Nerissa C. Brown

Mobile Communication and Local Information Flow: Evidence from Distracted Driving Laws: mobile communication and local information flow

We examine the influence of mobile communication on local information flow and local investor activity using the enforcement of statewide distracted driving restrictions, which are exogenous events that constrain mobile communication while driving. By restricting mobile communication across a potentially sizable set of local individuals, these restrictions could inhibit local information flow and, in turn, the market activity of stocks headquartered in enforcement states. We first document a decline in Google search activity for local stocks when restrictions take effect, suggesting that constraints on mobile communication significantly affect individuals’ information search activity. We further find significant declines in local trading volume when restrictions are enforced. This drop in liquidity is (1) attenuated when laws provide substitutive means of mobile communication and (2) magnified when locals have long car commutes and when their daily commutes overlap with regular exchange hours. Moreover, trading volume suffers the most for local stocks with lower institutional ownership, less analyst coverage, and more intangible information. Additional analyses show lower intraday volume during local commute times when mobile connectivity is constrained. Together, our results suggest that local information and local investors matter in stock markets and that mobile communication is an important mechanism through which these elements operate to affect liquidity and price discovery.

Non-GAAP Images, Press Release Prominence, and Investors’ Reliance on Non-GAAP Earnings

Firms are increasingly disseminating images on social media that display customized earnings measures (“non-GAAP images”). This practice falls outside the scope of mandatory disclosure rules on non-GAAP prominence in earnings releases and SEC filings. Using an experiment, we isolate this unexplored regulatory gap and investigate how non-GAAP images disseminated on social media and text-based prominence in hyperlinked earnings releases interact to influence investors’ reliance on non-GAAP earnings. Results indicate that, when the firm tweets an image featuring non-GAAP earnings, investors rely more on non-GAAP earnings even when GAAP earnings is prominent in a hyperlinked earnings release. Thus, a non-GAAP image tweet overrides the prominent placement of GAAP earnings in the earnings release. However, no such overriding effect occurs when non-GAAP earnings is tweeted in a plain text format. Supplemental experiments confirm that images operate as a distinctive prominence tool that differentially influences investors compared to traditional text-based prominence.

Non-GAAP Earnings Disclosure and IPO Pricing

We investigate the disclosure and prominence of non-GAAP earnings metrics in IPO prospectuses and how these disclosures affect IPO valuation. In contrast to already-public firms, we find an inverted U-shaped relation between IPO firms’ GAAP performance and the likelihood that they will disclose a non-GAAP metric, suggesting differing motivations for non-GAAP disclosure in the IPO setting. Our valuation tests indicate that IPO firms disclosing non-GAAP earnings metrics generally exhibit higher offer values and less undervaluation during the IPO process and that the disclosure of adjusted earnings information in the prospectus enables them to minimize undervaluation by economically significant amounts. We find, however, that these valuation effects depend on how issuers calculate the non-GAAP figure. Specifically, our results indicate that IPOs are more undervalued when prospectuses contain non-GAAP metrics with larger recurring exclusions (which are less justifiable and generally viewed to be more aggressive). Additional analyses of post-IPO stock returns suggest that aggressive recurring exclusions are appropriately discounted during the IPO process.

What are You Saying? Using Topic to Detect Financial Misreporting

Detection models of financial misreporting have evolved beyond basic quantitative or financial measures to include textual or linguistic characteristics of firms’ disclosures. While these textual analysis methods provide incremental power in identifying misreporting, they examine how content is being disclosed as opposed to what is being disclosed. This study introduces a novel fraud-detection measure, labeled as “topic,” that quantifies the thematic content of financial statements. We derive our measure from a Bayesian topic modeling methodology called Latent Dirichlet Allocation (LDA). We then demonstrate the incremental predictive power of our topic measure in detecting intentional financial misreporting. We identify occurrences of financial misreporting using SEC enforcement actions (AAERs) and restatements arising from intentional misapplications of GAAP (i.e., irregularities). We find strong evidence that topic predicts intentional misreporting beyond financial and textual style characteristics. Furthermore, our results indicate that the detection power of financial metrics is subsumed by our topic measure in prediction models for both AAERs and restatements arising from irregularities.