Essays in accounting and behaviorial finance [electronic resource]

This dissertation consists of three distinct chapters. In the first chapter, "Information or Spin? Evidence from Language Differences Between 8-Ks and Press Releases" I examine whether investors correctly distinguish qualitative information from promotional language in press releases related to material events of US public firms. For a variety of material events, firms are required to issue a Form 8-K, but 37% of the time also voluntarily issue a press release concerning the same event. Using textual analysis, I find that firms are more likely to issue a press release if the underlying 8-K tone is positive, and that tonal differences between the 8-K and the press release are driven in part by quotes from firm officers. I also find economically significant responses in firms' stock returns to tonal language in the 8-K, as well as to tonal differences between the two disclosures. To verify whether my strategy of comparing the press release against the 8-K is isolating the effects of promotional language or additional information, I test and find evidence of an initial positive reaction but a subsequent negative drift from positively toned press releases. This evidence implies that investors may have initially responded to both information and spin. Nominating investor inattention as a possible mechanism for overreaction, I use novel search traffic micro-data from the SEC EDGAR website and detect lower 8-K search intensity in the presence of a press release. Together, my results are consistent with some investors overestimating the degree of substitutability between the two disclosures and thus failing to readjust expectations accordingly. In the next chapter "Identifying Peer Firms: Evidence from EDGAR Search Traffic", using Internet traffic patterns from the Securities and Exchange Commission Electronic Data-Gathering, Analysis, and Retrieval (EDGAR) website, we show that firms appearing in chronologically adjacent searches by the same individual are fundamentally similar on multiple dimensions. In fact, traffic-based peer firms identified by our algorithm significantly outperform peer firms based on six-digit Global Industry Classification Standard (GICS) groupings in explaining cross-sectional variations in base firms' stock returns, valuation multiples, forecasted and realized growth rates, iv research and development expenditures, and various other key financial ratios. Our results highlight the usefulness of EDGAR data, as well as the latent intelligence in search traffic patterns. The last chapter, "Who is More Behaviorial- Investor or Manager? Evidence from Stock Ticker Symbol Assignment in China", explores stock ticker symbol assignment in China and finds strong evidence of non-random assignment of digits that are the subject of superstition in several Asian countries. I explore two explanations: manager superstition, and pandering by rational managers to investor sentiment during the IPO process. Leveraging a policy switch to random digit assignment at one of the two main exchanges in China, I find no difference in IPO returns to superstitious tickers which implies that investors do not causally respond to superstition. This is in contrast to correlated results in the non-randomly assigned exchange and also in the pre-randomization period, which may be driven by firm unobservables such as access to political connections. In addition, I find firms which are cross listed in both the local and foreign investor markets in China to exhibit similar numerical preferences in both markets, despite the fact that foreign investors should not hold the same beliefs. Jointly, these results identify manager's personal preferences as the source of the behavioral bias towards superstitious ticker symbol assignment, rather than manager higher order beliefs about investors. Finally, I provide descriptive evidence linking these preferences to financial disclosure behavior such as clustering of earnings announcements.

Keywords: Economics.
Advisor: Lee Charles M. C. primary advisor. Thesis advisor
Shoven John B. primary advisor. Thesis advisor
Larcker David F. advisor. Thesis advisor