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

Research output: Chapter in book/report/conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationBig Data Analysis and Deep Learning Applications - Proceedings of the 1st International Conference on Big Data Analysis and Deep Learning, 2018
EditorsThi Thi Zin, Jerry Chun-Wei Lin
PublisherSpringer Verlag
Pages3-8
Number of pages6
ISBN (Print)9789811308680
DOIs
Publication statusPublished - 2019
Event1st International Conference on Big Data Analysis and Deep Learning, ICBDL 2018 - Miyazaki, Japan
Duration: 14 May 201815 May 2018

Publication series

NameAdvances in Intelligent Systems and Computing
Volume744
ISSN (Print)2194-5357

Conference

Conference1st International Conference on Big Data Analysis and Deep Learning, ICBDL 2018
Country/TerritoryJapan
CityMiyazaki
Period14/05/1815/05/18

Scopus Subject Areas

  • Control and Systems Engineering
  • Computer Science(all)

User-Defined Keywords

  • Artificial neural network
  • Data-driven
  • Evolutionary
  • Genetic algorithm
  • Prediction
  • Stock

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