Data‐efficient model building for financial applications

Sven Sandow and Xuelong Zhou

Journal of Risk Finance, 2007, vol. 8, issue 2, 133-155

Abstract: Purpose - Investors often rely on probabilistic models that were learned from small historical labeled datasets. The purpose of this article is to propose a new method for data‐efficient model learning. Design/methodology/approach - The proposed method, which is an extension of the standard minimum relative entropy (MRE) approach and has a clear financial interpretation, belongs to the class of semi‐supervised algorithms, which can learn from data that are only partially labeled with values of the variable of interest. Findings - This study tests the method on an artificial dataset and uses it to learn a model for recovery of defaulted debt. In both cases, the resulting models perform better than the standard MRE model, when the number of labeled data is small. Originality/value - The method can be applied to financial problems where labeled data are sparse but unlabeled data are readily available.

Keywords: Learning; Learning methods; Financial modelling; Credit (search for similar items in EconPapers)
Date: 2007
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:jrfpps:15265940710732332

DOI: 10.1108/15265940710732332

Access Statistics for this article

Journal of Risk Finance is currently edited by Nawazish Mirza

More articles in Journal of Risk Finance from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().