Knowledge that Transforms

We analyze over 27,000 social media livestreams by Chinese mutual funds to investigate whether they achieve regulators’ goal of improving retail investment decisions. Our findings indicate that livestreams generate significant inflows, often within minutes of their start times. Yet rather than educating investors, livestreams amplify return-chasing behavior and predict sharp declines in fund performance. Investors who buy in response to livestreams would earn higher returns by holding index funds or even cash. Further analyses using deep learning algorithms reveal that livestreams are more persuasive when speakers are more physically attractive, use more positive language, and sound more excited. We conclude that livestreams primarily function as persuasive advertising and that regulators should be wary of educational efforts led by sellers of consumer financial products. We also conclude that prior evidence on the benefits of firms’ social media use in equity markets does not extend to financial product markets in this setting.

We examine whether external assurance improves the quality of firms’ carbon accounting. We develop a measure of carbon accounting quality based on the deviation of reported emissions from a model-based expected level and supplement it with two survey-based measures. We show that assurance is associated with higher carbon accounting quality. This association is stronger when firms have weaker pre-existing carbon accounting systems and when assurance is more thorough. Consistent with assurance enhancing quality, assurance relates to the identification of issues in a firm’s carbon accounting system, along with fewer omissions and more revisions of prior disclosures. Exploiting mandated assurance for non-financial reporting in three E.U. countries, we show that firms experience post-regulation increases in carbon accounting quality. Together, the findings highlight the importance of assurance for reporting firms that are improving their carbon accounting quality.

We find that nonlinear estimation of profitability decomposition frameworks yields more accurate out-of-sample profitability forecasts than forecasts from both a random walk and linear estimation. The improvements derive from nonlinear estimation and synergies between nonlinear estimation and profitability decomposition frameworks. We analyze three essential financial statement analysis design choices to provide insights for the practice of fundamental analysis and find robust evidence that higher levels of profitability decomposition, focusing on core items, and using up to three years of historical information improve forecast accuracy. We find that our forecasts predict returns and profitability changes before and after controlling for analyst forecasts and common asset pricing factors.

Taxation is a central economic policy tool, with governments increasingly using tax policy to stimulate local economic growth and also regulate multinational firms. We review the empirical literature that studies the effect of tax policies on firms’ investment, employment, and other real outcomes. Building on the neoclassical theory of corporate taxes and tangible investment, we propose an organizing framework for our review that captures the wide set of tax policies and firm responses examined in accounting research. This framework highlights four dimensions along which accounting scholars contribute to the literature: (i) documenting the role of financial reporting incentives as a moderating factor in firms’ real responses, (ii) studying firms’ reporting versus real responses, (iii) quantifying real effects of tax disclosure regulations, and (iv) improving measurement of firms’ tax status and proxies for investment and employment. We identify open questions for future research and suggest new international, federal, and local settings that may help uncover underlying mechanisms driving observed economic phenomena. Specifically, we encourage scholars to further distinguish firms’ reported and real responses to tax changes and improve measurement of these outcomes, especially in settings related to environmental taxation or settings in which tax avoidance and real outcomes are closely linked.

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading on long-run stock price informativeness. Overall, our findings indicate that a significant number of investors use ChatGPT in ways that influence their trading decisions and market outcomes. Future research can investigate the mechanisms underlying these GenAI effects and the potential risks of using GenAI for trading.

We investigate a 2012 comply-or-explain regulation implemented by China’s Shanghai Stock Exchange. The regulation requires eligible firms to pay 30% of their current-year profits as cash dividends or explain the reasons why they do not meet this requirement through a public conference call. Using firms listed on the Shenzhen Stock Exchange as a control group, our difference-in-differences estimates suggest that firms subject to the regulation decreased tunneling, irrespective of whether they complied by paying or disclosing. Further analyses suggest that the reduction in tunneling is partially attributed to enhanced regulatory monitoring over explaining firms and the constraint on excess cash of paying firms. These findings offer novel policy insights into how a flexible comply-or-explain form of regulation can mitigate agency costs between controlling and minority shareholders in a weak institutional environment.

We find that the adverse impact of bad news on analysts’ valuations is 57% larger when the CEO is Non-White, resulting in more pessimistic valuations for Non-White CEOs relative to their White counterparts. Non-White CEO firms are more likely to surpass analysts’ valuation targets in the subsequent 12 months, suggesting that this racial gap lacks economic justification. To provide further evidence of a racial bias: (1) we triangulate our empirical findings with corroborating evidence from a controlled experiment and (2) we provide evidence that analysts’ valuation disparities towards Non-White CEO firms become larger when race relations are worse. Increases in CEO familiarity attenuate these disparities, suggesting the bias we document appears to be subconscious. Our findings suggest that resources allocated towards educating a firm’s stakeholders about the potential impact of implicit racial biases and increasing self-awareness may be impactful in promoting equality within capital markets.