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Robust multi-period portfolio model based on prospect theory and ALMV-PSO algorithm




Liu, Jiahe, author
Jin, Xiu, author
Wang, Tianyang, author
Yuan, Ying, author
Expert Systems with Applications, publisher

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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.


Includes bibliographical references (pages 25-28).
Published as: Expert Systems With Applications, vol. 42, no.20, November 2015, pp.7252–7262,

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portfolio selection
prospect theory
robust optimization
multi-period portfolio
particle swarm optimization


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