An AI-enabled Design Framework for Price Intelligence and Trend Prediction Based on Machine Learning and Particle Swarm Optimisation

The stock market exhibits extreme volatility, nonlinearity, and changes in the internal and external environment. Artificial intelligence (AI) methods can be used to find this non-linearity, which dramatically improves forecast accuracy. This study reviews 148 studies that use neural and hybrid-neuro techniques to forecast stock markets. 43 automatically generated themes are used to group the papers. Study features and performance outcomes are the two main categories into which we split the papers that were surveyed. While the data pre-processing method, artificial intelligence approach, training algorithm, and standard performance have been categorised under "model characteristics," the stock market covered, the necessary information, and the type of study are further classified under "study characteristics." Our findings show that employing AI techniques, stock market behaviour may be successfully examined and assessed.