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This paper documents the puzzling evidence that a substantial number of large public non-financial US firms follow a zero-debt policy. Over the 1962-2009 period, on average 10.2% of such firms have zero debt and almost 22% have less than 5% book leverage ratio. Neither industry nor size can account for such puzzling behavior. Zero-leverage behavior is a persistent phenomenon, with 30% of zero-debt firms refrain from debt for at least five consecutive years. Particularly surprising is the presence of a large number of zero-leverage firms who pay dividends. They are more profitable, pay higher taxes, issue less equity, and have higher cash balances than their proxies chosen by industry and size. These firms also pay substantially higher dividends than their proxies and thus their total payout ratio is virtually independent of leverage. Firms with higher CEO ownership and longer CEO tenure are more likely to follow a zero-leverage policy, especially if boards are smaller and less independent. Family firms are also more likely to be zero-levered. Our results suggest that managerial and governance characteristics are related to the zero-leverage phenomena in an important way.

Zonder onderwerpscode: economie

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An AI analyst we build to digest corporate financial information, qualitative disclosure and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analyst. In the contest of "man vs machine," the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI's computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of "machine plus human" (instead of human displacement) in high-skill professions.

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We create a firm-level ChatGPT investment score, based on conference calls, that measures managers' anticipated changes in capital expenditures. We validate the score with interpretable textual content and its strong correlation with CFO survey responses. The investment score predicts future capital expenditure for up to nine quarters, controlling for Tobin's q and other determinants, implying the investment score provides incremental information about firms' future investment opportunities. The investment score also separately forecasts future total, intangible, and R&D investments. High-investment-score firms experience significant negative future abnormal returns. We demonstrate ChatGPT's applicability to measure other policies, such as dividends and employment.