnormal stock price fluctuations, this paper proposes a warning mechanism that integrates engineering statistics methods, culminating in a comprehensive abnormal fluctuation warning model. This approach delivers a system atic solution encompassing ”data processing, prediction, warning, and analysis,” and empirical testing has confirmed its accuracy and reliability in practical applications. This research successfully bridges theoretical insights with practical implementation, offering new perspectives and methodologies for stock price volatility analysis.