This paper studies how anticipated disagreement between a financier and an entrepreneur affects optimal contracting and asset prices. The value of debt is uniquely immune to anticipated disagreement, and when the set of anticipated disagreements is sufficiently rich, this immunity causes the optimal contract to give the financier debt. In contrast, the values of other contracts, including equity, decline as anticipated disagreement becomes more severe. This suggests a channel through which an increase in the severity of anticipated disagreement increases the equity premium and the debt-to-equity ratio.

This paper recasts the consumption asset pricing model in terms of accounting numbers that connect to consumption and the risk to consumption under accounting principles. The modeling yields an expected return measure that forecasts realized returns and the risk to those returns. It leads to the construction of a pricing factor from the accounting information. The factor performs well relative to extant factors in explaining cross-sectional returns. The factor return has negative correlation with the market portfolio and exhibits the property of protecting payoffs in bad states when consumption is low. This then prompts a two-factor representation that combines the market portfolio with a hedge portfolio against loss to consumption.

We provide novel evidence that arbitrageurs use exchange-traded funds (ETFs) as an avenue to circumvent short-sale constraints at the stock level. Using a large sample of U.S. equity ETF holdings, we document that shorting activity on ETFs rises with the difficulty of shorting underlying stocks. Stocks heavily shorted via their holding ETFs underperform those that are lightly shorted. The return predictability of ETF shorting is distinct from stock-level shorting measures and is concentrated among stocks that face severe arbitrage constraints. These findings suggest that ETFs allow arbitrageurs to target overpriced stocks that are otherwise difficult to short.

We study the impact of FinTech competition in payment services when a monopolist bank uses payment data to learn about consumers’ credit quality. Competition from FinTech payment providers disrupts this information spillover. The bank’s price for payment services and its loan offers are affected. FinTech competition promotes financial inclusion, may hurt consumers with a strong bank preference, and has an ambiguous effect on the loan market. Both FinTech data sales and consumer data portability increase bank lending, but the effects on consumer welfare are ambiguous. Under mild conditions, consumer welfare is higher under data sales than with data portability.

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.

Empirical studies demonstrate striking patterns in stock returns related to scheduled macroeconomic announcements. A large proportion of the total equity premium is realized on days with macroeconomic announcements. The relation between market betas and expected returns is far stronger on announcement days as compared with nonannouncement days. Finally, these results hold for fixed-income investments as well as for stocks. We present a model in which agents learn the probability of an adverse economic state on announcement days. We show that the model quantitatively accounts for the empirical findings. Evidence from options data provides support for the model’s mechanism.

Unlike prior research that focuses on determinants of firm-specific stock price crashes (SPCs), we study the consequences of SPCs on market information efficiency. The tension underlying our research question stems from two competing explanations. As an unanticipated shock, an SPC could stimulate (distort) information efficiency by triggering investor rational attention (opinion divergence). Our identification strategy involves a difference-in-differences analysis in which SPC firms in the treatment sample are propensity score matched with non-SPC firms in the industry-peer control sample, as well as placebo tests for falsification. Consistent with the stimulation effect, we find an increase of the earnings response coefficient and a decrease in post-earnings announcement drift, from the pre- to post-SPC period, for SPC firms, but not for non-SPC firms. Further analyses reveal that SPC firms attract increased investor attention, as reflected in greater analyst coverage and more investor access to firms' online financial filings following such an event. Using mutual fund flow redemption pressure based on hypothetical sales as an exogenous shock to SPCs, we provide evidence corroborating our causal interpretation of the main findings. Collectively, the evidence suggests that SPCs can attract increased investor attention, bringing about positive externalities by stimulating market information efficiency.

We propose an extrapolative model of bubbles to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: because of the interaction between extrapolative beliefs and disposition effects, investors are quick to not only buy assets with positive past returns but also sell them if good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test and confirm the model’s predictions about trading volume. We quantify the magnitude of the proposed mechanism and show that it can increase trading volume by another 30%.

We investigate two related questions about the trade-off between the short-term pressures on managers to meet earnings targets and the long-term environmental benefits of reduced pollution. Do firms release more toxins by cutting back on pollution abatement costs to boost earnings in years they meet earnings benchmarks? If so, is that relation weaker for firms with higher environmental ratings? Using Environmental Protection Agency (EPA) data on toxic emissions, we find that U.S. firms pollute more when they meet or just beat consensus earnings per share (EPS) forecasts, suggesting that meeting expectations is a more important goal than reducing pollution. We find this relation is stronger, not weaker, for firms with higher environmental ratings: they increase pollution even more when meeting earnings benchmarks than firms with lower ratings. This suggests that highly rated firms build regulatory and reputational slack over time and use it when needed to soften the negative impact of increased pollution. We contribute to the real earnings management and environmental economics literatures by documenting a negative externality of financial reporting incentives on the environment and society. We also contribute to the corporate sustainability literature by showing that an environmental, social, and governance (ESG) focus does not curb managerial short-termism.

This study examines the effect of fund managers' bubble-crash experience on their investment styles. Using the 2007–2008 and 2014–2015 bubble-crash events of China's A-share market, we find that managers who experienced a stock market crash are more value-oriented in their portfolios. While the effect of firsthand bubble-crash experience on investment style is significant, we find little evidence of the impact of secondhand experience gained from institutional memory or the management team. We explain these findings using incomplete information learning theory, salience theory, and the change in risk preferences. Moreover, different investment styles across manager types affect fund performance around the time the new bubble burst in June 2015. Managers with 2007–2008 bubble-crash experience and a more value-oriented investment style have lower (higher) fund returns than those without such experience before (after) the bubble burst.