Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment

Henry S.Y. Tang; Jean H Y LAI

doi:10.1007/978-981-13-0869-7_1

Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment

Henry S.Y. Tang^*, 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

Predicting stock price is a challenging problem as the market involve multi-agent activities with constantly changing environment. We propose a method of constrained evolutionary (CE) scheme that based on Genetic Algorithm (GA) and Artificial Neural Network (ANN) for stock price prediction. Stock market continuously subject to influences from government policy, investor activity, cooperation activity and many other hidden factors. Due to dynamic and non-linear nature of the market, individual stock price movement are usually hard to predict. Investment strategies used by regular investor usually require constant modification, remain secrecy and sometimes abandoned. One reason for such behavior is due to dynamic structure of the efficient market, where all revealed information will reflect upon the stock price, leads to dynamic behavior of the market and unprofitability of the static strategies. The CE scheme contains mechanisms which are temporal and environmental sensitive that triggers evolutionary changes of the model to create a dynamic response towards external factors.

Original language	English
Title of host publication	Big Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018
Editors	Thi Thi Zin, Jerry Chun-Wei Lin
Publisher	Springer Verlag
Pages	3-8
Number of pages	6
ISBN (Print)	9789811308680
DOIs	https://doi.org/10.1007/978-981-13-0869-7_1
Publication status	Published - 7 Jun 2018
Event	1st International Conference on Big Data Analysis and Deep Learning, ICBDL 2018 - Miyazaki, Japan Duration: 14 May 2018 → 15 May 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	744
ISSN (Print)	2194-5357

Conference

Conference	1st International Conference on Big Data Analysis and Deep Learning, ICBDL 2018
Country/Territory	Japan
City	Miyazaki
Period	14/05/18 → 15/05/18

Scopus Subject Areas

Control and Systems Engineering
General Computer Science

User-Defined Keywords

Artificial neural network
Data-driven
Evolutionary
Genetic algorithm
Prediction
Stock

Access to Document

10.1007/978-981-13-0869-7_1

Cite this

Tang, H. S. Y., & LAI, J. H. Y. (2018). Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment. In T. T. Zin, & J. C.-W. Lin (Eds.), Big Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018 (pp. 3-8). (Advances in Intelligent Systems and Computing; Vol. 744). Springer Verlag. https://doi.org/10.1007/978-981-13-0869-7_1

Tang, Henry S.Y. ; LAI, Jean H Y. / Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment. Big Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018. editor / Thi Thi Zin ; Jerry Chun-Wei Lin. Springer Verlag, 2018. pp. 3-8 (Advances in Intelligent Systems and Computing).

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title = "Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment",

abstract = "Predicting stock price is a challenging problem as the market involve multi-agent activities with constantly changing environment. We propose a method of constrained evolutionary (CE) scheme that based on Genetic Algorithm (GA) and Artificial Neural Network (ANN) for stock price prediction. Stock market continuously subject to influences from government policy, investor activity, cooperation activity and many other hidden factors. Due to dynamic and non-linear nature of the market, individual stock price movement are usually hard to predict. Investment strategies used by regular investor usually require constant modification, remain secrecy and sometimes abandoned. One reason for such behavior is due to dynamic structure of the efficient market, where all revealed information will reflect upon the stock price, leads to dynamic behavior of the market and unprofitability of the static strategies. The CE scheme contains mechanisms which are temporal and environmental sensitive that triggers evolutionary changes of the model to create a dynamic response towards external factors.",

keywords = "Artificial neural network, Data-driven, Evolutionary, Genetic algorithm, Prediction, Stock",

author = "Tang, {Henry S.Y.} and LAI, {Jean H Y}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 1st International Conference on Big Data Analysis and Deep Learning, ICBDL 2018 ; Conference date: 14-05-2018 Through 15-05-2018",

year = "2018",

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language = "English",

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Tang, HSY & LAI, JHY 2018, Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment. in TT Zin & JC-W Lin (eds), Big Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018. Advances in Intelligent Systems and Computing, vol. 744, Springer Verlag, pp. 3-8, 1st International Conference on Big Data Analysis and Deep Learning, ICBDL 2018, Miyazaki, Japan, 14/05/18. https://doi.org/10.1007/978-981-13-0869-7_1

Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment. / Tang, Henry S.Y.; LAI, Jean H Y.
Big Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018. ed. / Thi Thi Zin; Jerry Chun-Wei Lin. Springer Verlag, 2018. p. 3-8 (Advances in Intelligent Systems and Computing; Vol. 744).

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

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AU - Tang, Henry S.Y.

AU - LAI, Jean H Y

PY - 2018/6/7

Y1 - 2018/6/7

N2 - Predicting stock price is a challenging problem as the market involve multi-agent activities with constantly changing environment. We propose a method of constrained evolutionary (CE) scheme that based on Genetic Algorithm (GA) and Artificial Neural Network (ANN) for stock price prediction. Stock market continuously subject to influences from government policy, investor activity, cooperation activity and many other hidden factors. Due to dynamic and non-linear nature of the market, individual stock price movement are usually hard to predict. Investment strategies used by regular investor usually require constant modification, remain secrecy and sometimes abandoned. One reason for such behavior is due to dynamic structure of the efficient market, where all revealed information will reflect upon the stock price, leads to dynamic behavior of the market and unprofitability of the static strategies. The CE scheme contains mechanisms which are temporal and environmental sensitive that triggers evolutionary changes of the model to create a dynamic response towards external factors.

AB - Predicting stock price is a challenging problem as the market involve multi-agent activities with constantly changing environment. We propose a method of constrained evolutionary (CE) scheme that based on Genetic Algorithm (GA) and Artificial Neural Network (ANN) for stock price prediction. Stock market continuously subject to influences from government policy, investor activity, cooperation activity and many other hidden factors. Due to dynamic and non-linear nature of the market, individual stock price movement are usually hard to predict. Investment strategies used by regular investor usually require constant modification, remain secrecy and sometimes abandoned. One reason for such behavior is due to dynamic structure of the efficient market, where all revealed information will reflect upon the stock price, leads to dynamic behavior of the market and unprofitability of the static strategies. The CE scheme contains mechanisms which are temporal and environmental sensitive that triggers evolutionary changes of the model to create a dynamic response towards external factors.

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Tang HSY, LAI JHY. Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment. In Zin TT, Lin JCW, editors, Big Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018. Springer Verlag. 2018. p. 3-8. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-981-13-0869-7_1

Data-driven constrained evolutionary scheme for predicting price of individual stock in dynamic market environment

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Scopus Subject Areas

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