Review of Accounting Studies最新文献

Current corporate disclosures regarding carbon emissions lack generally accepted accounting rules. The transactional carbon accounting system described here takes the rules of historical cost accounting for operating assets as a template for generating carbon emissions (CE) statements comprising a balance sheet and a flow statement. The asset side of the CE balance sheet reports the carbon emissions embodied in operating assets. The liability side conveys the firm’s cumulative direct emissions into the atmosphere as well as the cumulative emissions embodied in goods acquired from suppliers less those sold to customers. Flow statements report the company’s annual corporate carbon footprint calculated as the cradle-to-gate carbon footprint of goods sold during the current period. Taken together, balance sheets and flow statements generate key performance indicators of a company’s past, current, and future performance in the domain of carbon emissions.

This study examines the investment value of information provided by crypto-influencers, that is, social media influencers covering crypto assets on Twitter. We examine the returns associated with approximately 36,000 tweets issued by 180 of the most prominent crypto social media influencers covering over 1,600 crypto assets for the two years spanning through December 2022. Our primary results indicate that crypto-influencers’ tweets are initially associated with positive returns. However, these tweets are followed by significant negative longer-horizon returns, suggesting they generate minimal long-term investment value. These effects are most pronounced for tweets issued by crypto-influencers proclaiming to be crypto experts, for smaller cap crypto asset securities and for self-described experts with many Twitter followers. In an additional analysis, we use machine-learning methods to classify tweets and find that this pattern of results strengthens when the tweets have a more positive sentiment or relate to buy recommendations.

While the production of credit ratings has long been limited mainly to rating agencies (CRAs), recent years have seen the growing popularity of consensus credit ratings crowdsourced from banks (i.e., bank ratings). We provide the first comprehensive examination of the properties and informativeness of bank ratings relative to CRA ratings. We find that bank ratings often deviate from CRA ratings, with over 60% of firm-months having different bank and CRA ratings. These deviations contain useful information. Bank ratings improve out-of-sample prediction of defaults and CRA rating revisions and explain the cross-section of credit spreads. However, bank ratings do not improve out-of-sample prediction of credit excess returns, indicating that current prices incorporate bank rating information. Overall our findings suggest that bank ratings are a useful supplement to traditional credit ratings.

Decentralized finance (DeFi) has emerged to offer traditional financial services such as lending, borrowing, and trading without intermediaries (e.g., banks). DeFi transactions are typically executed using a special digital class of contracts called smart contracts. These contracts are self-executing and hard-coded directly on a blockchain. We observe the emergence of a new class of voluntary audits that evaluate the integrity of these contracts. Using a hand-coded sample of about 8,500 smart contract audit reports, we provide some of the first evidence showing that (1) these audits are pervasive, (2) the audit firm market is composed of new technical audit firms, (3) the scope of these audits can span a variety of contract features, (4) the audit inputs and outputs differ substantively from those of conventional financial audits, and (5) the market reacts positively to the release of these audit reports, suggesting that these reports are value-relevant. These findings highlight the demand for novel assurance services driven by blockchain technology.

We study how the launch of an electronically traded fund (ETF) that holds firms in a specific industry changes the behavior of analysts who follow that firms in that industry. An industry ETF allows investors to trade the firm-specific payoff separately from the industry payoff. This causes significant changes in the value of different types of information. In particular, following an increase in a firm’s industry ETF coverage, the firm’s analyst coverage increases in the following year, and this holds after controlling for changes in institutional investment and other characteristics. We also find that, following an increase in ETF coverage, analyst recommendations are more likely to include an industry recommendation separate from the firm-specific recommendation, and the latter is more likely to be stated in relative terms. Our results strengthen when the new ETF is a better hedge against the industry payoff factor and when we introduce a plausible control for endogeneity.

From 2008 to 2020, 180 of S&P 1500 have disclosed employee diversity targets. We conduct the first analysis of firms’ employee diversity targets and ask three research questions: (i) who announces diversity targets? (ii) do firms deliver on their diversity targets? (iii) what are the implications of disclosure of such targets for employee hiring and investors? We find that firms with a greater willingness (proxied by past ESG penalties, higher CEO-to-median employee pay ratio, more media coverage, and after #MeToo and Black Lives Matter movements) and ability (proxied by financial strength, a blue-collar heavy labor force, and gender and ethnic minorities on boards) to improve employee diversity are more likely to disclose diversity targets. Exploiting the Revelio dataset of 15,639 firm-years for 1,203 distinct firms from 2008 to 2020, we observe that firms that disclosed a diversity target have indeed hired more diverse employees, but such diversity levels had already increased substantially prior to the target disclosure. Firms with numerical, forward-looking, and rank-and-file employee-targeted goals are associated with greater employee diversity relative to firms that announce other types of diversity goals. Moreover, improved diversity performance does not appear to occur at the cost of employee quality, as measured by Revelio. Overall our results have practical implications for how investors and stakeholders might want to interpret corporate diversity targets.

Activist investors in a firm often voluntarily release information about their governance intentions to the public. Voluntary disclosure theory suggests that an activist investor will disclose when she expects other investors to respond positively and support her in upcoming corporate control contests. We find that activists’ disclosures are accompanied by positive abnormal returns, reductions in bid-ask spreads, and increases in future earnings relative to similar targets without voluntary activist disclosures. Disclosures by activists who demand a board seat (the most common demand) have the highest announcement returns, and disclosers also win proxy contests and directorships more frequently than non-disclosers. These findings suggest that the activist’s beliefs about investor response in both pricing and voting are an important driver of her disclosure choice.

Ethnic tension in the United States became more salient after Donald Trump’s election on November 9, 2016. Exploring the variation in management forecasts, we show that after the Trump election, minority CEOs exhibit more pessimism in their earnings forecasts (underestimation of the mean of earnings), compared with their non-minority counterparts. This tendency toward pessimism is observed across CEOs from various minority groups and is particularly pronounced in firms led by less experienced or less confident minority CEOs. Further analyses reveal that minority CEOs make less specific and less precise forecasts. Minority CEO pessimism is not explained by alternative explanations including terrorist attacks and minority CEOs’ political ideology. Collectively, our research provides evidence that the Trump election induces biased beliefs in the form of pessimism in ethnic minority CEOs.

We examine the role of private equity in alleviating earnings myopia induced by public markets. We first construct a measure of earnings myopia and show that this measure varies as predicted with determinants and effects of myopia. Then we show that public firms exhibiting earnings myopia realize an increased likelihood of takeover by private equity buyers. Cross-sectional analyses indicate that this relation is strongest when costs of earnings myopia are likely higher. Following private equity takeovers, firms exhibiting greater measures of earnings myopia realize improvements to R&D investment and productivity. The results add to the understanding of the role of private equity in identifying and alleviating earnings myopia within U.S. capital markets. This is important given the increasing size of private equity assets under management. Takeover premiums paid for myopic firms suggest a cost of earnings myopia at approximately 6.9% of firm value.

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.