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Economics challenge the specification of discretionary accrual models. Since rent-seeking firms pursue differentiated business strategies, firms in the same industry experience idiosyncratic shocks due to heterogeneous economic fundamentals and hence have different accrual-generating processes. We present evidence that idiosyncratic shocks are widespread, propagate through multiple years of financial statements, and reduce accrual models' goodness of fit. This not only affects abnormal accrual estimates for the firm experiencing shocks, but also affects measurement of abnormal accruals for other firms in the industry. We show that idiosyncratic shocks not only add noise to abnormal accruals, but can also exacerbate bias in both unsigned and signed abnormal accruals. We propose ways to reduce accrual model misspecification.

We investigate the extent to which banks’ quarter-end borrowings in the repurchase market deviate from within-quarter levels, and associated factors. Quarter-end repo liabilities are materially lower than within-quarter averages for a large fraction of sample banks. These deviations are more pronounced at banks with a higher concentration of repo borrowings in their liability structure and with larger absolute trading gains or losses. Furthermore, the association with trading activity is mitigated when banks are better capitalized. We also find that these deviations are associated with bank depositor and borrower behavior. Together, the evidence suggests that deviations reflect both active window dressing and passive customer-driven liquidity dynamics. We document that unexpected downward quarter-end deviations in repo liabilities are associated with adverse short-term capital market consequences. Over the long term, banks with more frequent downward quarter-end deviations exhibit higher credit risk, but we find mixed evidence for equity market valuation multiples.

This study addresses simultaneity bias in piecewise linear forms of the earnings-return relation. We specify an overidentified system of simultaneous equations that incorporates both asymmetric earnings timeliness and asymmetric earnings persistence specifications and implement two-stage least squares for this piecewise linear system. Estimation of a system that is piecewise linear in endogenous variables presents several issues that are unprecedented in the accounting literature. Findings provide evidence that the asymmetric timeliness specification is particularly affected by simultaneity and that failing to correct for simultaneity results in coefficient estimates that potentially understate the degree of asymmetric earnings timeliness. Moreover, inferences regarding how conditional conservatism has evolved over time are sensitive to whether OLS or 2SLS coefficients are used as the basis of comparison.

We investigate an unexplored channel—loss given default (LGD)—through which accounting information can shape the design of debt contracts. Using a sample of defaulted bonds, we find that borrower accounting information available at contract initiation possesses significant power for predicting realized LGD at the subsequent default date. We then use this model to construct an accounting-based measure of expected LGD at the contracting date for a large sample of bond issuances. We find that this measure is positively associated with issuance date interest spread and covenant use, and document that these relations are not artifacts of an association between LGD and probability of default. We then show that accounting-based expected LGD has a stronger association with issuance date spread when the borrower’s underlying accounting is more conservative and when the accounting-based LGD predictors are more persistent. Our results increase our understanding of both the informational role and contracting role of accounting information.

We define accrual duration as the length of time between an accrual and its associated cash flow. Accrual duration is inextricably linked to accrual discretion and accrual quality by the fundamentals of the accrual process—the recording of longer-duration accruals involves using longer-term estimates, which makes them relatively more discretionary and less reliable, ceteris paribus. We provide the theoretical development of this broad idea and demonstrate several empirical applications linking accrual duration to earnings persistence, Accounting and Auditing Enforcement Releases, asset write-offs, and the observed kink in the earnings distribution. A major advantage of the accrual duration approach is that it is quite general, which allows us to derive a powerful new model of total accruals discretion and quality as well as a novel measure of total accruals duration. Finally, we discuss how the accrual duration approach can illuminate numerous ongoing issues in accounting research.

I seek a deeper understanding of bankruptcy contagion and its impact on the industry information environment by examining how intra-industry bankruptcy affects the equity market response to earnings news subsequently released by industry survivors. I document significant negative asymmetry, i.e., a weaker response to good relative to bad news, in the response to earnings news in the wake of intra-industry bankruptcy. Results are consistent with a combination of model uncertainty-induced asymmetry and an on-average reduction in earnings informativeness stemming from increased cross-firm default risk assessments. Results are further consistent with managerial preemptive disclosure of good news exacerbating, but not completely explaining, this asymmetry. Additional tests provide evidence that these information effects are directly associated with contagion. The results facilitate a deeper understanding of contagion, and provide evidence that bankruptcy imposes broader industry-wide consequences than previously documented.

