This dissertation consists of two essays on predictability of asset prices. "Benchmarking problems and long horizon abnormal returns" and, "Low R-square in the cross section of expected returns". Long run abnormal returns following Initial Public Offerings (IPOs), Seasoned Equity Offers (SEO) and other firm level events are well documented in the finance literature. These findings are difficult to reconcile in an efficient markets world. I examine the seriousness of potential benchmarking errors on the measurement of abnormal returns. I find that the simpler, more parsimonious models perform better in practice and finds that excess performance is not predictable regardless of the APM. Thus, the long run underperformance following SEOs found in the literature is consistent with market efficiency because excess performance itself is not predictable. In the other essay, "Low R-square in the cross section of expected returns", I examine the “low R-square†phenomenon observed in the literature. CAPM predicts exact linear relationship between return and betas (SML). This means that estimated time series betas for firms should be related with firms' future returns. However, the estimated betas have almost no relationship with future returns. The cross-sectional R2 are surprising low (3% average) while time series R2 are higher (around 30 % average). He develops a simple asset pricing model that explains this phenomenon. Even in a perfect world where there are no errors in the benchmark measurement or estimation of the price of market risk the difference in R-squares can be quite large due to the difference in variance between the "market" and average returns. I document that market variance exceeds the variance of average returns, with few exceptions, for the last 74 years.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-2061 |
| Date | 18 May 2007 |
| Creators | Sanchez, Benito |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
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