Knowledge that Transforms

This study examines the role of non-severe depression as a psychological anchor against overoptimism. Using earnings forecasts from Estimize, we find that an increase in the proportion of the U.S. population with depression is associated with improved forecast accuracy among users. This effect is concentrated among forecasts that are optimistic and analysts who take longer time to issue forecasts, highlighting reduced optimism and slow information processing as economic mechanisms that explain our results. We also show that this effect is distinct from the influence of temporary seasonal depression or other sentiment measures on decision-making. Overall, our research establishes a link between depression and crowdsourced financial evaluations

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

Life insurers sell savings contracts with surrender options, which allow policyholders to prematurely receive guaranteed surrender values. These surrender options move toward the money when interest rates rise. Hence, higher interest rates raise surrender rates, as we document empirically by exploiting plausibly exogenous variation in monetary policy. Using a calibrated model, we examine the impact of surrender options on insurers’ liquidity and portfolio rebalancing during an interest rate rise. We show how asset sales result from insurer balance sheet dynamics and explore their interaction with investment strategies and surrender value guarantees.

Consider a defaultable bond traded in a financial market that is subject to shocks and regime shifts. Its recovery payment has a hybrid structure, comprising two components: one contingent on historical information up to the time of default, and the other an independent variable indexed by the regime at the time of default. The default intensity, interest rate, and reference rate are assumed to be general deterministic functions of certain state variables, while these state variables jointly follow a jump-diffusion process, with drift and volatility coefficients governed by the regime and with jumps induced by shocks. We construct a risk-neutral pricing measure that prices all risk sources in an integrated manner. A rigorous verification of this pricing measure reveals the corresponding time-dependent market prices of these risk sources. The resulting pricing framework is applicable to most defaultable bonds and credit derivatives.

We provide compelling evidence of the association between credit standards at loan origination in the corporate sector and default risk. While this relationship has been extensively studied in the mortgage market, it remains underexplored in corporate lending. Using a comprehensive dataset from the Spanish credit register merged with firm-level balance sheet information over a full financial cycle, we show that debt-to-assets and interest coverage ratios at loan origination are significant predictors of future corporate loan defaults. The strength of this association varies across the financial cycle, sectors, firm size and age, and prior banking relationships. Real estate firms and small and medium-sized enterprises exhibit the strongest link between initial credit conditions and future default outcomes. Our findings suggest that limits in corporate credit standards, similar to those widely used in mortgage markets, could enhance firms’ resilience to adverse shocks and complement capital-based instruments within the macroprudential toolkit. However, the effectiveness and potential side effects of such measures depend critically on firm-specific characteristics and sectoral heterogeneity, underscoring the need for a targeted and flexible policy design.

We examine the causal effect of tariff uncertainty on firms’ cost of debt. Our tests exploit a unique trade policy that reduces tariff uncertainty on Chinese imports without affecting the actual tariff rate, United States (U.S.)–China permanent normal trade relations (PNTR). We reveal a significant drop in the loan spreads for firms affected by PNTR relative to other firms. We further demonstrate that such effects occur through the channel of increasing firms’ performance predictability. Overall, by examining a clean measure of uncertainty from the tariff source, we provide evidence that reducing uncertainty has a causal effect on reducing the cost of debt.

We study the impact of bank board structure as a signaling mechanism in loan syndication. We find that the quality of the lead arranger’s board, and specifically the monitoring quality of the board, has a positive effect on the ability to syndicate a loan. The impact of board quality is separate and distinct from lead arranger reputation. Board monitoring quality plays a more important role when the borrower and lead arranger have no prior relationship, after the bankruptcy of an existing borrower, and during the financial crisis. We posit that one channel for our findings is through board oversight of the CEO, and we provide evidence that CEO quality partially mediates the relationship between board quality and loan syndication. Overall, we conclude the quality of the lead arranger’s board, as a separate and distinct effect to reputation, serves as a credible signal to participant banks, mitigating moral hazard and adverse selection concerns.

Using machine learning and many predictors, we find strong bond return predictability, with an out-of-sample R-squared of 4.48% and an annualized Sharpe ratio of 3.27. ML models identify important predictors for aggregate predictors (bond market returns, TERM and HML factors, GDP growth) and bond characteristics (downside risk, short-term reversal, return skewness, and credit spreads). Predictability varies over time, being stronger during periods of high investor risk aversion, slow economic growth, and strong cross-sectional factor explanatory power. Our results highlight the benefits of leveraging both cross-sectional and time-series predictors to forecast corporate bond returns while considering public and private bonds.

We examine the impact of board gender diversity on equity-based compensation in executive pay. We show that the presence of female directors is associated with lower equity-based compensation in executive pay—particularly when female directors serve on compensation committees or when boards are dominated by insiders. Our findings support the view that female directors serve as effective monitors, potentially substituting for other corporate governance mechanisms, such as incentive-based pay and board independence. Our results are not driven by lower pay-for-performance sensitivity induced by poor stock performance. We do not find evidence consistent with risk-aversion on the part of female directors.

This paper introduces the tone-overlay framework for adjusting traditional commodity signals based on the level of salient optimism or pessimism in commodity newswires. By implementing the novel tone-overlay allocation strategy on 26 commodities using traditional allocation signals, we demonstrate that the resulting long-short portfolios yield substantial performance gains compared to the corresponding plain-vanilla traditional portfolios. Our findings suggest that newswire tone provides short-term predictive power for commodity futures returns, beyond well-known commodity characteristics. The tone-overlay portfolios harness a temporary mispricing that reflects an overreaction of commodity futures prices to commodity-specific newswire tone. The outperformance of the tone overlay strengthens with the salience of the newswire tone, consistent with theories of limited investor attention.