Article

Stock prediction based on financial correlation

Authors:

Yung-Keun Kwon,

Sung-Soon Choi,

Byung-Ro MoonAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 2061 - 2066

https://doi.org/10.1145/1068009.1068351

Published: 25 June 2005 Publication History

Abstract

In this paper, we propose a neuro-genetic stock prediction system based on financial correlation between companies. A number of input variables are produced from the relatively highly correlated companies. The genetic algorithm selects a set of informative input features among them for a recurrent neural network. It showed notable improvement over not only the buy-and-hold strategy but also the recurrent neural network using only the input variables from the target company.

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Cited By

陈欣(2024)Machine Learning Based Stock Return Prediction and Portfolio ResearchOperations Research and Fuzziology10.12677/orf.2024.14216314:02(599-609)Online publication date: 2024
https://doi.org/10.12677/orf.2024.142163
Jiang JWu LZhao HZhu HZhang W(2023)Forecasting movements of stock time series based on hidden state guided deep learning approachInformation Processing & Management10.1016/j.ipm.2023.10332860:3(103328)Online publication date: May-2023
https://doi.org/10.1016/j.ipm.2023.103328
Choi IKim W(2022)Analyzing Global Financial Market Indices and Predicting Fluctuations of the Korean Market Index Using Information Flow-Based Network AnalysisJournal of the Korean Institute of Industrial Engineers10.7232/JKIIE.2022.48.4.34048:4(340-354)Online publication date: 15-Aug-2022
https://doi.org/10.7232/JKIIE.2022.48.4.340
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Index Terms

Stock prediction based on financial correlation
1. Applied computing
  1. Enterprise computing

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Published In

cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 June 2005

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

陈欣(2024)Machine Learning Based Stock Return Prediction and Portfolio ResearchOperations Research and Fuzziology10.12677/orf.2024.14216314:02(599-609)Online publication date: 2024
https://doi.org/10.12677/orf.2024.142163
Jiang JWu LZhao HZhu HZhang W(2023)Forecasting movements of stock time series based on hidden state guided deep learning approachInformation Processing & Management10.1016/j.ipm.2023.10332860:3(103328)Online publication date: May-2023
https://doi.org/10.1016/j.ipm.2023.103328
Choi IKim W(2022)Analyzing Global Financial Market Indices and Predicting Fluctuations of the Korean Market Index Using Information Flow-Based Network AnalysisJournal of the Korean Institute of Industrial Engineers10.7232/JKIIE.2022.48.4.34048:4(340-354)Online publication date: 15-Aug-2022
https://doi.org/10.7232/JKIIE.2022.48.4.340
Kim YByun Y(2022)Ultra-Short-Term Continuous Time Series Prediction of Blockchain-Based Cryptocurrency Using LSTM in the Big Data EraApplied Sciences10.3390/app12211108012:21(11080)Online publication date: 1-Nov-2022
https://doi.org/10.3390/app122111080
Zhang XLiu SZheng X(2021)Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention MechanismMathematics10.3390/math90808009:8(800)Online publication date: 7-Apr-2021
https://doi.org/10.3390/math9080800
Wu YShang PXia J(2021)Inverse sample entropy analysis for stock marketsNonlinear Dynamics10.1007/s11071-020-06118-z103:1(741-758)Online publication date: 6-Jan-2021
https://doi.org/10.1007/s11071-020-06118-z
Li BLi L(2021)Stock Prediction Based on Adaptive Gradient Descent Deep LearningAdvanced Information Networking and Applications10.1007/978-3-030-75100-5_5(51-62)Online publication date: 24-Apr-2021
https://doi.org/10.1007/978-3-030-75100-5_5
Sim HKim HAhn JSilva T(2019)Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?Complexity10.1155/2019/43248782019Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1155/2019/4324878
Cao HLin TLi YZhang H(2019)Stock Price Pattern Prediction Based on Complex Network and Machine LearningComplexity10.1155/2019/41324852019(1-12)Online publication date: 28-May-2019
https://doi.org/10.1155/2019/4132485
Lee GLee S(2018)Universal Prediction System Realization Using RNNThe Journal of Korean Institute of Information Technology10.14801/jkiit.2018.16.10.1116:10(11-20)Online publication date: 31-Oct-2018
https://doi.org/10.14801/jkiit.2018.16.10.11
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