Usefulness of artificial neural networks for early warning system of economic crisis

Authors:
Tae Yoon Kim;Kyong Joo Oh;Insuk Sohn;Changha Hwang
Affiliations:
Department of Statistics, Keimyung University, Daegu 704-701, South Korea;Division of Business Administration, Hansung University, 389, 3-Ga, Samsun-Dong, Sungbuk-Gu, Seoul 136-792, South Korea;Department of Statistics, Korea University, Seoul 136-701, South Korea;Department of Statistical Information, Catholic University of Daegu, Daegu 713-702, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2004

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Abstract

During the 1990s, the economic crises in many parts of the world have sparked a need in building early warning system (EWS) which produces signal for possible crisis, and accordingly various EWSs have been established. In this paper, we focus on an interesting issue: 'How to train EWS?' To study this, various aspects of the training data (i.e. the past crisis related data) will be discussed and then several data mining classifiers including artificial neural networks (ANN) will be probed as a training tool for EWS. To emphasize empirical side of the problem, EWS for Korean economy is to be constructed. Our investigation suggests that ANN may be quite competitive in building EWS over other data mining classifiers.