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Machine Learning for Asset Managers


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Table of Contents

1. Introduction; 2. Denoising and detoning; 3. Distance metrics; 4. Optimal clustering; 5. Financial labels; 6. Feature importance analysis; 7. Portfolio construction; 8. Testing set overfitting.

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This element introduces machine learning (ML) tools that can help asset managers discover economic and financial theories.

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