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- W2972058319 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." @default.
- W2972058319 created "2019-09-12" @default.
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- W2972058319 date "2019-01-01" @default.
- W2972058319 modified "2023-09-25" @default.
- W2972058319 title "Portfolio rebalancing under uncertainty using meta-heuristic algorithm" @default.
- W2972058319 doi "https://doi.org/10.1504/ijor.2019.10023653" @default.
- W2972058319 hasPublicationYear "2019" @default.
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