Hirshleifer et al. (J. Account. Econom. 38 (2004)) and Taffler, Lu and Kausar (J. Account. Econom. 38 (2004)) document large and statistically significant abnormal returns from trading on balance sheet data and audit opinions. However, the statistical tests ignore high transactions costs, especially for selling short, that would likely make the trading strategies unprofitable. The accounting anomalies literature is adding little to what we know about how and why markets operate more or less efficiently. I identify some research questions and opportunities, highlighting those with accounting and auditing implications.

Researchers often use regression-based x-Scores (e.g., conservatism C-Score , misstatement F-Score ) from a stage 1 model as a dependent variable in stage 2. We argue that this x-Score analysis can cause coefficient biases and interpretation problems because (1) x-Score does not capture new sources of variation, and (2) the estimates often hinge on unacknowledged technical assumptions. Instead, we recommend that researchers include the test variables and the relevant controls in stage 1, obviating the need for an x-Score . In replication analyses, some important published findings change after we remove the coefficient bias caused by the use of x-Score as a dependent variable.

We show that banks with high environmental, social, and governance (ESG) ratings issue fewer mortgages in poor localities—in number and dollar amount—than banks with low ESG ratings. This lending disparity happens at both the county and census tract level, worsens in disaster areas of severe hurricane strikes, is robust to alternative ESG ratings (including using only the social (S) component), and cannot be explained by banks’ differential deposit networks. We find no difference in mortgage default rates between high- and low-ESG banks, rejecting an alternative explanation based on differential credit screening quality. We report a complementary, not substitution, relation between high-ESG banks’ mortgage lending and their community development investments (like affordable housing projects) in poor localities. Loan-application-level analyses confirm that high-ESG banks are more likely than low-ESG banks to reject mortgage loans in poor neighborhoods. The evidence hints at social wash: banks deploy prosocial rhetoric and symbolic actions while not lending much in disadvantaged communities, the social function they arguably ought to perform. Community Reinvestment Act (CRA) examinations partially undo the social wash effect.

We study how nonfinancial firms’ operating risks change after bank competition increases. By exploiting the 1990s staggered regulatory reforms across U.S. states that allowed interstate banking and branching, we show that out-of-state bank entry was associated with lower borrower risk-taking on average. Large, profitable, safe, and geographically diversified firms signed up as new clients of large entrant banks, which offered larger and cheaper loans that reflected their higher efficiency and risk reduction through geographical diversification. We argue that these large banks could substitute for local relationship lending with more data collection from branches in multiple states. Firms that began borrowing from entrant banks increased capital expenditures and project-specific financing and kept R&D expenses stable but reduced R&D risk. Firms that continued borrowing from incumbent banks paid higher interest rates and increased their risk, suggesting that their credit access fell. States that opened up more had bigger changes in these outcomes. Data availability: Data are available from the public sources cited in the text.