Risk-based peer networks and return predictability: Evidence from textual analysis on 10-K filings

Abstract

We construct a novel risk-based similarity peer network by applying machine learning techniques to extract a comprehensive set of disclosed risk factors from firms’ annual reports. We find that a firm’s future returns can be significantly predicted by the past returns of its risk-similar peers, even after excluding firms within the same industry. A long–short portfolio, formed based on the returns of these risk-similar peers, generates an alpha of 84 basis points per month. This return predictability is particularly pronounced for small stocks and those with limited investor attention, suggesting that the effect is driven by slow information diffusion across firms with similar risk exposures. Our findings highlight that the risk factors disclosed in 10-K filings contain valuable information that is often overlooked by investors.