Investor Sentiment Index Based on Prospect Theory: Evidence from China (submitted)

We propose a novel methodology for deriving investor sentiment from market transaction data, through an approach that models the actual decision-making process of investors. Our sentiment index outperforms the traditional Baker-Wurgler index in predicting returns, with less susceptibility to macroeconomic shifts. It enhances predictive power by forecasting cash flows, discount rates, and market volatility. The sentiment indices retain the advantages of pre-synthesis variables, resulting in superior forecasting accuracy. Notably, our indices contain information from Baker-Wurgler’s indices, indicating that micro-structural market data encompasses macroeconomic signals.