smq/directoryAn evolving quant resource archive
Back to directoryResource
Paper / 2023SMQ / 126557265191089
Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning
Si Zhao / Xiangyu Cui / Xun Li / Yun Shi
optimization / portfolio managementResource overview

Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning

Paper2023

by Si Zhao, Xiangyu Cui, Xun Li, Yun Shi

Tags
optimizationportfolio managementportfolio optimizationreinforcement learning

Access resource

Open original source Understand this paper

Continue on SelfMadeQuant

Understand this paper

Suggested foundations for portfolio construction and optimisation and numerical methods. 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

    Identify the objective, constraints and conditions for an optimum.

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

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. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

Explore a related reading path

Lesson links open public previews. Access to full lessons depends on the topic and your membership.

Explore Portfolio Theory and Quantitative Investment Explore Linear Algebra, Optimisation and Numerical Methods

Keep exploring

Related resources

More ideas from similar topics.

6

From the same author

Si Zhao, Xiangyu Cui, Xun Li, Yun Shi

2