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Paper / 2024SMQ / 16785806257803
Dynamic ETF Portfolio Optimization Using enhanced Transformer-Based Models for Covariance and Semi-Covariance Prediction(Work in Progress)
Hengzhi Wu / Jiahao Zhu
deep learning / financial machine learningResource overview

Dynamic ETF Portfolio Optimization Using enhanced Transformer-Based Models for Covariance and Semi-Covariance Prediction(Work in Progress)

Paper2024

by Hengzhi Wu, Jiahao Zhu

Tags
deep learningfinancial machine learningmachine learningneural networksportfolio managementportfolio optimizationrisk management

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

Before you read

  1. Eigenvalues and Covariance Matrices

    Understand the covariance structure of portfolio risk.

  2. Constrained and Convex Optimisation

    Read objectives, constraints and optimality conditions.

  3. Time-Ordered Train, Validation and Test Design

    Preserve time order and avoid future information in training.

  4. Linear Regression

    Establish an interpretable baseline before a flexible model.

Go further

  1. Mean-Variance Optimisation

    Construct allocations and examine sensitivity to estimated inputs.

  2. Portfolio Constraints

    Turn mathematical weights into feasible portfolio decisions.

  3. Finance-aware validation

    Account for dependence and overlapping observations.

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