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

doi:10.1109/HICSS.2009.297

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

Department of Industrial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (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 language	English
Title of host publication	Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS
DOIs	https://doi.org/10.1109/HICSS.2009.297
Publication status	Published - 2009
Event	42nd Annual Hawaii International Conference on System Sciences, HICSS - Waikoloa, HI, United States Duration: 2009 Jan 5 → 2009 Jan 9

Publication series

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

Other

Other	42nd Annual Hawaii International Conference on System Sciences, HICSS
Country/Territory	United States
City	Waikoloa, HI
Period	09/1/5 → 09/1/9

All Science Journal Classification (ASJC) codes

Computer Science Applications
Information Systems

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10.1109/HICSS.2009.297

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

@inproceedings{cde7291906ce49718f5c6c67b00ec1e1,

title = "Machine learning algorithm selection for forecasting behavior of global institutional investors",

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.",

author = "Ahn, {Jae Joon} and Lee, {Suk Jun} and Oh, {Kyong Joo} and Kim, {Tae Yoon} and Lee, {Hyoung Yong} and Kim, {Min Sik}",

year = "2009",

doi = "10.1109/HICSS.2009.297",

language = "English",

isbn = "9780769534503",

series = "Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS",

booktitle = "Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS",

note = "42nd Annual Hawaii International Conference on System Sciences, HICSS ; Conference date: 05-01-2009 Through 09-01-2009",

}

Ahn, JJ, Lee, SJ, Oh, KJ, Kim, TY, Lee, HY & Kim, MS 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, 42nd Annual Hawaii International Conference on System Sciences, HICSS, Waikoloa, HI, United States, 09/1/5. https://doi.org/10.1109/HICSS.2009.297

Machine learning algorithm selection for forecasting behavior of global institutional investors. / Ahn, Jae Joon; Lee, Suk Jun; Oh, Kyong Joo et al.

Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS. 2009. 4755460 (Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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Ahn JJ, Lee SJ, Oh KJ, Kim TY, Lee HY, Kim MS. Machine learning algorithm selection for forecasting behavior of global institutional investors. In Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS. 2009. 4755460. (Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS). doi: 10.1109/HICSS.2009.297

Machine learning algorithm selection for forecasting behavior of global institutional investors

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