Fuzzy portfolio selection based on three-way decision and cumulative prospect theory

2021 ◽  
Author(s):  
Xianhe Wang ◽  
Bo Wang ◽  
Shu Liu ◽  
Huaxiong Li ◽  
Tianxing Wang ◽  
...  
Keyword(s):  
Prospect Theory ◽  
Portfolio Selection ◽  
Cumulative Prospect Theory ◽  
Fuzzy Portfolio Selection

scholarly journals Cumulative Prospect Theory Portfolio Selection

2020 ◽  
Author(s):  
Diana Barro ◽  
Marco Corazza ◽  
Martina Nardon
Keyword(s):  
Prospect Theory ◽  
Portfolio Selection ◽  
Cumulative Prospect Theory

scholarly journals Dynamic Money Doctors under Cumulative Prospect Theory

2019 ◽  
pp. 1-26
Author(s):  
Liurui Deng ◽  
Lan Yang ◽  
Bolin Ma
Keyword(s):  
Prospect Theory ◽  
Portfolio Selection ◽  
Value Function ◽  
Cumulative Prospect Theory ◽  
Risky Asset ◽  
Investment Strategies ◽  
Portfolio Choices ◽  
Portfolio Managers ◽  
Dynamic Portfolio ◽  
Mean Variance

We investigate the interaction between investors and portfolio managers under cumulative prospect theory. We model trust in the manager and the relative anxiety about investing in a risky asset in an original way. Moreover, we study how trust and anxiety affect the manager’s fee and the portfolios of cumulative prospect theory investors. In contrast to previous work using the classical mean-variance preferences, there are two main novelties in our contribution. First, our research relies on cumulative prospect theory (CPT) rather than the classical mean-variance framework. Second, we focus on a dynamic portfolio selection. In other words, we formulate the optimal problem under multi-period setting. Besides, we attain an optimal portfolio choices in multi-period relying on the sub-game perfect investment strategies. Moreover, our research differs from traditional CPT work through an improved value function that accurately characterizes the reduction in anxiety suffered by the CPT investors from bearing risk when assisted by the portfolio managers’ help relative to when they lack such assistance.

scholarly journals Multi-Period Investment Strategies under Cumulative Prospect Theory

2019 ◽  
Vol 12 (2) ◽  
pp. 83 ◽  
Author(s):  
Liurui Deng ◽  
Traian A. Pirvu
Keyword(s):  
Numerical Analysis ◽  
Prospect Theory ◽  
Portfolio Selection ◽  
Cumulative Prospect Theory ◽  
Risky Asset ◽  
Model Parameters ◽  
Investment Strategies ◽  
Portfolio Constraints ◽  
Risk Criterion ◽  
Optimal Portfolio Selection

In this article, inspired by Shi et al., we investigate the optimal portfolio selection with one risk-free asset and one risky asset in a multiple period setting under the cumulative prospect theory (CPT) risk criterion. Compared with their study, our novelty is that we consider a stochastic benchmark and portfolio constraints. By performing a numerical analysis, we test the sensitivity of the optimal CPT investment strategies to different model parameters.

Research on disruption management of urgent arrival in job shop with deteriorating effect

2021 ◽  
pp. 1-13
Author(s):  
Ning Tao ◽  
Duan Xiaodong ◽  
An Lu ◽  
Gou Tao
Keyword(s):  
Prospect Theory ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Cumulative Prospect Theory ◽  
Disruption Management ◽  
Particle Swarm Algorithm ◽  
Shop Scheduling ◽  
Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

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