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

Il Suh Son, Kyong Joo Oh, Tae Yoon Kim, Dong Ha Kim

Research output: Contribution to journalArticle

14 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4951-4957
Number of pages7
JournalExpert Systems with Applications
Volume36
Issue number3 PART 1
DOIs
Publication statusPublished - 2009 Jan 1

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Alarm systems
Learning systems
Sales
Expert systems
Learning algorithms
Financial markets

All Science Journal Classification (ASJC) codes

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

Cite this

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An early warning system for global institutional investors at emerging stock markets based on machine learning forecasting. / Son, Il Suh; Oh, Kyong Joo; Kim, Tae Yoon; Kim, Dong Ha.

In: Expert Systems with Applications, Vol. 36, No. 3 PART 1, 01.01.2009, p. 4951-4957.

Research output: Contribution to journalArticle

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