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Reinforcement Learning for Deep Portfolio Optimization
Jiafei Jin / Kun Han / Ruyu Yan
DPO / Sharpe ratioResource overview

Reinforcement Learning for Deep Portfolio Optimization

Paper

by Jiafei Jin, Kun Han, Ruyu Yan

Tags
DPOSharpe ratioasset correlationdeep learningdeep portfolio optimizationfeature extractionfeature fusiongeneralizationinvestment decision-makingmachine learning in financemaximum drawdownmodern portfolio theoryportfolio optimizationportfolio valueprofitabilityquantitative financereal-world datasetsreinforcement learningrisk managementrisk-cost rewardtime seriestransaction costs

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Suggested foundations for portfolio construction and financial time series. 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. Stationarity and Unit Roots

    Check whether a relationship can be estimated consistently over time.

  4. Linear Regression

    Read the fitted relationship and its residuals.

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. Cointegration and Error Correction

    Study long-run relationships without confusing them with correlation.

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