TITLE:
Research on Stock Volatility Based on Investor Sentiment and Two-Dimensional Ising Model
AUTHORS:
Jiayi Zhang, Nianguo Mu
KEYWORDS:
Two-Dimensional Ising Model, Volatility, Econophysics, Behavioral Finance
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.5,
May
27,
2026
ABSTRACT: Integrating the two-dimensional Ising model with behavioral finance theory, this study uses investor sentiment to explain financial-market volatility. Specifically, spin flips in the Ising model are employed to simulate investors’ buy-sell switching behavior in financial markets, thereby constructing a quantifiable investor-sentiment variable, which is then embedded in a GARCH-X model to analyze stock-market volatility. The empirical results show that the price data simulated by the Ising model successfully reproduce typical statistical properties of financial markets, including sharp peaks, fat tails, volatility clustering, and long memory. Meanwhile, during periods of elevated investor sentiment, the fat-tail effect becomes significantly stronger. Compared with the benchmark GARCH model, the GARCH-X model that incorporates the sentiment variable exhibits a markedly better goodness of fit, confirming that investor sentiment has significant explanatory power for volatility. These findings indicate that investor sentiment drives changes in stock volatility and provide a new perspective for volatility research in financial markets.