Stock Market Insights Using Basic Recurrent Neural Networks Models

Using a confusion matrix-based methodology, this study explores the use of Simple RNNs for stock price forecasting. Our technology generates accurate forecasts by examining past data to reveal hidden trends and patterns. The model’s strong points and opportunities for development are reveal by a careful analysis of the confusion matrix. To assess the model’s performance, we categorize continuous price predictions into discrete bins. Our findings suggest that Simple RNNs are effective in predicting these prices. The study adds to the expanding body of research on the application of deep neural architecture in the financial sector.