• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • View Item
  •   Home
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Predicting corporate bond returns in the US bond market via machine learning

Storihle, Fredrik; Støylen, Erlend L.
Master thesis
Thumbnail
View/Open
masterthesis.pdf (1.615Mb)
URI
https://hdl.handle.net/11250/3017989
Date
2022
Metadata
Show full item record
Collections
  • Master Thesis [4657]
Abstract
We perform a comparative analysis of two machine learning methods to predict corporate

bond return in the US bond market. In contrast to previous studies, we find that the most

influential variables are associated with size risk and past return. However, credit and liquidity

risks are more prominent when negative externalities impact the market. Further, high

predictability at short horizons combined with the investment strategy employed translates

into highly significant alphas. We identify the best-performing method to be a decisiontree-

based model utilizing boosting. The out-of-sample performance for this method remains

statistically significant after accounting for transaction costs.

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