Automatic Stock Price Prediction and Classification Based on Hybrid with AI Feature Selection Method

In this research, we investigate the problem of automatically predicting and classifying stock prices, with an eye towards creating and testing a Hybrid AI Feature Selection Method. This work employs a fictitious dataset to offer a new method that integrates Genetic Algorithm (GA) and Recursive Feature Elimination (RFE) to isolate the most important characteristics for predicting stock prices and classifying market fluctuations. The findings demonstrate that the hybrid strategy is effective in reducing the complexity of features and greatly improving model performance over more conventional methods. Furthermore, a simulation of a trading strategy based on the categorization findings reveals its potential to produce more efficient and successful investment methods, highlighting the practical relevance of this study. This research contributes to the developing field of financial technology by laying the groundwork for a new way of thinking about financial prediction and decision making, giving professionals and investors access to cutting-edge resources that can help them make better, more profitable choices.