An early warning system for global institutional investors at emerging stock markets based on machine learning forecasting

Authors:
Il Suh Son;Kyong Joo Oh;Tae Yoon Kim;Dong Ha Kim
Affiliations:
Yonsei University, Department of Information and Industrial Engineering, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Republic of Korea;Yonsei University, Department of Information and Industrial Engineering, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Republic of Korea;Department of Statistics, Keimyung University, Daegu 704-701, Republic of Korea;Yonsei University, Department of Information and Industrial Engineering, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

At local emerging stock markets such as Korea, Hong Kong, Singapore and Taiwan, global institutional investors (GII) comprised of global mutual funds, offshore funds, and hedge funds play a key role and more often than not cause severe turmoil via massive selling. Thus, for the concerned local governments or private and institutional investors, it is quite necessary to monitor the behavior of GII against a sudden pullout. The main aim of this article is to propose an early warning system (EWS) which purposes issuing warning signal against the possible massive selling of GII at the local market. For this, we introduce machine learning algorithm which forecasts the behavior of GII by predicting future conditions. Technically, this EWS is an advanced form of the EWS developed by Oh et al. [Oh, K. J., Kim, T. Y., & Kim, C. (2006). An early warning system for detection of financial crisis using financial market volatility. Expert Systems, 23, 83-98] which issues a warning based on classifying present conditions. This study is empirically done for the Korean stock market.