Fuzzy Portfolio Selection with Flexible Optimization via Quasiconvex Programming

EasyChair Preprint 7823, version 1

Abstract

In this study, we consider a single objective fuzzy portfolio optimization with flexible goal and constraints, in which the Sharpe ratio is chosen as the goal and the portfolio's mean and variance are included in the constraints. Although this problem has much significance in finance, it is difficult to solve because of the nonconvexity of the objective function. Based on fuzzy theory and flexible optimization, the fuzzy portfolio problem is transformed to the crisp form which is proved to be a semistrictly quasiconvex programming problem for any decreasing membership functions. This property of the equivalent problem is the basis to solve the main problem efficiently by available convex programming algorithms. The computational experiments with SP500 data set is reported to demonstrate the performance of the proposed model.

Keyphrases: Flexible optimization, Fuzzy portfolio selection, Semistrictly quasiconvex programming, Sharpe ratio, soft constraints

@booklet{EasyChair:7823,
  author    = {Tran Thi Thanh Tuoi and Truong Tuan Khang and Nguyen Thi Ngoc Anh and Tran Ngoc Thang},
  title     = {Fuzzy Portfolio Selection with Flexible Optimization via Quasiconvex Programming},
  howpublished = {EasyChair Preprint 7823},
  year      = {EasyChair, 2022}}