Machine learning algorithm selection for forecasting behavior of global institutional investors

Jae Joon Ahn, Suk Jun Lee, Kyong Joo Oh, Tae Yoon Kim, Hyoung Yong Lee, Min Sik Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recently Son et al. [32] proposed early warning system (EWS) monitoring the behaviors of global institutional investors (GII) against their possible massive pullout from the local emerging stock market. They used machine learning algorithm for lag l classifier to forecast the behavior of GII. The main aim of this article is to implement various machine learning algorithms in constructing the EWS and to compare their performances to select the proper one. Our results address various important issues for machine learning forecasting problem. In particular, a proper machine learning algorithm will be recommended for both long term and short term forecasting. This is empirically studied for the Korean stock market.

Original languageEnglish
Title of host publicationProceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS
DOIs
Publication statusPublished - 2009 Apr 3
Event42nd Annual Hawaii International Conference on System Sciences, HICSS - Waikoloa, HI, United States
Duration: 2009 Jan 52009 Jan 9

Publication series

NameProceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS

Other

Other42nd Annual Hawaii International Conference on System Sciences, HICSS
CountryUnited States
CityWaikoloa, HI
Period09/1/509/1/9

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

Cite this

Ahn, J. J., Lee, S. J., Oh, K. J., Kim, T. Y., Lee, H. Y., & Kim, M. S. (2009). Machine learning algorithm selection for forecasting behavior of global institutional investors. In Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS [4755460] (Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS). https://doi.org/10.1109/HICSS.2009.297