Interaction between financial risk measures and machine learning methods

Jun-ya Gotoh (), Akiko Takeda and Rei Yamamoto

Computational Management Science, 2014, vol. 11, issue 4, 365-402

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 $$\nu $$ 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 $$\ell _1$$ ℓ 1 -regularization. Numerical examples demonstrate how the developed methods work for bond rating. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: $$\nu $$ ν -Support vector machine ( $$\nu $$ ν -SVM); Conditional value-at-risk (CVaR); Mean-absolute semi-deviation (MASD); Coherent measures of risk; Credit rating; 62H30; 62P05; 90C90; 91B28; 91B30; 91G40 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10287-013-0175-5

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