A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization

Stock market trading has been a subject of interest to investors, academicians, and researchers. Analysis of the inherent non-linear characteristics of stock market data is a challenging task. A large number of learning algorithms are developed to study market behaviours and enhance the prediction a...

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Published inArchives of computational methods in engineering Vol. 28; no. 4; pp. 2133 - 2164
Main Authors Thakkar, Ankit, Chaudhari, Kinjal
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.06.2021
Springer Nature B.V
Subjects
Artificial intelligence
Economic analysis
Engineering
Evolutionary algorithms
Evolutionary computation
Global optimization
Machine learning
Mathematical and Computational Engineering
Optimization
Original Paper
Particle swarm optimization
Securities markets
Swarm intelligence
Online AccessGet full text
ISSN1134-3060
1886-1784
DOI10.1007/s11831-020-09448-8

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Abstract Stock market trading has been a subject of interest to investors, academicians, and researchers. Analysis of the inherent non-linear characteristics of stock market data is a challenging task. A large number of learning algorithms are developed to study market behaviours and enhance the prediction accuracy; they have been optimized using swarm and evolutionary computation such as particle swarm optimization (PSO); its global optimization ability with continuous data has been exploited in financial domains. Limitations in the existing approaches and potential future research directions for enhancing PSO-based stock market prediction are discussed. This article aims at balancing the economics and computational intelligence aspects; it also analyzes the superiority of PSO for stock portfolio optimization, stock price and trend prediction, and other related stock market aspects along with implications of PSO.
AbstractList Stock market trading has been a subject of interest to investors, academicians, and researchers. Analysis of the inherent non-linear characteristics of stock market data is a challenging task. A large number of learning algorithms are developed to study market behaviours and enhance the prediction accuracy; they have been optimized using swarm and evolutionary computation such as particle swarm optimization (PSO); its global optimization ability with continuous data has been exploited in financial domains. Limitations in the existing approaches and potential future research directions for enhancing PSO-based stock market prediction are discussed. This article aims at balancing the economics and computational intelligence aspects; it also analyzes the superiority of PSO for stock portfolio optimization, stock price and trend prediction, and other related stock market aspects along with implications of PSO.
Author Chaudhari, Kinjal
Thakkar, Ankit
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  fullname: Chaudhari, Kinjal
  email: 17ftphde21@nirmauni.ac.in
  organization: Department of Computer Science and Engineering, Institute of Technology, Nirma University
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Snippet Stock market trading has been a subject of interest to investors, academicians, and researchers. Analysis of the inherent non-linear characteristics of stock...
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SubjectTerms Artificial intelligence
Economic analysis
Engineering
Evolutionary algorithms
Evolutionary computation
Global optimization
Machine learning
Mathematical and Computational Engineering
Optimization
Original Paper
Particle swarm optimization
Securities markets
Swarm intelligence
Title A Comprehensive Survey on Portfolio Optimization, Stock Price and Trend Prediction Using Particle Swarm Optimization
URI https://link.springer.com/article/10.1007/s11831-020-09448-8
https://www.proquest.com/docview/2532599519
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