• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Handelshøyskolen BI
  • Student papers
  • Master of Science
  • View Item
  •   Home
  • Handelshøyskolen BI
  • Student papers
  • Master of Science
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Selecting characteristics using the Adaptive Group Lasso on U.S. industries

Greve, Henrik Andreas; Maseng, Ivar Gjerstad
Master thesis
Thumbnail
View/Open
2639406.pdf (1.892Mb)
Final version.R (14.53Kb)
URI
https://hdl.handle.net/11250/2687406
Date
2020
Metadata
Show full item record
Collections
  • Master of Science [963]
Abstract
Throughout 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.
Description
Masteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2020
Publisher
Handelshøyskolen BI

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit