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Performance of Asset Pricing Models in the Nordic Stock Markets

Abstract
This study aims to investigate the performance of four different asset pricing models, the Fama and French (1993) three factor model, the Carhart (1997) four factor model, the Fama and French (2015) five factor model, and the Hou et al. (2015) model, in the Nordic stock markets. I examine whether the Fama and French (2015) five factor model and the Hou et al. (2015) model outperform the other two models, in describing the variation in average stock returns. This is done by running time-series regressions and Gibbons, Ross, and Shanken (1989) tests for different combinations of portfolios, on these models. I also investigate whether there is a possibility to form a hybrid model that outperforms all the four models tested in this paper, by using a combination of the factors from the models. This is done by using Principal Component Analysis to pick the best factors to include in the hybrid model. I implement my analysis on a sample of all stocks traded on the four major Nordic stock markets (OMX Stockholm, OMX Copenhagen, OMX Helsinki, and Oslo Bors) in the period between July 1993 and June 2018. The main finding is that both the Carhart (1997) four factor model and the Hou et al. (2015) model outperform the two Fama and French (1993, 2015) models in explaining the variation in average stock returns on the Nordic stock markets. I also find evidence for two different seven factor hybrid models that outperform all the other four models in explaining the variation in average stock returns on the Nordic stock markets. These two seven factor models both include factors for market return, firm size, book-to-equity ratio, operating profitability, investment, return-on-equity, and momentum, all constructed based on Nordic stock data.
Degree
Master 2-years
Other description
MSc in Finance
URI
http://hdl.handle.net/2077/60877
Collections
  • Master theses
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gupea_2077_60877_1.pdf (1.080Mb)
Date
2019-07-02
Author
Olsson, Jonas
Series/Report no.
Master Degree Project
2019:147
Language
eng
Metadata
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