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dc.contributor.authorLønø, Bjørn Erik
dc.contributor.authorSvendsen, Christoffer Erevik
dc.date.accessioned2019-10-10T13:45:53Z
dc.date.available2019-10-10T13:45:53Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11250/2621449
dc.descriptionMasteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2019nb_NO
dc.description.abstractAbstract This master thesis tests and evaluates different asset pricing models for the Norwegian stock market. The models are made to explain the cross-section of expected stock returns, and we apply them to real-world data and compare their performance. This paper applies four models to the Norwegian stock market; CAPM, Fama and French Three-Factor Model (FF3), Fama and French Five-Factor Model (FF5), and the Carhart Four-Factor Model (C4). We evaluate their performance using the Fama and MacBeth (1973) procedure with both time-series and cross-sectional regressions and compare the models based on intercept analysis, explanatory power, and stability in results. The purpose of the comparison is to find a superior model that should be applied when analysing the Norwegian stock market. The Fama-French three-factor model is the most preferred amongst our models. We find no evidence that adding more factors, either the Momentum or the RMW and CMA factor, explain the cross-section of expected returns better than the three-factor model. Further, all models yield a significant intercept which entails that the models are missing priced risk factors for the Norwegian stock market. Other models with different risk factors should, therefore, be considered when conduction analysis in the Norwegian market. However, we find that the Fama- French three-factor model is a relatively stable and applicable model.nb_NO
dc.language.isoengnb_NO
dc.publisherHandelshøyskolen BInb_NO
dc.subjectfinansnb_NO
dc.subjectfinancenb_NO
dc.subjectfinancialnb_NO
dc.subjecteconomicsnb_NO
dc.titleA comparison of Asset Pricing Models in the Norwegian Stock Marketnb_NO
dc.typeMaster thesisnb_NO


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