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dc.contributor.authorGreve, Henrik Andreas
dc.contributor.authorMaseng, Ivar Gjerstad
dc.date.accessioned2020-11-11T12:49:10Z
dc.date.available2020-11-11T12:49:10Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11250/2687406
dc.descriptionMasteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2020en_US
dc.description.abstractThroughout the years, hundreds of factors have been proposed to forecast stock returns. Cochrane (2011) referred to these factors as the "zoo of new factors." In this thesis, we consider 62 of these factors and analyze which of them provide incremental value when forecasting stock return in 12 U.S industries. We apply the Adaptive Group Lasso (AGL) method for model selection described by Freyberger, Neuhierl, and Weber (2018), and use the Classical Linear Regression Model (CLRM) as a benchmark. The AGL selects, on average, approximately three characteristics, while the linear approach selects 24. The results indicate that the AGL approach generates more accurate predictions when the sample size increases compared to the CLRM. Our analysis indicates that there is no superior method for model selection in our samples.en_US
dc.language.isoengen_US
dc.publisherHandelshøyskolen BIen_US
dc.subjectfinansen_US
dc.subjectfinanceen_US
dc.titleSelecting characteristics using the Adaptive Group Lasso on U.S. industriesen_US
dc.typeMaster thesisen_US


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