Essays on behavioral finance
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] This dissertation consists of three essays on behavioral finance. In the first essay, we proposes a new daily market-level investor sentiment index (Photo Pessimism) constructed from photos in news media. We apply a machine learning technique to classify perceived sentiment from a large sample of photos and study how sentiment in these photos relates to market activities. Between 1926 and 2018, we show that Photo Pessimism is able to predict market return-reversal and increase in abnormal trading volume. We document that the return predictability pattern is concentrated among stocks with high limits to arbitrage and during periods of high uncertainty and low investor distraction. ... In the third essay, we examine whether information in mutual fund advertisements is valuable and how investors respond to such information. Motivated by Nelson (1970, 1974) and Kihlstrom and Riordan (1984), we find that advertising expenditure is associated with significant increase in three-factor (18.9 bps) and four-factor (19.4 bps)alphas in the subsequent year. This relation is primarily driven by mutual fund advertisements that contain information on past relative performance. Generally, the positive relation between advertising expenditure and fund flows is not sensitive to the content of advertisements with one exception-during recessions, the inclusion of information about past performance or fees in advertisements is associated with capital outflows. This evidence is consistent with the coarse thinking model (Mullainathan et al., 2008).
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