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

Jae Joon Ahn, Kyong Joo Oh, Tae Yoon Kim, Dong Ha Kim

Research output: Contribution to journalArticle

17 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2966-2973
Number of pages8
JournalExpert Systems with Applications
Volume38
Issue number4
DOIs
Publication statusPublished - 2011 Apr 1

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Alarm systems
Support vector machines
Deterioration
Classifiers
Economics
Monitoring

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

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Usefulness of support vector machine to develop an early warning system for financial crisis. / Ahn, Jae Joon; Oh, Kyong Joo; Kim, Tae Yoon; Kim, Dong Ha.

In: Expert Systems with Applications, Vol. 38, No. 4, 01.04.2011, p. 2966-2973.

Research output: Contribution to journalArticle

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