Predicting the Co-movement of Stocks in the Hong Kong Stock Market

Chen Chuxun; Jean H Y LAI

doi:10.1007/978-981-13-5841-8_75

Predicting the Co-movement of Stocks in the Hong Kong Stock Market

Chen Chuxun
, Jean H Y LAI^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Stock co-movement was examined in Finance research but not in the IT research. Previous studies revealed that the co-movement is usually caused by either the determinants of the stocks’ values, habitat movements between stocks, or the change in portfolio composition. Most of the studies used a statistical approach to uncover the co-movement relation between stocks. This paper takes a combination of the statistical approach and the machine learning approach to: (1) prove the existence of stock co-movement; and (2) identify a prediction model that can forecast the stock co-movement. Both supervised and unsupervised methods are used. In this study, the inter-day stock data in the real estate industry were extracted from the Yahoo finance in Hong Kong. After cleaning the data, stocks of the industry were categorized into two groups by its market capitalization. The correlation between the two trading data set is tested. Support Vector Machine (SVM) is used to train the prediction model. The predictive power of the model looks good.

Original language	English
Title of host publication	Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018
Editors	Jeng-Shyang Pan, Shih-Pang Tseng, Jerry Chun-Wei Lin, Bixia Sui
Publisher	Springer Verlag
Pages	723-734
Number of pages	12
ISBN (Print)	9789811358401
DOIs	https://doi.org/10.1007/978-981-13-5841-8_75
Publication status	Published - 12 May 2019
Event	12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018 - Changzhou, China Duration: 14 Dec 2018 → 17 Dec 2018 https://link.springer.com/book/10.1007/978-981-13-5841-8

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	834
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018
Abbreviated title	ICGEC 2018
Country/Territory	China
City	Changzhou
Period	14/12/18 → 17/12/18
Other	Conference Proceedings
Internet address	https://link.springer.com/book/10.1007/978-981-13-5841-8

User-Defined Keywords

K-means clustering
Stock Co-movement
Support vector machine

Access to Document

10.1007/978-981-13-5841-8_75

Cite this

Chuxun, C., & LAI, J. H. Y. (2019). Predicting the Co-movement of Stocks in the Hong Kong Stock Market. In J.-S. Pan, S.-P. Tseng, J. C.-W. Lin, & B. Sui (Eds.), Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018 (pp. 723-734). (Advances in Intelligent Systems and Computing; Vol. 834). Springer Verlag. https://doi.org/10.1007/978-981-13-5841-8_75

Chuxun, Chen ; LAI, Jean H Y. / Predicting the Co-movement of Stocks in the Hong Kong Stock Market. Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. editor / Jeng-Shyang Pan ; Shih-Pang Tseng ; Jerry Chun-Wei Lin ; Bixia Sui. Springer Verlag, 2019. pp. 723-734 (Advances in Intelligent Systems and Computing).

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abstract = "Stock co-movement was examined in Finance research but not in the IT research. Previous studies revealed that the co-movement is usually caused by either the determinants of the stocks{\textquoteright} values, habitat movements between stocks, or the change in portfolio composition. Most of the studies used a statistical approach to uncover the co-movement relation between stocks. This paper takes a combination of the statistical approach and the machine learning approach to: (1) prove the existence of stock co-movement; and (2) identify a prediction model that can forecast the stock co-movement. Both supervised and unsupervised methods are used. In this study, the inter-day stock data in the real estate industry were extracted from the Yahoo finance in Hong Kong. After cleaning the data, stocks of the industry were categorized into two groups by its market capitalization. The correlation between the two trading data set is tested. Support Vector Machine (SVM) is used to train the prediction model. The predictive power of the model looks good.",

keywords = "K-means clustering, Stock Co-movement, Support vector machine",

author = "Chen Chuxun and LAI, \{Jean H Y\}",

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booktitle = "Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018",

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Chuxun, C & LAI, JHY 2019, Predicting the Co-movement of Stocks in the Hong Kong Stock Market. in J-S Pan, S-P Tseng, JC-W Lin & B Sui (eds), Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. Advances in Intelligent Systems and Computing, vol. 834, Springer Verlag, pp. 723-734, 12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018, Changzhou, China, 14/12/18. https://doi.org/10.1007/978-981-13-5841-8_75

Predicting the Co-movement of Stocks in the Hong Kong Stock Market. / Chuxun, Chen; LAI, Jean H Y.
Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. ed. / Jeng-Shyang Pan; Shih-Pang Tseng; Jerry Chun-Wei Lin; Bixia Sui. Springer Verlag, 2019. p. 723-734 (Advances in Intelligent Systems and Computing; Vol. 834).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Predicting the Co-movement of Stocks in the Hong Kong Stock Market

AU - Chuxun, Chen

AU - LAI, Jean H Y

PY - 2019/5/12

Y1 - 2019/5/12

N2 - Stock co-movement was examined in Finance research but not in the IT research. Previous studies revealed that the co-movement is usually caused by either the determinants of the stocks’ values, habitat movements between stocks, or the change in portfolio composition. Most of the studies used a statistical approach to uncover the co-movement relation between stocks. This paper takes a combination of the statistical approach and the machine learning approach to: (1) prove the existence of stock co-movement; and (2) identify a prediction model that can forecast the stock co-movement. Both supervised and unsupervised methods are used. In this study, the inter-day stock data in the real estate industry were extracted from the Yahoo finance in Hong Kong. After cleaning the data, stocks of the industry were categorized into two groups by its market capitalization. The correlation between the two trading data set is tested. Support Vector Machine (SVM) is used to train the prediction model. The predictive power of the model looks good.

AB - Stock co-movement was examined in Finance research but not in the IT research. Previous studies revealed that the co-movement is usually caused by either the determinants of the stocks’ values, habitat movements between stocks, or the change in portfolio composition. Most of the studies used a statistical approach to uncover the co-movement relation between stocks. This paper takes a combination of the statistical approach and the machine learning approach to: (1) prove the existence of stock co-movement; and (2) identify a prediction model that can forecast the stock co-movement. Both supervised and unsupervised methods are used. In this study, the inter-day stock data in the real estate industry were extracted from the Yahoo finance in Hong Kong. After cleaning the data, stocks of the industry were categorized into two groups by its market capitalization. The correlation between the two trading data set is tested. Support Vector Machine (SVM) is used to train the prediction model. The predictive power of the model looks good.

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KW - Support vector machine

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A2 - Tseng, Shih-Pang

A2 - Lin, Jerry Chun-Wei

A2 - Sui, Bixia

PB - Springer Verlag

T2 - 12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

Y2 - 14 December 2018 through 17 December 2018

ER -

Chuxun C, LAI JHY. Predicting the Co-movement of Stocks in the Hong Kong Stock Market. In Pan JS, Tseng SP, Lin JCW, Sui B, editors, Genetic and Evolutionary Computing - Proceedings of the 12th International Conference on Genetic and Evolutionary Computing, 2018. Springer Verlag. 2019. p. 723-734. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-13-5841-8_75

Predicting the Co-movement of Stocks in the Hong Kong Stock Market

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