Abstract This paper examines how climate risk transparency influences house prices and credit supply. I contend that inadequate property-level climate risk disclosures induce homebuyers to demand a risk aversion discount on house prices, creating a potential market for lemons. Employing a stacked difference-in-differences design, I find that flood risk disclosure laws, by enhancing dwelling-specific flood risk transparency, raise house prices on average by 7.6%. Results strengthen in states with stricter disclosure requirements. Exploiting within-state heterogeneity and controlling for housing market trends, I find that the effects persist and intensify in regions with higher aggregate exposure to flood risk, greater information frictions, and more attention to climate risks. Conversely, the effectiveness of flood risk disclosure laws attenuates in regions where households are less concerned about climate change. Additionally, less sophisticated lenders extend credit to financially constrained borrowers in response to the laws but experience lower profitability.

ABSTRACT We examine the implications of GAAP earnings forecast quality for accounting research. Using a tax law change with an estimable and material GAAP earnings impact, we find that analysts’ GAAP forecasts generally fail to incorporate this impact, whereas investors respond promptly, suggesting that GAAP forecasts omit earnings information deemed relevant by investors and are of low quality. Analyzing quarterly GAAP forecasts from 2004–2019 and classifying GAAP forecasts that equal their street counterparts when GAAP and street actuals differ as low quality, we again find widespread low GAAP forecast quality. Low quality GAAP forecasts affect research inferences: they dampen GAAP earnings response coefficient (ERC) estimates, reduce the explanatory power of GAAP surprises for returns, affect inferences regarding market rewards for meeting-or-beating via exclusions, and understate the extent that GAAP forecasts incorporate exclusion components. We propose two strategies to mitigate the adverse effects of low quality GAAP forecasts on research inferences. Data Availability: Data are from publicly available sources as identified within the manuscript. JEL Classifications: G14; M40; M41.

ABSTRACT We use machine learning methods and high‐dimensional detailed financial data to predict the direction of one‐year‐ahead earnings changes. Our models show significant out‐of‐sample predictive power: the area under the receiver operating characteristics curve ranges from 67.52% to 68.66%, significantly higher than the 50% of a random guess. The annual size‐adjusted returns to hedge portfolios formed based on the prediction of our models range from 5.02% to 9.74%. Our models outperform two conventional models that use logistic regressions and small sets of accounting variables, and professional analysts’ forecasts. Analyses suggest that the outperformance relative to the conventional models stems from both nonlinear predictor interactions missed by regressions and the use of more detailed financial data by machine learning.

ABSTRACT Over three-quarters of U.S. taxpayers receive federal tax refunds, largely due to income tax over-withholding. This study explores how over-withholding impacts investments by comparing individuals’ investment behavior following wage receipts and tax refunds. We find a significantly higher marginal propensity to invest out of wages than refunds, suggesting that over-withholding, which alters the labeling and timing of income, meaningfully influences financial decision-making. Our cross-sectional analysis indicates that the differential investment rates are more pronounced for individuals with automatic investment setups and lower financial sophistication. The difference cannot be fully explained by alternative explanations such as lack of awareness, fixed dollar investment goals, timing differences between wages and refunds, uncertainty of refunds, transaction costs, or the perception of refunds as additional windfall income. Our findings underscore the importance of considering behavioral factors in the formulation of tax policies and contribute to the accounting literature that examines taxes and investment behavior.