We explore the effects of fundamental extrapolation on stock returns. Empirically, we propose a novel approach to extrapolate firms' fundamental information and find that a strategy based on fundamental extrapolation earns an average return of 0.80% per month. Theoretically, we show that...

We propose a unsupervised learning approach to construct latent factor model for cross sectional asset returns where firm characteristics instrument for the dynamic factor exposures. Firm characteristics are clustered with consideration to their prior economic content. Our method can also be...

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%,...

We propose an employee sentiment index, which complements investor sentiment and manager sentiment indices, and find that high employee sentiment predicts a subsequent low market return, significant both in- and out-of-sample. The predictability of the employee sentiment index can also deliver...