This paper establishes a causal relation between an individual's decision whether to own stocks and average stock market participation of the individual's community. We instrument for the average ownership of an individual's community with lagged average ownership of the states in which one's nonnative neighbors were born. Combining this instrumental variables approach with controls for individual and community fixed effects, a broad set of time‐varying individual and community controls, and state‐year effects rules out alternative explanations. To further establish that word‐of‐mouth communication drives this causal effect, we show that the results are stronger in more sociable communities.

Milton Friedman argued that irrational traders will consistently lose money, will not survive, and, therefore, cannot influence long‐run asset prices. Since his work, survival and price impact have been assumed to be the same. In this paper, we demonstrate that survival and price impact are two independent concepts. The price impact of irrational traders does not rely on their long‐run survival, and they can have a significant impact on asset prices even when their wealth becomes negligible. We also show that irrational traders' portfolio policies can deviate from their limits long after the price process approaches its long‐run limit.

We examine how the Big 4 responded to evolving partner qualification requirements in China. By 2017, at least 80 percent of the Big 4 partners were required to hold CICPA qualifications, a requirement that did not apply to local firms. Using data from a 14-year window around the reform, we document that the Big 4 expanded their partnership and primarily complied through internal promotion of locally licensed auditors, often into junior signing roles and serving new clients. Our analyses of audit outcomes yield mixed and time-varying evidence: although we observe relative increases in restatements in selected periods, the triangulated evidence does not suggest a pervasive deterioration in audit quality. However, we find a decline in the Big 4’s market share and audit fee premium. Overall, our findings shed light on how regulatory interventions targeting auditor qualifications reshape audit firms and market competition. Data Availability: Data are available from the public sources cited in the text.

We examine the impact of the SEC’s Office of Minority and Women Inclusion (OMWI) on the role of employee gender in the Division of Corporation Finance’s filing review process. Gender bias theory suggests that women may work harder to compensate for perceived bias and discrimination. Consistent with this theory, we find that women reviewers issue longer comment letters, raise more issues, ask more accounting-specific questions, reference more authoritative guidance, request more filing amendments, follow up on more issues from prior rounds, and take longer to close the comment letter process. We also find that women are less prevalent in higher paygrades and leadership positions. These gender differences attenuate after the establishment of OMWI in 2011, but significant differences remain. Analyses of SEC employee survey data corroborate our comment letter results. Data Availability: All data are publicly available.

We describe variation in the reporting quality (i.e., accuracy and bias of reported net asset values (NAVs)) of private equity (PE) funds across types of external monitors (investors and auditors) and third-party service providers (valuation specialists, marketers, and administrators). In contrast to public markets, we find only limited evidence that reporting quality varies with the composition and types of investors in PE funds. We observe, however, that reporting quality varies with auditor involvement and the use of third-party service providers; these associations often differ across buyout (BO) and venture capital (VC) funds and from those observed in public markets. Our evidence is important to investors and regulators, especially now that PE supersedes public markets as the main vehicle to raise capital and as regulators increase their focus on private markets. Data Availability: Data used in this study are available from public sources listed in the paper.

This study uses machine learning models to forecast future material misstatements. Using raw financial data, audit variables, qualitative features, and an efficient algorithm, we design a dynamic model that continuously updates with new information. Our model outperforms the benchmarks for both one-year-ahead and two-year-ahead predictions in terms of out-of-sample predictive power and economic impact on net income. Using Explainable Artificial Intelligence, we identify key predictive features, including comprehensive income, foreign firm status, and accrued interest and penalties from unrecognized tax benefits. Results show that investors achieve better outcomes using a proactive investment strategy based on our prediction models than reactive detection models. Furthermore, our prediction model can help managers prevent internal control weaknesses, assist auditors in assessing misstatement risks in advance, and enable regulators to allocate inspection resources proactively. Our study advances the literature by moving beyond the detection of past material misstatements to the forecasting of future misstatements. Data Availability: Publicly available.

We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our machine-generated ETR predictions with those from ETRs implied by analysts’ earnings forecasts and find the algorithm’s predictions are less biased, more precise, and explain more of the variance in future ETRs. We then use Explainable AI (based on Shapley values) to measure the usefulness of each disclosure item in the algorithm’s predictions. We find that while some tax-related items are useful, others offer minimal value. Using the machine learning algorithm’s use of information as a benchmark, we then further use Shapley values to examine which information is underweighted or overweighted by analysts. Overall, our results help inform standard setters on the relevance of certain tax disclosures in achieving the objective of predicting future ETRs.