Asset pricing anomalies : persistence, aggregation, and monotonicity
| dc.contributor.advisor | Sialm, Clemens | en |
| dc.creator | Maslov, Denys | en |
| dc.date.accessioned | 2014-06-23T17:57:52Z | en |
| dc.date.issued | 2014-05 | en |
| dc.date.submitted | May 2014 | en |
| dc.date.updated | 2014-06-23T17:57:53Z | en |
| dc.description | text | en |
| dc.description.abstract | In Chapter 1, I investigate whether returns of strategies based on asset pricing anomalies exhibit time series persistence which can be attributed to flow-induced trading by mutual funds. I find persistence for thirteen characteristics, which is statistically significant for five including size, corporate investment, and bankruptcy likelihood. The persistence is not explained by individual stock momentum and is not limited to certain calendar months. The return predictability can be used to construct new trading strategies, which on average earn 4.5% annually. A price pressure measure of mutual fund flow-driven trading explains a substantial part of the strategy performance persistence. In Chapter 2, we propose a new approach for estimating expected returns on individual stocks from firm characteristics. We treat expected returns as latent variables and develop a procedure that filters them out using the characteristics as signals and imposing restrictions implied by a one factor asset pricing model. The estimates of expected returns obtained by applying our method to thirteen asset pricing anomalies generate a wide cross-sectional dispersion of realized returns. Our results provide evidence of strong commonality in the anomalies. The use of portfolios based on the filtered expectations as test assets increases the power of asset pricing tests. In Chapter 3, we examine the sensitivity of fourteen asset pricing anomalies to extreme observations using robust regression methods. We find that although all anomalies except size are strong and robust for stocks with presumably low returns, most of them are sensitive to individual influential observations for stocks with presumably high returns. For some anomalies, extreme observations distort regression results for all stocks and even portfolio returns. When the impact of such observations is mitigated, eight anomalies become positively related to expected returns for stocks with low characteristics meaning that these anomalies have an inverted J-shaped form. Chapter 4 concludes by summarizing the main contributions of three chapters and their implications. | en |
| dc.description.department | Finance | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/2152/24778 | en |
| dc.language.iso | en | en |
| dc.subject | Asset Pricing Anomalies | en |
| dc.subject | Mutual Funds | en |
| dc.title | Asset pricing anomalies : persistence, aggregation, and monotonicity | en |
| dc.type | Thesis | en |
| thesis.degree.department | Finance | en |
| thesis.degree.discipline | Finance | en |
| thesis.degree.grantor | The University of Texas at Austin | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |