A New Investor Sentiment Index Model and Its Application in Stock Price Prediction and Systematic Risk Estimation of Bull and Bear Market

Abstract

Many studies in recent years have shown that investor sentiment affects investor decision-making, which in turn affects stock market volatility and the direction of stock market prices. Since behavioral finance researchers find that linear combinations of stock turnover and popularity indices can greatly reflect stock investor sentiment, this paper aims to construct a new investor sentiment index that can be reasonably applied to predict stock market risk by selecting rational factors. A new investor sentiment index model is first proposed by combining specific monthly new account ratio (SNIA), monthly turnover rate (TOR), popularity index AR, delayed yield (DY) and using principal component analysis approach. Secondly, the indicator is statistically tested. The results of the correlation analysis show that the investor sentiment index is positively correlated with the monthly rate of return, and the result of causal analysis reveals that the investor sentiment index is the Granger cause of the change in yield. Thirdly, a new method is designed to predict the stock price trend by using the presented investor sentiment index. Finally, based on VaR and CoVaR model the investor sentiment index can be utilized to forecast and estimate of systematic risk in the bull or bear market.