10. Ridge Regression Demo

Exploring Ridge Regression in Stock Market Data

Understanding ridge regression through a practical example using stock market data. Comparing ridge regression with lasso regression to understand their differences:

• Regularization Techniques: Both ridge and lasso regression apply regularization, but with slight differences:

  • Lasso Regression: Takes a stringent approach, aiming to reduce coefficients towards zero.
  • Ridge Regression: Less stringent, allowing it to capture more complexity in the data.

• Practical Implementation:

  • Load necessary libraries and historical data.
  • Apply train-test split and standardization.
  • Create and train a ridge regression model using training data.

• Prediction and Evaluation:

  • Use the trained model to make predictions on testing data.
  • Index and rename predictions properly for easy identification.
  • Compare predictions with actual test outcomes.

• Developing a Trading Strategy:

  • Define a simple trading strategy based on predicted returns.
  • Evaluate strategy performance using metrics like cumulative returns and Sharpe ratio.

Conclusion highlights the comparison between ridge regression, lasso regression, and the S&P 500, addressing their respective performances.