The sudden and exogenous nature of the COVID-19 crash provides a unique identification opportunity to study insiders’ informational advantages. We find that the sales of insiders at firms with connections to China were significantly more profitable during the COVID-19 crisis than the sales of insiders at firms without connections to China. Consistent with greater attentiveness to public information about the COVID-19 pandemic, this result is driven by China connected insiders executing larger (smaller) sales in the early (late) COVID-19 period than non–China connected insiders. We find our results are driven by trades that are not preplanned under Rule 10b5–1 and are consistent with anticipation of the systematic market effects of COVID-19 on an insider’s firm as opposed to firm-specific effects. Aggregate China connected insider trades also predict market returns during the COVID-19 period. Our study contributes to the insider trading literature by introducing geographic connection to market-wide information as a source of public information advantage and to regulatory efforts to investigate and understand corporate insider behavior related to the COVID-19 pandemic.

This paper examines the current empirical accounting research paradigm. We ask: In general, do the estimated regressions support the promoted narratives? We focus on a regression model’s main variable of interest and consider the extent to which it contributes to the explanation of the dependent variable. We replicate 10 recently published accounting studies, all of which rely on significant t-statistics, per conventional levels, to claim rejection of the null hypothesis. Our examination shows that in eight studies, the incremental explanatory power contributed by the main variable of interest is effectively zero. For the remaining two, the incremental contribution is at best marginal. These findings highlight the apparent overreliance on t-statistics as the primary evaluation metric. A closer examination of the data shows that the t-statistics produced reject the null hypothesis primarily due to a large number of observations (N). Empirical accounting studies often require N > 10,000 to reject the null hypothesis. To avoid the drawback of t-statistics’ connection with N, we consider the implications of using Standardized Regressions (SR). The magnitude of SR coefficients indicates variables’ relevance directly. Empirical analyses establish a strong correlation between a variable’s estimated SR coefficient magnitude and its incremental explanatory power, without reference to N or t-statistics.

ChatGPT frequently appears in the media, with many predicting significant disruptions, especially in the fields of accounting and auditing. Yet research has demonstrated relatively poor performance of ChatGPT on student assessment questions. We extend this research to examine whether more recent ChatGPT models and capabilities can pass major accounting certification exams including the Certified Public Accountant (CPA), Certified Management Accountant (CMA), Certified Internal Auditor (CIA), and Enrolled Agent (EA) certification exams. We find that the ChatGPT 3.5 model cannot pass any exam (average score across all assessments of 53.1%). However, with additional enhancements, ChatGPT can pass all sections of each tested exam: moving to the ChatGPT 4 model improved scores by an average of 16.5%, providing 10-shot training improved scores an additional 6.6%, and allowing the model to use reasoning and acting (e.g., allow ChatGPT to use a calculator and other resources) improved scores an additional 8.9%. After all these improvements, ChatGPT passed all exams with an average score of 85.1%. This high performance indicates that ChatGPT has sufficient capabilities to disrupt the accounting and auditing industries, which we discuss in detail. This research provides practical insights for accounting professionals, investors, and stakeholders on how to adapt and mitigate the potential harms of this technology in accounting and auditing firms.