Presenting a Hybrid Method to Overcome the Challenges of Determining the Uncertainty of Future Stock Price Identification

Abstract

A particular location, framework, or forum where buyers and sellers congregate to trade products, services, or assets is referred to as an economic market. While the future is unpredictable and unknowable, it is still possible to make informed predictions about the course of events. Predicting stock market movements using artificial intelligence and machine learning is one such potential. Even if the stock market is volatile, it is still feasible and wise to use artificial intelligence to create well-informed forecasts before making an investment. The current work suggests a novel approach to increase stock price forecast accuracy by integrating the Radical basis function with Particle Swarm Optimization, Slime Mold Algorithm, and Moth Flame Optimization. The objective of the study is to improve stock price forecast accuracy while accounting for the complexity and volatility of financial markets. The efficacy of the proposed strategy has been tested in the real world using historical stock price statistics. Results demonstrate considerable accuracy improvements over traditional RBF models. The combined strength of RBF and the optimization technique enhances the model's ability to adapt to changing market conditions in addition to increasing prediction accuracy. Results were 0.984, 0.990, 0.991, and 0.994 for RBF, PSO-RBF, SMA, and MFO-RBF, respectively. The performance of MFO-RBF in comparison to RBF shows how combining with the optimizer can enhance the performance of the given model. By contrasting the outcomes of various optimizers, the most accurate optimization has been determined as the main optimizer of the model.