Knowledge that Transforms

We examine whether managerial cognitive heuristics spill over from personal to corporate decisions. We identify “anchoring CEOs” who anchor on the 52-week high in personal stock trading and show that this behavior extends to corporate financial decisions. These CEOs are more likely to issue seasoned equity offerings near the 52-week high and place greater weight on the target’s 52-week high in acquisition pricing, with the latter associated with negative abnormal returns. The effect is stronger under competitive pressure and uncertainty but weaker with stronger governance and CEO experience. Our findings highlight cross-domain persistence in managerial heuristics and the role of governance in mitigating behavioral distortions.

We study a macroprudential regulation in the emerging market of Chile that raised loan-loss provisions for residential mortgages with loan-to-value (LTV) ratios above 80%. The policy reduced high-LTV borrowing and overall leverage but unintentionally affected households likely to borrow above 80% LTV based on preregulation characteristics. These borrowers liquidated term deposits to meet higher down payments, lowering liquidity and raising short-term delinquency, especially near the threshold. The results uncover a regulatory trade-off: systemic risk is curbed, but financially constrained households face short-term vulnerability.

In July 2013, Moody’s unexpectedly increased the amount of equity credit speculative-grade firms receive for preferred stock from 50% to 100%. Firms affected by the rule change were suddenly considered less levered by Moody’s, even though their balance sheets did not change. These firms responded by issuing debt to restore the original leverage ratio as defined by Moody’s and growing their assets. The rule change transferred value from debt to equity holders and led to an increase in preferred stock issuance. How rating agencies assess risk thus has a significant causal impact on firms’ financing, investment, and security design decisions.

This paper reviews firm-level data on artificial intelligence (AI) and the emerging evidence on AI’s economic effects. It argues that measurement is central: different AI data sets capture different objects, including invention versus use; internal capability building versus outsourcing; and realized activity versus investor perceptions, and can therefore lead to different conclusions. The paper develops a framework for choosing among these measures and surveys available data sources on firm AI efforts. It synthesizes evidence on AI’s effects on firm growth, valuation, productivity, risk, labor, competition, financial markets, and applications. The paper concludes by suggesting some ideas for future research.

Regulators condition bank capital on risk but struggle to measure risk accurately. Capital requirements thus rely on inputs from banks’ internal risk models, and banks have discretion over modeling choices. Using novel hand-collected data we show that reported bank risk varies systematically with simulation method, holding period, and historical data size. Hence, modeling choices can be a significant channel of underreporting of risk. Consistent with this presumption we find that less-capitalized banks tend to choose less conservative methods. Moreover, banks using a softer simulation method display higher actual market risk, while reporting lower market risk to regulators.

We investigate how households adjust their shopping behavior in response to financial shocks. Our results show that households visit stores more frequently but buy fewer products and spend less in financial downturns. When money is tight, they switch to cheaper products, use more coupons, and reduce bulk purchases. During financially stressful times, households shrink the set of stores that they visit, mainly by discarding high-end stores, and varying the stores within the same set. Our store-level analysis shows that heightened financial stress in the area is associated with a decline in sales of all products, but much less so for cheaper products. Our results underscore the pivotal role of product markets in facilitating consumption smoothing.

We study investor disagreement within conversations rather than across independent messages. Using Reddit’s r/wallstreetbets (WSB), we classify the disagreement of each comment toward its parent using a large language model and aggregate the signed disagreement scores at the ticker-day level by conversational depth. Disagreement does not diminish as discussions deepen, and later conversational stages can remain highly polarized. Contrary to the predictions of common prior models, successive rounds of discussion do not drive investors toward consensus. Relevance to market outcomes, however, concentrates in first-round replies. Depth-1 conversational disagreement predicts retail trading volume most robustly in meme stocks from the 2021 retail-trading episode, with effects persisting from the day of the post through subsequent trading days. In the broader cross-section of stocks discussed on WSB, the same association appears contemporaneously but weakens at longer horizons. Discussions that are deeper in the thread are uninformative about trading outcomes.

While firms manage their capital structure to target credit ratings, how targeting impacts capital structure decisions is not well understood. We hypothesize that firms engage in ratings cushioning by preserving a leverage buffer against rating downgrades. We show that ratings cushions are sizable in magnitude. Following plausibly exogenous increases in cushion, firms increase leverage to consume their newfound cushion particularly when they have attractive investment opportunities and are less exposed to earnings shocks. These findings suggest that ratings cushioning restrains firms from pursuing otherwise more aggressive capital structure and investment choices.

As nonbanks’ market share increases in a local residential mortgage market, the quality of their postorigination mortgage servicing improves. This finding is confirmed by two instrumental variable analyses exploiting (1) stress tests conducted by the Federal Reserve, and (2) mortgage industry surety bonds required by each state. Evidence suggests that improvements in service quality arise through two channels. First, expansion in nonbank market share leads to greater lender specialization: nonbanks increasingly service lower-income borrowers, while traditional banks increasingly concentrate on higher-income segments. Second, higher nonbank market share is associated with increased investment in technology by nonbanks. (JEL G21, G23, L13, L15)Received: May 22, 2025

Delegating high-stakes decisions creates a fundamental tension: incentivizing experts to acquire unobservable information inevitably distorts their final choices. In a principal-agent setting, we characterize the optimal compensation contract under hidden learning, showing it endogenously generates either contrarian or conformist bias. The direction of this bias depends on learning costs and the precision of public and private information. Our framework links information acquisition incentives to systematic biases in experts’ choices and offers a unifying explanation for conflicting empirical evidence in financial advice: why analysts issue excessive contrarian recommendations, and why inexperienced analysts follow the consensus more than their experienced peers.