TY - JOUR
T1 - Interaction between financial risk measures and machine learning methods
AU - Gotoh, Jun ya
AU - Takeda, Akiko
AU - Yamamoto, Rei
N1 - Funding Information:
The research of the first author is partly supported by a MEXT Grant-in-Aid for Young Scientists (B) 23710176. Also, the authors appreciate the comments by two anonymous referees and Dr. Pando G. Georgiev.
Publisher Copyright:
© 2013, Springer-Verlag Berlin Heidelberg.
PY - 2014/9/27
Y1 - 2014/9/27
N2 - 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.
AB - 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.
KW - Coherent measures of risk
KW - Conditional value-at-risk (CVaR)
KW - Credit rating
KW - Mean-absolute semi-deviation (MASD)
KW - ν-Support vector machine (ν-SVM)
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U2 - 10.1007/s10287-013-0175-5
DO - 10.1007/s10287-013-0175-5
M3 - Article
AN - SCOPUS:84919455614
VL - 11
SP - 365
EP - 402
JO - Computational Management Science
JF - Computational Management Science
SN - 1619-697X
IS - 4
ER -