There is a long-standing debate on whether sell-side analysts learn from their experience to improve earnings forecast skills. This study shows that incentive is an important factor for understanding the “learning by doing” effect by analysts. We examine analysts’ response to a complex type of information – corporate pension underfunding. Pension underfunding negatively impacts future earnings and analysts on average underreact to such information in their earnings forecasts. More importantly, when there is a strong incentive for analysts to deliver accurate forecasts, analyst learning effectively reduces their underreaction to pension underfunding information. On the other hand, when such an incentive is absent, the analyst learning effect is not discernible in the data. Further evidence suggests that analyst learning and incentive jointly reduce stock market mispricing associated with corporate pension underfunding.

We use matched employer-employee data to study the role of the firm in the transmission of income growth into consumption growth. We find that growth in income relative to the firm average (the within-firm component) translates significantly less into consumption than growth in firm average income (the between-firm component). These findings are explained by the lower persistence of the within-firm component of income, better self-insurance for workers more exposed to variations in income growth from the within-firm component, and peer effects in the workplace. Quantitatively, income persistence provides 43% of the explanatory power, self-insurance provides 35%, and peer effects provide 22%.

We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory‐motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support‐vector‐machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.

This paper investigates the impact of international political risk on government bond yields in 34 debtor countries using a comprehensive database of 109 international political crises from 1988 through 2007. After employing the total number of international political crises as a proxy for political risk and controlling for country-specific economic conditions, we establish a positive and significant link between international political risk and government bond yields. This is consistent with global bond investors demanding higher returns at times of high political uncertainty. In addition, we show that international political risk has a reduced adverse effect on bond prices when the debtor country has a stable political system and strong investor protection.

Managers often overreact to revenue fluctuations, leading to unnecessary workforce adjustments and increased training costs. This study examines how board governance influences firms’ employment sensitivity to revenue fluctuations. Analyzing global board reforms, we find that board reforms significantly reduce managerial overreaction to revenue fluctuations. Utilizing recent difference-in-differences estimators that address heterogeneous treatment effects, we ensure the robustness of our results. The reduction in employment sensitivity is more pronounced when board reforms strengthen the independence of boards and audit committees, particularly in jurisdictions with weaker board efficacy, shareholder, and employment protection legislation. Enhanced effects are observed in firms with initially lower board independence and rapid reform compliance, in entities experiencing greater information asymmetry, marked by higher labor intensity, higher pre-reform agency costs and financial constraints, and in firms led by less experienced CEOs or boards with higher male representation.

Taking advantage of the institutional difference in capture between local and non-local media in China, we examined the association between media capture and post-earnings announcement drift (PEAD). Using both portfolio and regression analyses, we found that, for the same firms, non-local media coverage is negatively associated with PEAD; however, there is no association between local media coverage and PEAD, except for non-state-owned firms. Given that in China, non-local media are less captured or more independent than local media, the negative association observed for non-local media coverage can be interpreted as an indication that media independence plays a role in reducing PEAD or improving informational efficiency in the stock market.