This paper studies the effect of timing and commitment of verification in a principal-agent relationship with moral hazard. To acquire additional information about the agent’s behavior, the principal possesses a costly technology that produces a noisy signal about the agent’s effort choice. The precision of this signal is affected by the principal’s verification effort. Two verification procedures are discussed: monitoring where the principal verifies the agent’s behavior simultaneously with his effort choice and auditing where the principal can condition her verification effort on the realized outcome. As it is well known, the principal prefers to audit the agent’s behavior if she can commit to her verification effort at the time of contracting. The main contribution of this paper is to highlight the importance of commitment by the principal to her verification effort. In particular, I show that, when the principal cannot commit to her verification effort ex-ante, the principal strictly prefers monitoring to auditing if the gains from choosing high effort are sufficiently high.

We examine how removing user anonymity affects social media’s ability to generate value-relevant information for the stock market. Using a difference-in-differences design that exploits the differential timing of adopting real-name verification policies by the two most popular investment-related social media websites in China, we find that content on the treated site becomes significantly more informative about future stock returns and earnings after the policy takes effect. This effect is primarily driven by continuing users who post more actively in the pre-period. Although these users post less after the policy, the informational content of their posts increases, suggesting greater prudence in expressing opinions. Our results strengthen for firms that attract regulatory scrutiny. Overall, our study suggests that real-name verification policies can discipline internet users and potentially improve the informativeness of investment-related social media, particularly in emerging markets where retail investors predominate.

The Tax Cuts and Jobs Act (TCJA) of 2017 marked a significant change in U.S. domestic and international tax policy, altering incentives for U.S. firms to own foreign assets. We examine the initial response of U.S. firms’ foreign acquisition patterns to the TCJA’s key reform provisions. We find a significant overall decrease in the probability that a foreign target is acquired by a U.S. firm after the reform, suggesting that the net effect of the TCJA was to reduce acquisitions abroad. Cross-sectional variation across target and acquirer characteristics points to the elimination of the repatriation tax and the TCJA’s global intangible low-taxed income (GILTI) regime as critically influencing cross-border acquisitions by U.S. firms. Specifically, U.S. acquirers with little foreign presence prior to the TCJA are more likely to acquire a foreign target, while U.S. acquirers are less likely to acquire profitable targets in low-tax countries. Results from our empirical analyses are consistent with the TCJA prompting fewer but more value-enhancing, less tax-motivated foreign acquisitions by U.S. firms.

We examine whether information processing constraints limit managers’ ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.

We extract and describe corporate-issued guidance contained in over 23,000 LSEG Guidance Reports of S&P 1,500 firms from 2005 to 2021. Our sample contains 1.735 million Guidance Reports guidance instances that span over 180 guided items and fall into three broad categories: (1) qualitative topics, (2) consolidated financial statements, and (3) other key performance indicators. We identify research opportunities arising from Guidance Reports’ rich features, including quantitative or qualitative form, underpinning text, disclosure channels, and source speakers. We also compare Guidance Reports to the commonly used I/B/E/S Guidance database, which covers only quantitative guidance for 13 items. Approximately 1.494 million Guidance Reports instances fall outside I/B/E/S Guidance’s coverage, and even among overlapping items, only a subset is translated to I/B/E/S Guidance based on LSEG cost–benefit considerations. Our findings suggest researchers should be aware of the extent and nature of I/B/E/S Guidance omissions when studying guidance.