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.

股市交易一直是投资者，院士和研究人员感兴趣的主题。分析股票市场数据固有的非线性特征是一项艰巨的任务。开发了大量学习算法来研究市场行为并提高预测准确性；它们已使用群体和进化计算（例如粒子群优化（PSO））进行了优化；其具有连续数据的全局优化能力已在金融领域得到开发。讨论了现有方法的局限性和潜在的未来研究方向，以增强基于PSO的股票市场预测。本文旨在平衡经济学和计算智能方面；它还分析了PSO在股票投资组合优化，股票价格和趋势预测方面的优势，