Portfolio optimisation: bridging the gap between theory and practice

By Cristiano Arbex Valle

Rating

1350
Battle Count: 237

Relevance

9/10
Highly relevant for implementing theoretical portfolio models in practice, especially for automated trading systems

Implementation Complexity

7/10
Requires advanced optimization techniques and careful consideration of real-world constraints

Reproducibility

4/5
The paper provides detailed mathematical formulations and computational experiments, with data and code available on GitHub

About this paper

Methodology: Two-stage optimisation framework. Problem types: Portfolio Optimization, Risk Management.

The interactive Everscope explorer (charts, battles, favorites) loads below.