This paper explores the shock of school closures caused by the COVID-19 pandemic to study the effect of childcare responsibilities on analyst forecasts. With manually collected data on whether analysts have children, I find that female analysts with children (mother analysts) are less likely to issue timely forecasts after school closures, compared to male analysts with children (father analysts). Mother analysts’ forecasts also become less accurate after school closures, but the negative effect only exists among forecasts for firms with relatively low institutional ownership, suggesting that mother analysts prioritize maintaining the forecast accuracy for firms that are more important to their careers. Additionally, mother analysts shift forecast release times to avoid childcare hours. My findings imply that childcare responsibilities hurt the productivity of mother analysts more than that of father analysts, even though these women have established themselves in a competitive industry.

Between 2010 and 2020, the U.S. coal industry experienced a 50% drop in production, employment, and active mines, driven by regulatory factors and technological innovation in alternative energy sources. We study the impact of this energy transition on household employment, wages, migration, and home ownership in affected communities. Compared to non-coal-producing, resource-rich counties, coal-producing counties experience 6% and 4% drops in employment and wages, respectively, during this period. Economic mobility and access to banking services significantly moderate these real effects, suggesting a potential role for finance to shape the industrial and economic changes associated with climate transitions.

In China's credit markets with financial repression, state-controlled non-financial firms (SOEs) are privileged in gaining access to bank credit, while non-SOEs, especially those small- and medium-sized firms, are disadvantaged. Corporate re-lending emerges as a response wherein the former secure bank loans and then re-lend to the latter. We document the characteristics of inter-corporate loans from a sample of legal cases. We employ four empirical strategies to conduct a forensic study of re-lending by detecting abnormal relations between financial accounts of listed firms. State-controlled companies conduct more re-lending, and firms with better growth opportunities, stronger corporate governance, and more financial constraints engage less. We compare re-lending with entrusted loans and find that firms extending nonaffiliated entrusted loans conduct re-lending actively, while firms offering affiliated entrusted loans do not. We also compare inter-corporate loans with micro-credit company loans in a review of legal cases.

We document a regime change in the Treasury market post-Global Financial Crisis (GFC): dealers switched from net short to net long Treasury bonds. We construct “net-long” and “net-short” curves that account for balance sheet and financing costs, and show that actual yields moved from the net short curve pre-GFC to the net long curve post-GFC. Our theory shows the regime shift caused negative swap spreads and co-movement among swap spreads, dealer positions, and covered-interest-parity violations. Furthermore, the effects of various monetary and regulatory policies are regime-dependent. We highlight Treasury supply as a plausible driver of this regime shift.

Violations of no-arbitrage conditions measure the shadow cost of intermediary constraints. Intermediary asset pricing and intertemporal hedging together imply that the risk of these constraints tightening is priced. We describe a “forward CIP trading strategy” that bets on CIP violations shrinking and show that its returns help identify the price of this risk. This strategy yields the highest returns for currency pairs associated with the carry trade. The strategy’s risk substantially contributes to the volatility of the stochastic discount factor, is correlated with both other near-arbitrages and intermediary wealth measures, and appears to be consistently priced across various asset classes.

SEC-mandated, machine-readable structured filings are an alternative source to Compustat for companies' accounting data. Discrepancies between as-filed and Compustat data, potentially a result of Compustat's standardizations, are more pronounced for firms with complex financial reporting. We show that these data discrepancies affect inferences in four research settings: (i) properties of accrual accounting, including accruals-cash flow relationships and abnormal accruals; (ii) real earnings management; (iii) the existence and magnitude of six of 21 accounting-based anomalies examined, including the accruals anomaly; and (iv) disclosure quality assessments based on the hierarchical structure of financial statement items. FactSet data also exhibit significant and often larger discrepancies from as-filed data. Our findings demonstrate the importance of these data discrepancies for the interpretation of empirical tests.

The Basel Committee and the Financial Stability Board require a consensus on the identification of characteristics that make a financial institution more prone than others to be severely hit by systemic shocks. This paper introduces a new tool to achieve this goal: a model for the Conditional Average Systemic Effects (CASE). The CASE quantifies the average effect of a system wide shock or market downturn on the profit and loss account of a bank, a firm or on the return of an asset. We propose a linear model for CASE with heterogeneous effects in observable characteristics. These models complement alternative measures of systemic risk and allow researchers to identify the determinants of the vulnerability of a given financial institution. We develop bootstrap inference that accounts for both estimation risk and model misspecification risk, and show the utility of our results in Monte Carlo simulations and an empirical application to 100 large U.S. financial firms.