09. Lasso Regression Demo

Implementing Lasso Regression for Stock Data Analysis

Lasso regression is applied to stock market data for improved predictive modeling. The process involves:

• Data Setup: Utilize historical prices and total returns similar to previous regression analysis.
• Train-Test Split: Prepare data using a split strategy, applying the standard scaler for normalization.

Model Construction

• Initialization: Create a lasso model with an Alpha parameter. This parameter controls model simplicity, balancing feature selection without excessive simplification.
• Training: Fit the model using X_train and Y_train datasets.

Predictions and Analysis

• Making Predictions: Use the trained model on test data, comparing predictions against actual values.
• Assess Coefficients: Analyze model outputs, revealing short-term mean reversion tendencies and longer-term trends.

Trading Strategy Formulation

• Strategy Definition: Simplify strategy decisions, such as going long if predictions exceed zero.
• Performance Evaluation: Compare strategy against S&P 500 and previous regression models, noting improved returns with lasso regression.

This approach showcases lasso regression's potential for enhanced performance in stock prediction models.