Stock Market Prediction MATLAB Program with Neural Network Implementation

This MATLAB program provides a flexible framework for stock market prediction using configurable neural network models. The implementation allows users to select from different neural network types (such as feedforward networks, RNNs, or LSTMs) through parameter settings in the configuration file or GUI interface. This architectural flexibility enables improved prediction accuracy by matching appropriate network structures to specific market conditions and data characteristics. The program structure includes modular components for data preprocessing, feature extraction, network training, and prediction validation. Key functions include data normalization routines, network initialization methods, and backpropagation algorithms for training optimization. The code supports multiple input formats including historical price data, technical indicators, and macroeconomic variables. Beyond stock market applications, the program's modular design facilitates extension to other financial markets (forex, commodities) and economic forecasting domains. Analysts can adapt the feature engineering component and retrain models using sector-specific datasets. The implementation includes performance evaluation metrics like MSE (Mean Squared Error) and directional accuracy scoring to validate prediction quality. Investors and financial analysts can leverage this tool to gain deeper insights into market trends and volatility patterns, enabling data-driven investment decisions and potentially enhancing portfolio returns through more accurate trend predictions and risk assessment capabilities.