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

• Published in: Archives of computational methods in engineering Vol. 28; no. 4; pp. 2133 - 2164
• Main Authors: Thakkar, Ankit, Chaudhari, Kinjal
• Format: Journal Article
• Language: English
• 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
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-020-09448-8

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.