Financial institutions, especially in Europe, hold a disproportionate amount of domestic sovereign debt. We examine the extent to which this home bias leads to capital misallocation in a real business cycle model with imperfect information and fiscal stress. We assume banks can hold sovereign debt according to a zero-risk weight policy and contrast this scenario to one in which banks weight the sovereign debt according to default probabilities. Banks are assumed to miscalculate the probability of a disaster state due to moral hazard and imperfect monitoring. This distortion pushes the economy away from the first-best allocation. We show that the zero risk weight policy exacerbates these distortions while a non-zero risk-weight improves allocations. The welfare costs associated with zero-risk weight policies are large. Households are willing to give up 3.2 percent of their consumption to move to the first-best allocation, whereas in the economy with non-zero risk-weights households are willing to give up only 1.2 percent of their consumption to move to the first-best allocation.

Research suggests that the standard model used to detect opportunistic shifting of core expenses to special items is potentially biased. Such bias has been attributed to the use of accruals, including special item related accruals, as a control for the impact of performance on core earnings in this model. This paper provides an improved classification shifting model which both tests for such accruals-related bias and controls for other sources of error in the measurement of shifting. The paper also modifies conventional market rationality tests in accounting research to examine new dimensions of rationality in relation to measurement and valuation of shifting. The main empirical findings are as follows. First, the improved classification shifting model provides strong evidence of shifting and rejects the hypothesis that inclusion of accruals in the model causes bias. Second, estimates of shifted core expenses generated by the improved model exhibit forecasting properties of shifted earnings. Third, rationality test results are broadly consistent with rationality in relation to shifted core expenses but indicate possible partial (ir)rationality in relation to adjusted special items (i.e., special items excluding shifted core expenses). Further analysis of the latter findings, however, suggests they are more likely related to risk than irrationality. Overall, the paper contributes to improved measurement of shifting and highlights the importance of considering rational expectations when examining stock returns associated with shifting.

I show that an alignment in partisan affiliation, between a firm’s management and the president, is associated with higher levels of investment. Using insider trading data, I find that managers become more optimistic about their companies’ prospects when their preferred party is in power. This optimism-driven increase in investment is amplified by herding and associated with both lower profitability and stock returns. Overall, managers’ political beliefs produce heterogeneous expectations about future cash flows and distort investment decisions.

We examine whether the change in earnings announcement textual tone, aggregated across individual publicly traded firms, helps predict gross domestic product (GDP) growth. The literature finds that changes in aggregate accounting earnings do help predict GDP growth, but only when aggregate earnings changes are negative. Because conservative accounting rules limit managers' ability to communicate positive news promptly, we examine the tone of quarterly corporate earnings announcements as a possible source of timely positive information provided by firms. We find that the change in aggregate tone in the earnings announcements from the same quarter in the previous year predicts one‐quarter‐ahead GDP growth, but only when the change is positive. Our study contributes to the literature by investigating the relation between aggregate corporate disclosure tone and macroeconomic outcomes.

ABSTRACT This study examines how algorithmic trading (AT) affects forward‐looking disclosures in Management Discussion and Analysis (MD&A) of annual reports. We predict and find evidence that AT relates negatively to modifications in year‐over‐year forward‐looking MD&A disclosures. This evidence is consistent with AT reducing investors’ demand for fundamental information, which reduces managers’ incentives to supply costly forward‐looking disclosures. Cross‐sectional tests provide additional evidence that this negative relation is more pronounced for firms with larger earnings surprises and those with losses. We further validate our conclusion by demonstrating that investors’ fundamental information searches are a channel through which AT affects forward‐looking disclosures. The conclusion is robust to using the SEC's Tick Size Pilot Program as an exogenous shock to AT and to using alternative disclosure measures (e.g., tone revisions and number of sentences in forward‐looking MD&A disclosures). Overall, our study demonstrates that AT is a contributing factor to regulators’ concerns over the diminishing usefulness of forward‐looking information in MD&A disclosures.

Gun violence is the most pressing public safety problem in U.S. cities. We report results from a randomized controlled trial (N = 2,456) of a community-researcher partnership called the Rapid Employment and Development Initiative (READI) Chicago. The program offered an 18-month job alongside cognitive behavioral therapy and other social support. Both algorithmic and human referral methods identified men with strikingly high scope for gun violence reduction: for every 100 people in the control group, there were 11 shooting and homicide victimizations during the 20-month outcome period. Fifty-five percent of the treatment group started programming, comparable to take-up rates in programs for people facing far lower mortality risk. After 20 months, there is no statistically significant change in an index combining three measures of serious violence, the study’s primary outcome. Yet there are signs that this program model has promise. One of the three measures, shooting and homicide arrests, declined 65% (p = .13 after multiple-testing adjustment). Because shootings are so costly, READI generated estimated social savings between $182,000 and $916,000 per participant (p = .03), implying a benefit-cost ratio between 4:1 and 18:1. Moreover, participants referred by outreach workers—a prespecified subgroup—saw enormous declines in arrests and victimizations for shootings and homicides (79% and 43%, respectively) which remain statistically significant even after multiple-testing adjustments. These declines are concentrated among outreach referrals with higher predicted risk, suggesting that human and algorithmic targeting may work better together.

We study the impact of exchange rate volatility on cost efficiency and market structure in a cross-section of banks that have non-trivial exposures to foreign currency (FX) operations. We use unique data on quarterly revaluations of FX assets and liabilities (Revals) that Russian banks were reporting between 2004 Q1 and 2020 Q2. First, we document that Revals constitute the largest part of the banks’ total costs, 26.5% on average, with considerable variation across banks. Second, we find that stochastic estimates of cost efficiency are both severely downward biased – by 30% on average – and generally not rank preserving when Revals are ignored, except for the tails, as our nonparametric copulas reveal. To ensure generalizability to other emerging market economies, we suggest a two-stage approach that does not rely on Revals but is able to shrink the downward bias in cost efficiency estimates by two-thirds. Third, we show that Revals are triggered by the mismatch in the banks’ FX operations, which, in turn, is driven by household FX deposits and the instability of Ruble’s exchange rate. Fourth, we find that the failure to account for Revals leads to the erroneous conclusion that the credit market is inefficient, which is driven by the upper quartile of the banks’ distribution by total assets. Revals have considerable negative implications for financial stability which can be attenuated by the cross-border diversification of bank assets.

This paper provides empirically implementable sufficient statistics formulas for optimal nonlinear tax systems in the presence of across-income heterogeneity in preferences, inheritances, income-shifting capabilities, and other sources. We characterize optimal smooth tax systems on income and savings (or other commodities), as well as simpler tax systems. We use familiar elasticity concepts and a novel sufficient statistic for heterogeneity correlated with earnings ability: the difference between across-income variation in savings and the causal effect of income on savings. We apply these formulas to the United States and find that the optimal savings tax is mostly positive and progressive. (JEL E21, G51, H21, H24)

ABSTRACT We study the extent to which credit index (CDX) options are priced consistent with S&P 500 (SPX) equity index options. We derive analytical expressions for CDX and SPX options within a structural credit‐risk model with stochastic volatility and jumps using new results for pricing compound options via multivariate affine transform analysis. The model captures many aspects of the joint dynamics of CDX and SPX options. However, it cannot reconcile the relative levels of option prices, suggesting that credit and equity markets are not fully integrated. A strategy of selling CDX volatility yields significantly higher excess returns than selling SPX volatility.

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). The standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair, and thus, is conservative. The analytical inference involves estimating multiple functional quantities that requires several tuning parameters. In this paper, we propose two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. In particular, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.