Portfolio rebalancing under uncertainty using meta-heuristic algorithm

Mostafa Zandieh and Seyed Omid Mohaddesi

International Journal of Operational Research, 2019, vol. 36, issue 1, 12-39

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. (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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