Title: Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Authors: Mostafa Zandieh; Seyed Omid Mohaddesi

Addresses: Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran ' Department of Financial Engineering, Raja University, Qazvin, Iran

Abstract: In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimisation solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver.

Keywords: portfolio rebalancing; transaction costs; constant-proportion portfolio insurance; CPPI; uncertainty theory; meta-heuristic algorithm.

DOI: 10.1504/IJOR.2019.102068

International Journal of Operational Research, 2019 Vol.36 No.1, pp.12 - 39

Received: 31 May 2016
Accepted: 29 Aug 2016

Published online: 04 Sep 2019 *

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