This paper builds a dynamic trade-off model of corporate financing with differences in belief between the insider manager and outside investors. The optimal leverage depends on differences of opinion and can differ significantly from that in standard trade-off models. The manager's market timing behavior leads to several stylized facts, such as the low average debt ratios of firms in the cross section, the substantial presence of zero-debt firms that pay larger dividends and keep higher cash balances than other firms, and negative long-run abnormal returns following stock issuance. Market timing behavior leads to substantial losses of firm value through excessive financing activities. Market timing and debt conservatism depend negatively on shareholder control of the firm.

We present the puzzling evidence that, from 1962 to 2009, an average 10.2% of large public nonfinancial US firms have zero debt and almost 22% have less than 5% book leverage ratio. Zero-leverage behavior is a persistent phenomenon. Dividend-paying zero-leverage firms pay substantially higher dividends, are more profitable, pay higher taxes, issue less equity, and have higher cash balances than control firms chosen by industry and size. Firms with higher Chief Executive Officer (CEO) ownership and longer CEO tenure are more likely to have zero debt, especially if boards are smaller and less independent. Family firms are also more likely to be zero-levered.

Using a novel measure of investment companies’ reliance on generative artificial intelligence (GenAI), we document a sharp increase in GenAI usage by hedge funds after ChatGPT’s 2022 launch. A difference-in-differences test shows that hedge funds adopting GenAI earn 2-4% higher annualized abnormal returns than nonadopters, while non-hedge funds do not benefit. The outperformance originates from funds’ AI talent and ChatGPT’s strength in analyzing firm-specific information. We conduct a new survey of fund managers’ GenAI usage to provide direct validation of our measure and offer additional new insights on how managers adopt GenAI tools in their practice.

We provide large-scale evidence on the occurrence and value of FinTech innovation. Using data on patent filings from 2003 to 2017, we apply machine learning to identify and classify innovations by their underlying technologies. We find that most FinTech innovations yield substantial value to innovators, with blockchain being particularly valuable. For the overall financial sector, internet of things (IoT), robo-advising, and blockchain are the most valuable innovation types. Innovations affect financial industries more negatively when they involve disruptive technologies from nonfinancial startups, but market leaders that invest heavily in their own innovation can avoid much of the negative value effect.ReceivedMay 31, 2017; editorial decision September 30, 2018 by Editor Andrew Karolyi.

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.

We examine whether firms that imitate peer companies’ strategies (copycats) profit from such behavior and how their success may cause competitive harm to disclosing companies. We identify copycat companies by tracking the digital footprints of investment companies that view disclosures on the SEC EDGAR Web site. We find that copycat companies are able to identify profitable trades that outperform other trades disclosed by the copycatted companies by 5.5% annually. Such stock‐screening skills are related to investment sophistication and research intensity. Furthermore, copycats inflict greater damage on the performance of disclosing companies when they possess superior copycat skills, when disclosed trading strategies take longer to complete, and when disclosed stock holdings are characterized by high information asymmetry.

An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win ‘‘Man vs. Machine’’ when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in ‘‘Man + Machine’’, which also substantially reduces extreme errors. Analysts catch up with machines after ‘‘alternative data’’ become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.

This paper studies the “confidential holdings” of institutional investors, especially hedge funds, where the quarter‐end equity holdings are disclosed with a delay through amendments to Form 13F and are usually excluded from the standard databases. Funds managing large risky portfolios with nonconventional strategies seek confidentiality more frequently. Stocks in these holdings are disproportionately associated with information‐sensitive events or share characteristics indicating greater information asymmetry. Confidential holdings exhibit superior performance up to 12 months, and tend to take longer to build. Together the evidence supports private information and the associated price impact as the dominant motives for confidentiality.

We examine the impact of mandatory portfolio disclosure by mutual funds on stock liquidity and fund performance. We develop a model of informed trading with disclosure and test its predictions using the May 2004 SEC regulation requiring more frequent disclosure. Stocks with higher fund ownership, especially those held by more informed funds or subject to greater information asymmetry, experience larger increases in liquidity after the regulation change. More informed funds, especially those holding stocks with greater information asymmetry, experience greater performance deterioration after the regulation change. Overall, mandatory disclosure improves stock liquidity but imposes costs on informed investors.