Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 20, Pages 7252-7262Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2015.04.063
Keywords
Finance; Portfolio selection; Prospect theory; Robust optimization; Multi-period portfolio; Particle swarm optimization
Categories
Funding
- National Science Foundation of China [71372186, 71271047, 70901017]
- Program for New Century Excellent Talents in University - China [NCET-13-0115]
- Program for Liaoning Excellent Talents in University [LJQ2013030]
- Fundamental Research Funds for the Central Universities [N120506002, N130606002]
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The studies of behavioral finance show that the cognitive bias plays an important role in investors' decision-making process. In this paper, based on the robust theory and prospect theory, a robust multi-period portfolio considering investors' behavioral factors is constructed, which features the reference dependence, loss aversion and diminishing sensitivity. To solve the proposed portfolio model, an improved particle swarm optimization (PSO) algorithm is developed, which incorporates the two-stage initialization strategy, the improved stochastic ranking approach, the aging leader and the multi-frequency vibrational mutation operator. We illustrate the robust model with real market data and show its effectiveness based on the performance of the proposed algorithm. The results show that the proposed algorithm is successful in solving the constrained multi-period portfolio model and the proposed portfolio model provides an effective tool for a real multi-period investment. (C) 2015 Elsevier Ltd. All rights reserved.
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