A strong reputation is a cornerstone of corporate finance theory, widely believed to relax financial constraints and lower capital costs. We challenge this view by identifying an ‘reputation paradox’: under modern risk-sensitive regulation, for firms with long-term liabilities, a better reputation may paradoxically increase capital strain. We argue that the improvement of firm’s reputation alters customer behavior , , which extends liability duration and amplifies measured risk. By using the life insurance industry as an ideal laboratory, we develop an innovative framework that integrates LLMs with actuarial cash flow models, which confirms that the improved reputation increases regulatory capital demands. A comparative analysis across major regulatory regimes—C-ROSS, Solvency II, and RBC—and two insurance products, we further demonstrate that improvements in reputation affect capital requirements unevenly across product types and regulatory frameworks. Our findings challenge the conventional view that reputation uniformly alleviates capital pressure, emphasizing the necessity for insurers to strategically align reputation management with solvency planning.

This paper provides evidence that some seasoned equity offerings are motivated by public information. We test this channel in the supply chain setting, where supplier managers are more attentive than outside investors to customer news. We find that supplier firms are more likely to issue seasoned equity when their customer firms have negative earnings surprises. The results are mitigated when there is common scrutiny on the customer-supplier firm pairs by outside investors and analysts. Furthermore, long-run stock market performance appears to be worse for firms that issue seasoned equity following the negative earnings surprise of their customer firms.

This study examines the economic consequences of China’s Three-Red-Lines policy—introduced in 2021 to cap real estate developers’ leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance, with privately-owned developers disproportionately affected relative to state-owned firms. Using granular project-level data, we document significant drops in sales and a demand shift from private to state-owned developers. The policy also reduced local governments’ land sale revenues, prompting greater reliance on hidden local government financing vehicles for land purchases. The policy induced broad structural changes in China’s housing and land markets.

摘 要：随着互联网与电子贸易的发展，用户的个人隐私保护机制已成为当今社会的热点议 题之一。本文探讨《中华人民共和国个人信息保护法》实施对企业创新的影响。选取2021年《个 人信息保护法》的实施作为准自然实验以构建双重差分模型，并以机构投资者和分析师关注度为 中介变量构建机制分析。在《个人信息保护法》实施后，实验组比对照组的每年专利数量增量减 少了 12.4%。《个人信息保护法》的实施短期内对企业获职数据的能力造成限制，因此会抑制数 字化转型程度较高的企业的创新活动。此外，机制分析表明《个人信息保护法》通过抑制机构持 股比例和分析师关注度进而抑制企业创新，这一结论与异质性检验结果一致，即尽管长远看有助 于构建健康数字经济环境，该抑制效应短期内在国有高数字化转型水平企业及非四大审计的企业 中更为显著。

We study the influence of investor attention on mutual fund investors' fund selection and fund managers' portfolio choice. Using the Google Search Volume Index to measure investor attention on individual stocks, we find fund investors tend to direct more capital to mutual funds holding more high-attention stocks; fund managers tend to perform window-dressing trading to increase the portfolio holdings of high-attention stocks displayed to investors. Our results suggest that funds, particularly those with strong incentives, strategically trade on stock attention to attract investor flows. This strategic trading behaviour is also associated with fund underperformance and leads to larger non-fundamental volatility of holding stocks.

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.

We utilize the Bayesian model scan approach to examine the best performing models in a set of 15 factors discovered in the literature, plus principal components (PCs) of anomalies unexplained by the initial factors in the Chinese A-share market. The Bayesian comparison of approximately eight million models shows that HML, MOM, IA, EG, PEAD, SMB, VMG,PMO, plus the four PCs, PC1, PC6, PC7, PC8 are the best supported specification in terms of marginal likelihoods and posterior model probabilities. We also find that the best model outperforms existing factor models in terms of pricing tests and out-of-sample Sharpe ratio.

We investigate the economic impact of artificial intelligence (AI) adoption in the mutual fund industry by introducing a novel measure of AI adoption based on the presence of AI skilled personnel at fund management firms. We provide robust evidence that AI adoption enhances fund performance, primarily by improving risk management, increasing attentive capacity, and enabling faster information processing. Furthermore, we find that mutual funds with higher levels of AI adoption experience greater investor net flows and exhibit lower flow-performance sensitivity. While AI adoption benefits individual funds, we find no evidence of aggregate performance improvements at the industry level.

在我国资本市场中，政策作为宏观调控的重要工具，对行业资产价格具有显著影响。本文尝试将政策文本纳入金融文本分析框架，构建政策——行业相似度指标体系，识别政策支持导向，并探讨其在行业定价中的作用机制。文章构建了涵盖多层级政策的文本数据库，分别采用传统模型（LDA和LSA）与大语言模型（LLM）识别政策中的行业提及频次，测算政策——行业相似度指数，并结合行业收益数据构建策略。文章进一步引入支持向量回归（SVR）识别不同行业的最优政策滞后期，提升策略表现。实证结果表明：LLM模型在政策主题提取上明显优于传统方法，基于政策相似度构建的行业策略在多阶段均展现出稳健的超额收益，且政策对行业的影响有长期滞后效应，行业反应通常在政策发布半年后。考虑现实市场约束，基于最优滞后窗口构建的单边多头策略也表现优秀，具备良好实用性，特别是在政策密集期（如2015、2020年）表现突出。本文的研究为政策信号的量化研究与行业资产配置提供了新的方法与实证支持。

How do Held-to-Maturity (HTM) securities that limit the impacts of banks’ unrealized capital loss on the regulatory capital measures affect banks’ exposure to deposit run risks when policy rates increase? And how should regulators design policies on classifying securities as HTM jointly with bank capital regulation? To answer these questions, we develop a model of bank runs in which banks classify long-term assets as HTM or Asset-for-Sale (AFS). Banks trade off the current cost of issuing equity to meet the capital requirement when the interest rate increases against increasing future run risks when the interest rate increases further in the future. When banks underestimate interest rate risks or have limited liability to depositors in the event of default, capping held-to-maturity long-term assets and mandating more equity capital issuance may reduce the run risks of moderately capitalized banks. Using bank-quarter-level data from Call Reports, we provide empirical support for the model’s testable implications.