In this paper, we apply the BERT model, a cut-edging deep learning model, to construct a novel textual sentiment index in the Chinese stock market. By introducing the market returns as sentiment labels, our BERT model successfully extracts useful sentiment-related information contained in asset...

We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices both in- and out-of-sample, and the...

This paper constructs a manager sentiment index based on the aggregated textual tone of corporate financial disclosures. We find that manager sentiment is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R2 of 9.75% and 8.38%,...

Based on 10 commonly used non-momentum factors, we construct a novel factor momentum strategy and find that it earns an annualized return of 9.91% and a Sharpe ratio of 1.15 in the Chinese stock market, which lacks stock-level momentum. We also find that factor momentum has strong explanatory...