MATLAB Code Implementation of Support Vector Machine for Stock Price Prediction

Implementing Support Vector Machine (SVM) in MATLAB for stock price prediction combined with univariate linear regression analysis represents a classic financial time series forecasting problem. Below is the technical framework and implementation approach: Data Preparation Phase: First, historical stock price data must be acquired, typically including features such as opening price, closing price, highest price, lowest price, and trading volume. For univariate linear regression, we can select a single feature like closing price as the prediction target, while SVM can handle multidimensional features. Feature Engineering Processing: - Normalize raw price data using z-score or min-max normalization - Construct technical indicator features (e.g., moving averages, RSI indicators) - Create time-lagged features to capture temporal dependencies - Split dataset into training and testing sets (typically using 7:3 or 8:2 ratio) SVM Model Implementation: MATLAB's Statistics and Machine Learning Toolbox provides the fitrsvm function for regression-type SVM implementation. Key steps include: - Select appropriate kernel functions (linear kernel, Gaussian RBF kernel etc.) using fitrsvm's 'KernelFunction' parameter - Set proper penalty parameter C and epsilon-insensitive parameter ε through hyperparameter tuning - Optimize hyperparameters using cross-validation with fitrsvm's 'OptimizeHyperparameters' option - Train model and evaluate prediction performance using predict() function Linear Regression as Baseline: Establish univariate linear regression model as prediction benchmark using MATLAB's fitlm function for quick implementation. Model Evaluation and Comparison: - Use performance metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE) - Visualize comparison curves between predicted results and actual prices using plot() function - Analyze performance differences between models under various market conditions Practical Application Considerations: - Address non-stationarity and noise issues in financial time series through differencing or detrending - Incorporate volatility features to enhance model robustness using volatility indicators - Implement periodic model retraining to adapt to market changes with automated retraining scripts This combined approach leverages SVM's capability to handle nonlinear relationships while providing interpretable baseline references through simple linear regression, making it suitable for multi-perspective analysis of complex systems like stock prices.