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Learning Non-Linear Relationships in the Cross-Section

Asset managers use factor investing to explain and forecast individual asset returns and to construct portfolios. A factor approach can help to increase diversification, improve returns, and minimize risks. Classic factors include value, size, and momentum, but the list of modern “candidate” factors is exceedingly long.

Credit Suisse’s equity investment strategist Ricardo Pachón Cortes and Valerio Sperandeo of MathWorks® discuss how to effectively apply machine learning techniques to a range of factors available in the HOLT dataset.

In this presentation, they explain:

- How to apply factor-timing techniques as well as machine learning techniques to choose between factor weights
- How to optimize the hyperparameters in the fitting process
- The outputs of the machine learning process

     Why use MATLAB?
     What is Equity Factor Investing
     Factor Prediction
     Machine Learning Model for Stock Selection
     Training Data
    HOLT Variable and Equity Factor Definitions
    Hyperparameter Optimization
   The Results
    Interpreting the Results
    Putting it into Practice

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