Abstract
The liquidity risk is one of the most important adjustable parameters of the portfolio selection. This paper proposes an improved model considering the liquidity risk and market risk, which makes it more suitable for the actual situation. In the improved model we take into account the risk appetite of investors and other psychological factors. To solve the improved portfolio optimization model with complex constraints, we present a comparative study for three swarm intelligence methods namely genetic algorithm (GA), bacterial foraging optimization (BFO) and particle swarm optimization (PS0). The primary results demonstrate their effectiveness and efficiency.
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Niu, B., Xiao, H., Tan, L., Fan, Y., Rao, J. (2010). Liquidity Risk Portfolio Optimization Using Swarm Intelligence. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_72
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DOI: https://doi.org/10.1007/978-3-642-14831-6_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
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