Leading financial scholars present essays examining the performance of the basic financial functions underlying global financial systems: payments, lending and investing, pooling funds, allocating risk, providing information, and dealing with incentive issues - with particular emphasis on how their performance is changing and implications for the future.
Participating in insurgency is physically risky. Why do people do so? Using new data on 3,799 payments to insurgent fighters by Al Qa'ida Iraq, we find that: (i) wages were extremely low relative to outside options, even compared to unskilled labor; (ii) the estimated risk premium is negative; and (iii) the wage schedule favors equalization and provides additional compensation for larger families. These results challenge the notion that fighters are paid their marginal product, or the opportunity cost of their time. They may be consistent with a “lemons” model in which fighters signal commitment by accepting low wages.
ABSTRACT This study examines the incremental predictive power of aggregate measures of financial misreporting for recession and real gross domestic product (GDP) growth. We draw on prior research suggesting that misreporting has real economic effects because it represents misinformation on which firms base their investment, hiring, and production decisions. We find that aggregate M-Score incrementally predicts recessions at forecast horizons of five to eight quarters ahead. We also find that aggregate M-Score is significantly associated with lower future growth in real GDP, real investment, consumption, and industrial production. Additionally, our result that aggregate M-Score predicts lower real investment one to four quarters ahead partially accounts for why misreporting predicts recessions five to eight quarters ahead. Our findings are weaker when we use aggregate F-Score as a proxy for misreporting. Overall, this study provides novel evidence that aggregate misreporting measures can aid forecasters and regulators in predicting recessions and real GDP growth. JEL Classifications: M41.
The Accounting Review2026101(2), 89-119open access
ABSTRACT We study how firms' inventory holdings are affected by natural disasters. Building on the premise that managers often make decisions in line with the availability heuristic, we investigate whether managers increase inventory holdings in response to heightened disaster risk perceptions and the need to hedge against inventory shortages. Through a battery of tests, we show that the occurrence of disasters in neighboring counties triggers inventory stockpiling, an effect that is unlikely to be driven by the real disaster disruptions. Our results also indicate that inventory stockpiling is likely inconsistent with a rational expectations equilibrium. Collectively, our results highlight another undesirable consequence of natural disasters and warn about supply chain implications due to increased climate ambiguity. Data Availability: All the data used in this study are publicly available. JEL Classifications: G31; G41; M21; M11; M41; Q54.
The Accounting Review202499(3), 201-224open access
ABSTRACT We exploit a regulatory change to examine whether bank regulator strictness is affected when regulators no longer rely on external assurance. In the absence of external assurance, we find that banks report higher nonaccrual loans, higher troubled debt restructurings, and both a timelier loan loss provision and higher quality allowance for loan loss reserve. Further, regulators spend more days performing targeted bank examinations for banks affected by the regulatory change. We do not find evidence of operational deterioration, but rather the findings are consistent with increased regulator strictness over the reporting of problem assets, particularly during targeted examinations. Overall, our results suggest that regulators become stricter when they can no longer rely on the work of external auditors and that third-party assurance is an imperfect substitute for direct regulatory monitoring. Data Availability: Bank regulatory rating and examination dates are confidential and were obtained from the Federal Reserve Bank of St. Louis. All other data are available from the public sources cited in the text. JEL Classifications: G21; G28; M42.
This study investigates the effect of firms' adoption of SFAS No. 131 segment disclosure rules on the stock market's ability to predict the firms' earnings, as captured by the forward earnings response coefficient (FERC). The FERC is the association between current-year returns and next-year earnings. SFAS No. 131, effective for fiscal years beginning after December 15, 1997, arguably increased both the quantity and quality of segment disclosure. Consistent with the standard's intended qualitative effects, pre-131 multi-segment firms experienced a significant increase in FERC after adopting SFAS No. 131. Consistent with the standard's intended quantitative effects, many pre-131 single-segment firms began disclosing multiple segments, and those that did experienced an increase in FERC. However, pre-131 single-segment firms that remained single segment (i.e., were unaffected by SFAS No. 131) had no change in FERC, indicating that the increase in FERC for 131-affected firms is not due to some other event concurrent to the adoption of SFAS No. 131. These results are robust under numerous procedures that control for characteristics of the sample firms and their earnings, providing strong evidence that SFAS No. 131 resulted in an increase in stock price informativeness for affected firms. Thus, we provide the first empirical price-based evidence that SFAS No. 131 provided more information (about future earnings) to the market, as the standard's proponents have suggested.
Across two large healthcare systems, intrasepsis factors improved postsepsis cardiovascular risk prediction as compared with presepsis cardiovascular risk profiles. Further exploration of sepsis factors that contribute to postsepsis cardiovascular events is warranted for improved mechanistic and predictive models.
ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.