Comparative Risk Analysis and Price Prediction of Corporate Shares Using Deep Learning Models like LSTM and Machine Learning Models

Abstract

The prediction of share prices and risk analysis have always posed significant challenges for investors due to the influence of various economic, financial, and political factors. Inaccurate price predictions can lead to severe financial losses, particularly for investors with limited financial market knowledge. Recent research and advancements in Artificial Intelligence, Machine Learning, and Deep Learning models have greatly improved the accuracy of stock price predictions. This research focuses on applying the Long Short Term Memory model, a specialized Deep Learning technique, to predict the closing prices of Tech Industry stocks. The study calculates the Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) to evaluate the model’s performance. Additionally, the results are compared with other models, including Feed Forward Neural Networks, Recurrent Neural Networks, and Machine Learning Models like Support Vector Machine and Gradient Boosting, using the Weighted Average metric. The Long Short Term Memory models showed the lowest weighted average error value of 0.0115, establishing it as one of the most effective models for predicting stock prices. The findings have significant implications for investors and risk analysts, particularly in the Tech Industry, offering a robust tool for improving stock price prediction accuracy.