Multi-period portfolio selection based on uncertainty theory with bankruptcy control and liquidity

Due to the impact of uncertain events, such as the 2008 financial crisis and the outburst of COVID19 pandemic, the experts' evaluations information is becoming increasingly important. This paper considers a multi-period portfolio optimization problem under uncertain circumstance, and the return rates of risky securities are regarded as uncertain variables, where the uncertainty theory is used to deal with experts' evaluations. In light of the complexity of financial markets, we formulate an uncertain multi-period mean-entropy-variance model, where bankruptcy, liquidity, diversification and self-financing are considered as realistic constraints. Furthermore, the maximum return and the minimum risk are both achieved in a single-objective model through the normalization method. Then the equivalent deterministic forms of two secondary models for main model are provided. In addition, we develop a modified root system growth algorithm, which is more suitable for the proposed model. Finally, the effectiveness of the proposed model and designed algorithm is confirmed by numerical simulations.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper addresses the issue of multi-period portfolio optimization under uncertain circumstances by treating the return rates of risky securities as uncertain variables and using uncertainty theory to handle experts' evaluations. A complex multi-period mean-entropy-variance model is formulated, taking into account realistic constraints such as bankruptcy, liquidity, diversification, and self-financing. The maximum return and minimum risk are achieved simultaneously in a single-objective model through normalization. Equivalent deterministic forms of secondary models for the main model are provided, and a modified root system growth algorithm is developed for better suitability. Numerical simulations confirm the effectiveness of the proposed model and algorithm.