A new portfolio selection problem in bubble condition under uncertainty: Application of Z-number theory and fuzzy neural network

作者：

Highlights：

• Classical risk measures perform poorly in bubble condition.

• A new portfolio selection problem is proposed to capture the bubble.

• Fuzzy neural network is used to calculate the input data of the model.

• To capture the uncertainty of input data, the Z-number theory is applied.

• Results show the advantage of the proposed model over classical risk measures.

摘要

•Classical risk measures perform poorly in bubble condition.•A new portfolio selection problem is proposed to capture the bubble.•Fuzzy neural network is used to calculate the input data of the model.•To capture the uncertainty of input data, the Z-number theory is applied.•Results show the advantage of the proposed model over classical risk measures.

论文关键词：Portfolio selection problem,Fundamental value,Market value,Z-number theory,Fuzzy neural network

论文评审过程：Received 8 November 2020, Revised 22 March 2021, Accepted 23 March 2021, Available online 27 March 2021, Version of Record 5 April 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114944