https://github.com/jaydu1/sparseportfolio

High Dimensional Portfolio Selection with Cardinality Constraints
https://github.com/jaydu1/sparseportfolio

cardinality-constraints high-dimensional-statistics l1-regularization portfolio-optimization

Last synced: about 1 month ago
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High Dimensional Portfolio Selection with Cardinality Constraints

• Host: GitHub
• URL: https://github.com/jaydu1/sparseportfolio
• Owner: jaydu1
• License: mit
• Created: 2021-11-29T16:57:29.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-09-27T13:06:33.000Z (about 2 years ago)
• Last Synced: 2024-02-21T18:54:14.276Z (9 months ago)
• Topics: cardinality-constraints, high-dimensional-statistics, l1-regularization, portfolio-optimization
• Language: Python
• Homepage:
• Size: 4.21 MB
• Stars: 4
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# High-Dimensional Portfolio Selecton with Cardinality Constraints

This repo contains code for perform proximal gradient descent to solve sample average approximation of expected utility maximization problems with cardinality constraints.

We show that, under mild conditions, the $l_1$-regularized problem is equivalent to the $l_0$-constrained problem.

# Requirements

We use Python 3 for our code.

Please refer to `requirements.txt`, and use `pip` or `conda` to create a virtual environment with required packages installed.