Sleep is an important component of human health and can be measured longitudinally using digital activity trackers. Further, decentralized digital research has the potential to provide a real-world picture of sleep in large populations. This study examined whether longitudinal sleep patterns from activity trackers could predict risk of obstructive sleep apnea (OSA) and hypertension, as defined by the Berlin questionnaire and self-report, respectively. We recruited adults aged ≥18 years nationwide to join our sleep-focused smartphone-based study, called the Research Framework for Exploring Sleep Health. Our sample of 391 adults predominantly comprised women (68%, 247/364) with a mean age of 48 (SD 13.62) years. Participants were asked to fill out health-related surveys, including the Berlin questionnaire and the Horne-Ostberg questionnaire for chronotype. Participants were asked to link their own activity tracker to the app to collect longitudinal sleep data. We analyzed sleep data from 391 participants; the cohort was predominantly White (65%, 231/353) followed by multiracial (17%, 61/353) and Hispanic or Latino (6.5%, 23/353) participants. Collinearity testing showed that OSA risk and self-reported hypertension could be considered independently. Holding BMI at a fixed value, the odds of having high OSA risk increased by 159% for every 1-hour increase in weekday sleep variability (odds ratio [OR] 2.592, 95% CI 1.613-4.400; P<.001), and the odds of high OSA risk increased by 93% for each 1-hour increase in weekend sleep variability (OR 1.928, 95% CI 1.197-3.094; P=.01). The odds of having high OSA risk increased by 22% for each unit (kg/m2) increase in BMI, holding both weekday and weekend sleep at separate fixed values (OR 1.217, 95% CI 1.153-1.293; P<.001). Controlling for age, sex, and BMI, the odds of endorsing hypertension increased by 71% for every 1-hour increase in weekday sleep variability (OR 1.712, 95% CI 1.062-2.917; P=.03). Conversely, for weekend sleep, the odds of endorsing hypertension increased by 43% for a 1-hour increase in weekend sleep variability (OR 1.432, 95% CI 1.062-1.928; P=.04). Increased sleep variability predicted a high risk for both OSA and hypertension in this decentralized cohort, when using data from the Berlin questionnaire. Our study demonstrates the utility of decentralized digital health studies in sleep research. It highlights the potential of activity trackers to predict risk for OSA and hypertension without requiring other patient information or assessment. Sleep variability is gaining increasing importance in the context of sleep health. Digital devices have the potential to help individuals assess their risk for certain disorders.

Solar‐wind forecasting is critical for predicting events which can affect Earth's technological systems. Typically, forecasts combine coronal model outputs with heliospheric models to predict near‐Earth conditions. Ensemble forecasting generates sets of outputs to create probabilistic forecasts which quantify forecast uncertainty, vital for reliable/actionable forecasts. We adapt meteorological methods to create a calibrated solar‐wind ensemble and probabilistic forecast for ambient solar wind, a prerequisite for accurate coronal mass ejection (CME) forecasting. Calibration is achieved by adjusting ensemble inputs/outputs to align the ensemble spread with observed event frequencies. We produce hindcasts in near‐Earth space using coronal‐model output over Solar Cycle 24, as input to Heliospheric Upwind eXtrapolation with time dependence (HUXt) solar‐wind model. Making spatial perturbations to the coronal model output at 0.1 AU, we produce ensembles of inner‐boundary conditions for HUXt, evaluating how forecast accuracy was impacted by the scales of perturbations applied. We found optimal spatial perturbations described by Gaussian distributions with variances of 20° latitude and 10° longitude; these might represent spatial uncertainty within the coronal model. This produced probabilistic forecasts better matching observed frequencies. Calibration improved forecast reliability, reducing the Brier score by 9% and forecast decisiveness increasing AUC ROC score by 2.5%. Improvements were subtle but systematic. Additionally, we explored statistical post‐processing to correct over‐confidence bias, improving forecast actionability. However, this method, applied post‐run, does not affect the solar‐wind state used to propagate CMEs. This work represents the first formal calibration of solar‐wind ensembles, laying groundwork for comprehensive forecasting systems like a calibrated multi‐model ensemble.

The grape is one of the earliest domesticated fruit crops and, since antiquity, it has been widely cultivated and prized for its fruit and wine. Here, we characterize genome-wide patterns of genetic variation in over 1,000 samples of the domesticated grape, Vitis vinifera subsp. vinifera, and its wild relative, V. vinifera subsp. sylvestris from the US Department of Agriculture grape germplasm collection. We find support for a Near East origin of vinifera and present evidence of introgression from local sylvestris as the grape moved into Europe. High levels of genetic diversity and rapid linkage disequilibrium (LD) decay have been maintained in vinifera, which is consistent with a weak domestication bottleneck followed by thousands of years of widespread vegetative propagation. The considerable genetic diversity within vinifera, however, is contained within a complex network of close pedigree relationships that has been generated by crosses among elite cultivars. We show that first-degree relationships are rare between wine and table grapes and among grapes from geographically distant regions. Our results suggest that although substantial genetic diversity has been maintained in the grape subsequent to domestication, there has been a limited exploration of this diversity. We propose that the adoption of vegetative propagation was a double-edged sword: Although it provided a benefit by ensuring true breeding cultivars, it also discouraged the generation of unique cultivars through crosses. The grape currently faces severe pathogen pressures, and the long-term sustainability of the grape and wine industries will rely on the exploitation of the grape's tremendous natural genetic diversity.

Accurately reconstructing the solar wind throughout the inner heliosphere is essential for understanding solar–terrestrial interactions and improving space‐weather forecasts. Conventional reconstruction methods rely on photospheric magnetic field observations and coronal models to estimate solar wind conditions near the Sun, typically at 0.1 AU. This introduces substantial uncertainty in the background flow used by heliospheric models through which coronal mass ejections (CMEs) propagate. Here we present a new approach that instead derives the inner‐boundary conditions directly from in situ solar wind observations, typically obtained near 1 AU. These observations are ballistically backmapped to 0.1 AU while accounting for both solar wind acceleration and solar rotation, and then corrected for stream‐interaction effects using a convolutional neural network trained on synthetic model data. The resulting 0.1 AU boundary conditions are used to drive the Heliospheric Upwind eXtropolation with time dependence (HUXt) model. Applied to the highly geoeffective May 2024 CME interval, this method reproduces solar wind conditions at Earth and at Solar Orbiter—on the far side of the Sun—with speed errors reduced by around 50% relative to traditional coronal‐model approaches. Although this represents a post‐event reconstruction rather than an operational forecast, the approach provides a fast, accurate, and magnetogram‐independent means of reconstructing the inner heliosphere, paving the way for improved CME analyses and future forecasting applications.