Journal of Accounting Research最新文献

We examine the usefulness of machine learning approaches for measuring vocal tone in corporate disclosures. We document a substantial mismatch between the widely adopted actor-based training data underlying these approaches and speech in corporate disclosures. We find that existing models achieve near-perfect vocal tone classification within their training domain. However, when tested on actual executive speech during conference calls, their performance declines to chance levels. We thus introduce FinVoc2Vec, a deep learning model that adapts to audio recordings of conference calls and classifies the vocal tone of executive speech significantly more accurately than chance. FinVoc2Vec estimates are associated with future firm performance and can be used to construct profitable stock portfolios. Throughout our analyses, estimates from previous vocal tone models are largely unrelated to firm performance. Our findings emphasize the importance of a domain-specific approach to voice analysis in accounting and finance.

We propose a new measure of firms' disclosure based on company websites, which are widely available and contain a wealth of information. For a sample of U.S. public firms, we construct our disclosure measure using historical website data, validate it by correlating it with extant measures of disclosure and information asymmetry, and explore its determinants. We then apply our measure to the study of U.S. private firms' disclosure and French firms' compliance with a nonfinancial disclosure mandate. Our applications illustrate that our website-based measure provides a useful complement to extant measures of disclosure, which are more narrowly focused on investors in public capital markets.

The protection of free speech enhances the ability of various public stakeholders to disseminate privately observed adverse information about public firms, making it difficult for corporate managers to conceal negative information about their companies. Using the staggered enactment of anti-strategic lawsuit against public participation (anti-SLAPP) laws across U.S. states as a shock that strengthens free speech protection, we show that stronger protection is associated with less concealment of bad news. This is evidenced by a lower likelihood of stock price crashes, a decreased probability of accounting fraud, and an increased frequency of firm-initiated negative press releases. These results are more pronounced for states with stronger anti-SLAPP laws and where such laws are likely to be more influential. Moreover, the impact of free speech protection on bad news concealment is more significant for firms with greater ex ante incentives to conceal negative information. Further analyses suggest that free speech protection likely enhances the ability of short sellers, employees, and the media, among others, to reveal bad news about public firms. Finally, we find evidence that strengthened free speech protection also improves financial reporting quality. Our study highlights the importance of free speech protection in enhancing corporate transparency.

We study how foreign investors’ access to corporate information varies with pairwise geopolitical tensions between the investor's and investee's countries. Using a sample of 1,760 country-pairs, we find that geopolitical tensions between a conference call host firm's country and a foreign country relate negatively with investor participation from that foreign country as well as the quality and tone of management responses in the call, suggesting reduced foreign investor information access with rising tensions. A single-country analysis of Chinese AB-share firms produces similar inferences. With higher China–foreign tensions, foreign attendance at private meetings with management declines, and English translations of local language disclosures become less frequent and shorter in length. Curtailment in English translations is more pronounced when local language disclosures contain politically sensitive words, suggesting lower information supply by firm choice. Reduced disclosures from geopolitical tensions are impounded in foreign investors’ asset pricing decisions, resulting in steeper foreign-share price discounts.

Most loan contracts in the United States contain a provision for lender voting rules. We study the optimal voting rule that allows lenders to waive a covenant violation. When lenders have heterogeneous preferences, lenient voting rules increase the probability of waivers that allow inefficient investments. Stringent voting rules tend to allocate the marginal vote to lenders who deny waivers after false alarms so that they can renegotiate the loan to extract value from the firm, which incurs deadweight costs. In equilibrium, the optimal voting rule balances these two forces to improve contracting efficiency. We derive and empirically test comparative statics on how the optimal voting rule varies with lenders’ preferences and the borrower's accounting properties. Our model offers a rationale for the prevalent use of voting rule clauses in syndicated loan contracts.