所属栏目:资本市场/市场有效性

Risk-Based Peer Networks and Return Predictability: Evidence from textual analysis on 10-K filings
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发布日期:2025年07月07日 上次修订日期:2025年07月07日

摘要

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 negative-return 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.
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张一鸣; 袁见 Risk-Based Peer Networks and Return Predictability: Evidence from textual analysis on 10-K filings (2025年07月07日) https://www.cfrn.com.cn/lw/16292.html

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