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The Myth and Reality of Financial Machine Learning
Marcos López de Prado
financial machine learning / machine learningResource overview

The Myth and Reality of Financial Machine Learning

Paper

by Marcos López de Prado

Tags
financial machine learningmachine learningportfolio management

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Suggested foundations for financial machine learning and portfolio construction. Specialist prerequisites vary by paper.

Before you read

  1. Time-Ordered Train, Validation and Test Design

    Preserve time order and avoid future information in training.

  2. Linear Regression

    Establish an interpretable baseline before a flexible model.

  3. Eigenvalues and Covariance Matrices

    Understand the covariance structure of portfolio risk.

  4. Constrained and Convex Optimisation

    Read objectives, constraints and optimality conditions.

Go further

  1. Finance-aware validation

    Account for dependence and overlapping observations.

  2. Feature importance

    Examine which information contributes to predictions.

  3. Mean-Variance Optimisation

    Construct allocations and examine sensitivity to estimated inputs.

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