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 language | English |
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Pages (from-to) | 365-402 |
Number of pages | 38 |
Journal | Computational Management Science |
Volume | 11 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2014 Sep 27 |
Externally published | Yes |
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