Interaction between financial risk measures and machine learning methods

Jun ya Gotoh, Akiko Takeda, Rei Yamamoto

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The purpose of this article is to review the similarity and difference between financial risk minimization and a class of machine learning methods known as support vector machines, which were independently developed. By recognizing their common features, we can understand them in a unified mathematical framework. On the other hand, by recognizing their difference, we can develop new methods. In particular, employing the coherent measures of risk, we develop a generalized criterion for two-class classification. It includes existing criteria, such as the margin maximization and ν-SVM, as special cases. This extension can also be applied to the other type of machine learning methods such as multi-class classification, regression and outlier detection. Although the new criterion is first formulated as a nonconvex optimization, it results in a convex optimization by employing the nonnegative ℓ1-regularization. Numerical examples demonstrate how the developed methods work for bond rating.

Original languageEnglish
Pages (from-to)365-402
Number of pages38
JournalComputational Management Science
Volume11
Issue number4
DOIs
Publication statusPublished - 2014 Sep 27
Externally publishedYes

Keywords

  • Coherent measures of risk
  • Conditional value-at-risk (CVaR)
  • Credit rating
  • Mean-absolute semi-deviation (MASD)
  • ν-Support vector machine (ν-SVM)

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems

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