We examine how societal secrecy affects the underpricing of initial public offerings (IPOs). Using a large sample of 18,304 IPOs across 38 countries, we find robust evidence that IPO underpricing is positively related to societal secrecy. Additional analyses reveal that investor protection, market openness, and third-party certification moderate the effect of societal secrecy on IPO underpricing. We find that societal secrecy influences IPO underpricing through the information asymmetry, demand for control, and information cascade channels. Collectively, we show that societal secrecy exerts a strong influence on IPO underpricing globally.

Abstract Hedge fund positions are an important component of crowded trades. These vehicles are particularly active, take highly concentrated positions, and utilize leverage and short sales. Using a database of hedge fund holdings, we measure the degree of security-level crowdedness. The difference between the average returns on portfolios sorted by high versus low crowdedness portfolios is sizable, and the variation in the realized portfolio returns is distinct from other traditional risk factors. Further, hedge fund exposures to crowdedness are often significant, and they help to explain downside “tail risk,” as funds with higher exposures experience relatively larger drawdowns during periods of industry distress. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Abstract The role of underwriters is altered in new seasoned equity offering deal types in which the offering follows quickly after its announcement. Controlling for the endogenous matching between issuing firms and underwriters, we find increased underwriter reputation mitigates the immediate price impact of announcing an accelerated bookbuilt offering, exacerbates the price impact of announcing a bought offering, and has no immediate price impact for fully marketed deals. In contrast, underwriter reputation positively affects price outcomes for fully marketed deals around the offer date. Reputation effects are not apparent in the absence of controlling for the endogenous matching.

This study examines whether the availability of traded credit default swaps (CDS) influences the referenced firms' incentive to smooth their performance. We show that with the introduction of CDS trading on their debt, CDS-referenced firms (CDS firms) lower both their earnings and cash flow volatility. Specifically, earnings volatility declines faster than cash flow volatility, which is consistent with income smoothing behavior. The effect of CDS trading on performance smoothing is qualitatively similar under different market and economic conditions. These results support the notion that CDS firms smooth their performance to avoid renegotiation with CDS-protected creditors. We also find that CDS firms smooth their cash flows via hedging with derivatives and smooth their earnings using discretionary accruals after the inception of CDS trading.

ABSTRACT Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion”—the tendency to discount computer‐based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm's AI system (instead of a human specialist) propose smaller adjustments to management's complex estimates, particularly when management develops their estimates using relatively objective (vs. subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.

Abstract We develop a Bayesian latent factor model of the joint long-run evolution of GDP per capita for 113 countries over the 118 years from 1900 to 2017. We find considerable heterogeneity in rates of convergence, including rates for some countries that are so slow that they might not converge (or diverge) in century-long samples, and a sparse correlation pattern (“convergence clubs”) between countries. The joint Bayesian structure allows us to compute a joint predictive distribution for the output paths of these countries over the next 100 years. This predictive distribution can be used for simulations requiring projections into the deep future, such as estimating the costs of climate change. The model's pooling of information across countries results in tighter prediction intervals than are achieved using univariate information sets. Still, even using more than a century of data on many countries, the 100-year growth paths exhibit very wide uncertainty.

We develop a theory of how corporate lending and financial intermediation change based on the fundamentals of the firm and its environment. We focus on the interaction between the prospective net worth or liquidity of an industry and the firm’s internal governance or pledgeability. Variations in prospective liquidity can induce changes in the nature, covenants, and quantity of loans that are made, the identity of the lender, and the extent to which the lender is leveraged. We offer predictions on how these might vary over the financial cycle.

Using novel credit data, we show that hedge fund borrowing is significantly overcollateralized, primarily with rehypothecable securities. An idiosyncratic liquidity shock to a major prime broker significantly decreases credit to connected hedge funds. The dominant channel behind this shock transmission is credit supply reduction rather than precautionary demand reduction. Funds posting more rehypothecable collateral are less affected because their collateral alleviates prime broker liquidity constraints. Exposed funds subsequently have lower aggregate credit with worse terms, suggesting imperfect substitutability across hedge fund credit sources. Funds subject to the decrease in balance sheet leverage subsequently increase portfolio illiquidity, embedded leverage, and derivatives exposure.

Abstract We document racial disparities in auto lending. Combining credit bureau records with borrower characteristics, we find that Black and Hispanic applicants’ approval rates are 1.5 percentage points lower, even after controlling for creditworthiness. In aggregate, this effect crowds out 80,000 minority loans each year. Results are stronger where racial biases are more prevalent and lending competition is lower. Minority borrowers pay 70-basis-point higher interest rates, but default less ceteris paribus, consistent with racial bias rather than statistical discrimination. A major antidiscrimination enforcement policy initiated in 2013, but halted in 2018, reduced unexplained racial differences in interest rates by 60%. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Using unique data, this paper examines investment banks’ choice of peers in comparable companies analysis in mergers and acquisitions. We find strong evidence that product market space is amongst the most important factors in peer selection, but Standard Industrial Classification (SIC) codes, particularly three and four digit codes, do a poor job of categorizing related firms in this setting. Banks strategically select large, high growth peers with high valuation multiples, factors that are also positively related to premiums. Our evidence is consistent with target-firm advisors selecting peers with high valuation multiples to negotiate higher takeover prices.