Indirect Methods for Personalized Mean-CVaR Portfolio Optimization

https://doi.org/10.47194/orics.v6i2.385

Authors

Keywords:

Personalized Mean-CvaR, Portfolio Optimization, Indirect Methods, Steepest Descent, Newton's Method

Abstract

This study presents a reformulation of the Personalized Mean-CVaR model into an unconstrained optimization problem, which is then solved using iterative methods, including steepest descent and Newton’s method. The reformulation introduces challenges related to feasibility region checking, convexity of the feasible set and objective functions, and the use of Lagrange multipliers to handle constraints. Additionally, Taylor expansion is utilized to approximate the objective function in each iteration. The research evaluates the effectiveness of iterative optimization techniques in solving the Personalized Mean-CVaR problem, while addressing key challenges in convergence and stability of the solution.

References

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Published

2025-06-30

How to Cite

Setyawan, D. P. ., & Salih, Y. (2025). Indirect Methods for Personalized Mean-CVaR Portfolio Optimization. Operations Research: International Conference Series, 6(2), 107–116. https://doi.org/10.47194/orics.v6i2.385

Issue

Vol. 6 No. 2 (2025): Operations Research International Conference Series (ORICS), June 2025

Section

Articles
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Copyright (c) 2025 Deva Putra Setyawan; Yasir Salih

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