This paper develops a theoretical model to explain corporate divestment in the context of accounting-based valuation and provides empirical evidence to support the model's predictions. Building on Zhang's (2000) real-options-based equity value model, we develop a model to explain why firms with multiple business segments may have incentives in financial reporting to shift earnings from one segment to another to influence market valuation. Cross-segment earnings shifting, however, causes information asymmetry about segmental performance, which leads to market misvaluation. Divestment arises as a voluntary commitment by (some) firms to not engage in segmental earnings manipulation, with the aim of restoring valuation accuracy. Our theoretical analysis yields a number of testable implications. Consistent with our model's predictions, we find empirically that (1) divestment is preceded by an increased divergence in profitability between the divested and continuing segments of the divesting firm, (2) there are positive abnormal stock returns surrounding divestment announcements that are not dependent on increased expectations about future operating performance, (3) the magnitude of market revaluation increases with the profitability divergence between the divested and continuing segments, and (4) market revaluation is greater for more complex firms (in terms of having a larger number of segments and greater uncertainty facing investors).

Applying a real-options-based valuation approach, we develop and test a model that addresses the incremental value relevance of segment data beyond firmlevel accounting data. Prior studies (e.g., Zhang 2000; Biddle et al. 2001) show that equity valuation requires accounting data (in part) because accounting provides signals that guide capital investments underlying value creation. In this study, we establish that the usefulness of segment data beyond aggregate data relates to heterogeneity of investment opportunities across segments, caused by divergences of segment profitability and growth potential. Empirical results are consistent with the model's predictions. We also assess the magnitude of the valuation impact of segment information relative to that of firm-level information.

How prior trust moderates investor responses to restatements is unknown. We examine how societal trust affects the changes in institutional investors’ shareholdings around a restatement. We consider two competing hypotheses based on the erosion of trust and confirmatory bias. We find the change in institutional investors’ shareholdings around a restatement is more negative for investors from high trust areas compared to low trust areas, consistent with an erosion of trust where high trust institutional investors view the restatement as a violation of trust. Further analyses show that our findings vary with the regulatory or economic environment, type of institution, and type of restatement. Our results are also robust to different tests that address endogeneity and use alternative societal trust measures. Overall, we contribute to the literature by examining the role of societal trust in a dynamic setting where investors’ trust-based beliefs about the credibility of accounting information are not realized. Data Availability: GSS Sensitive Data Files are not available from the authors. Persons interested in obtaining these data should contact the GSS at [email protected]. Other data are available from the public sources cited in the text.

This paper documents that changes in litigation risk affect corporate voluntary disclosure practices. We make causal inferences by exploiting three legal events that generate exogenous variations in firms' litigation risk. Using a matching-based fixed-effect difference-in-differences design, we find that the treated firms tend to make fewer (more) management earnings forecasts relative to the control firms when they expect litigation risk to be lower (higher) following the legal event. The results are concentrated on the earnings forecasts conveying negative news and are robust to alternative specifications, samples, and outcome variables.