Usefulness of support vector machine to develop an early warning system for financial crisis

Authors:
Jae Joon Ahn;Kyong Joo Oh;Tae Yoon Kim;Dong Ha Kim
Affiliations:
Department of Information and Industrial Engineering, Yonsei University, 134, Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea;Department of Information and Industrial Engineering, Yonsei University, 134, Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea;Department of Statistics, Keimyung University, Daegu 704-701, South Korea;Department of Information and Industrial Engineering, Yonsei University, 134, Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Oh, Kim, and Kim (2006a), Oh, Kim, Kim, and Lee (2006b) proposed a classification approach for building an early warning system (EWS) against potential financial crises. This EWS classification approach has been developed mainly for monitoring daily financial market against its abnormal movement and is based on the newly-developed crisis hypothesis that financial crisis is often self-fulfilling because of herding behavior of the investors. This article extends the EWS classification approach to the traditional-type crisis, i.e., the financial crisis is an outcome of the long-term deterioration of the economic fundamentals. It is shown that support vector machine (SVM) is an efficient classifier in such case.