Based on the DSSW model, we analyze the nonlinear impact mechanism of investor sentiment on stock return and volatility by adjusting its hypothesis in Chinese stock market. We examine the relationship between investor sentiment, stock return, and volatility by applying OLS regression and quantile regression. Our empirical results show that the effects of investor sentiment on stock market return are asymmetric. There is “Freedman effect” in Chinese stock market, but only optimistic sentiment has a significant nonlinear impact on stock market returns when the stock market is a balanced market or a bear market. Meanwhile, “create the space effect” does exist in Chinese stock market too. It only exists when the market is in equilibrium, and only pessimistic sentiment has the nonlinear effect on stock market volatility.

Measuring investor sentiment has become one of the most widely examined areas in behavioral finance. For the purpose of measuring investor sentiment more accurately, we classify the investor sentiment proxy into six market dimensions for the first time, and construct the investor sentiment monitoring index system. By using PCA method and excluding macroeconomic factors, we construct comprehensive investor sentiment index in Chinese stock market. Our results show this index as a good prediction ability to stock market.

We select five objective sentiment indicators and one subjective sentiment indicator to build investor sentiment composite index in Chinese stock market by using the partial least squares. The reason why we do that is to improve the shortcomings of the principal component analysis, which was adopted to build investor sentiment composite index in the pioneering research. Moreover, due to the large proportion of individual investors in Chinese stock market and the rapid change of investor sentiment, we innovatively use the weekly data with smaller information granularity and higher frequency. Through empirical tests for its reasonability and market’s predictive capability, we find that this index appears to fit the data better and improves prediction.