In this thesis, two behavioral asset pricing factors proposed by Daniel, Hirshleifer, and Sun (2017) are analysed. The financing factor (FIN) captures long-horizon mispricing while the post-earnings announcement drift factor (PEAD) captures short-horizon mispricing. By supplementing the risk-based market index with these two behavior-based factors, the authors create a sparse model that outperforms prominent multi-factor models in explaining a large set of market anomalies in the United States. The goal of this thesis is to construct these factors for the United States and the Great Britain stock markets. Another goal is to examine the behavioral model’s ability to explain the timee-series of asset returns compared to other factor models. To this end, the multivariate F-test developed by Gibbons, Ross, and Shanken (1989) is applied to a set of factor models in both stock markets. Furthermore, a comparative analysis of all included factors is provided. In both stock markets, the behavioural factors earn significant profits that are not captured by other factor models. The result from the original paper, that the behavioral factors successfully price other traded factors could only partially be confirmed for Great Britain, since the momentum effect is left unexplained by FIN and PEAD. In both stock markets, the behavioural model outperforms the traditional Fama-French three-factor model and Carhart’s four- factor model in explaining returns on different test asset portfolios. In the United States, the behavioral factor model competes with the Fama-French five-factor model in explaining different portfolio returns.
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In this thesis, two behavioral asset pricing factors proposed by Daniel, Hirshleifer, and Sun (2017) are analysed. The financing factor (FIN) captures long-horizon mispricing while the post-earnings announcement drift factor (PEAD) captures short-horizon mispricing. By supplementing the risk-based market index with these two behavior-based factors, the authors create a sparse model that outperforms prominent multi-factor models in explaining a large set of market anomalies in the United States....
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