We examine how accounting transparency and investor base jointly affect financial analysts' expectations of mispricing (i.e., expectations of stock price deviations from fundamental value). Within a range of transparency, these two factors interactively amplify analysts' expectations of mispricing—analysts expect a larger positive deviation when a firm's disclosures more transparently reveal income‐increasing earnings management and the firm's most important investors are described as transient institutional investors with a shorter‐term horizon (low concentration in holdings, high portfolio turnover, and frequent momentum trading) rather than dedicated institutional investors with a longer‐term horizon (high concentration in holdings, low portfolio turnover, and little momentum trading). Results are consistent with analysts anticipating that transient institutional investors are more likely than dedicated institutional investors to adjust their trading strategies for near‐term factors affecting stock mispricings. Our theory and findings extend the accounting disclosure literature by identifying a boundary condition to the common supposition that disclosure transparency necessarily mitigates expected mispricing, and by providing evidence that analysts' pricing judgments are influenced by their anticipation of different investors' reactions to firm disclosures.

We use experimental markets to examine how pushing investment information and the value relevance of that information interact to influence investors’ value estimate accuracy and market price efficiency. Developments in technology allow information to be pushed to investors anytime and anywhere. However, in addition to value‐relevant information, pushed information often includes information that is irrelevant for assessing firm value. Drawing on psychology theory, we find that pushing information has divergent effects depending on the value relevance of the information. Pushing only value‐relevant information increases investors’ processing of the information and leads to more accurate value estimates and market prices than when not pushed. In contrast, pushing a mix of value‐relevant and value‐irrelevant information reduces investors’ processing of value‐relevant information, leading to less accurate value estimates and market prices due to poorer acquisition and integration of information than when not pushed or when only value‐relevant information is pushed. Collectively, our results reveal a dark side to push technologies, particularly with the growing presence of value‐irrelevant information.

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic ) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10‐K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10‐K filing amendments. Our out‐of‐sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.

We examine how CEOs can facilitate the development of investor trust that helps mitigate the effects of negative information. Results from an experiment show that investors trust the CEO more and are more willing to invest in the firm when the CEO communicates firm news followed by a negative earnings surprise through a personal Twitter account than when the news and surprise comes from the CEO via a website or from the firm's Investor Relations Twitter account or website. A follow‐up experiment shows that repeating the negative news does not incrementally affect investors who received the news from the CEO's Twitter account, but does further negatively impact investors who received the news via other disclosure mediums, especially those who received the news via the Investor Relations Twitter account. Our results have implications for firms and executives considering the costs and benefits of communicating with investors via Twitter.

An enduring issue in financial reporting is whether and how salient summary measures of firm performance (“earnings metrics”) affect market price efficiency. In laboratory markets, we test the effects of salient earnings metrics, which vary in how they combine persistent and transitory elements, on investor information search, beliefs about value, offers to trade, and market price efficiency. We find that including transitory elements in salient earnings metrics causes traders to search unnecessarily for further information about these elements and to overestimate their effect on fundamental value relative to a rational benchmark. In contrast, separately displaying persistent elements in earnings increases the accuracy of traders’ value estimates. Prices generally reflect traders’ beliefs about value, and prices are most efficient when transitory elements are excluded from earnings metrics entirely. Our study contributes to research on salience effects in financial reporting by showing that including transitory elements in salient earnings metrics causes inefficient information search and biased beliefs about value that can aggregate to affect market prices. We also contribute to research in experimental markets by showing that redundant disclosure is not always beneficial; redundant disclosure of transitory earnings elements, in particular, appears to have negative consequences for investor behavior and market efficiency.

We examine the influence of investor sentiment on managers’ discretionary disclosure of “pro forma” (adjusted) earnings metrics in earnings press releases. We find that managers’ propensity to disclose an adjusted earnings metric (especially one that exceeds the GAAP earnings number) increases with the level of investor sentiment. Furthermore, our analyses suggest that, as investor sentiment increases, managers: (1) exclude higher levels of both recurring and nonrecurring expenses in calculating the pro forma earnings number and (2) emphasize the pro forma figure by placing it more prominently within the earnings press release. Additional analyses indicate that the association between investor sentiment and managers’ pro forma disclosure decisions at least partly reflects opportunistic motives. Finally, we find that managers’ own sentiment‐driven expectations also play a role in their pro forma disclosure decisions.