Bacterial foraging based approaches to portfolio optimization with liquidity risk

Authors:
Ben Niu;Yan Fan;Han Xiao;Bing Xue
Affiliations:
College of Management, Shenzhen University, Shenzhen 518060, China and e-Business Technology Institute, The University of Hong Kong, Hong Kong, China and Hefei Institute of Intelligent Machines, C ...;College of Management, Shenzhen University, Shenzhen 518060, China;College of Management, Shenzhen University, Shenzhen 518060, China;Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand
Venue:
Neurocomputing
Year:
2012

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Abstract

This paper proposes a bacterial foraging based approach for portfolio optimization problem. We develop an improved portfolio optimization model by introducing the endogenous and exogenous liquidity risk and the corresponding indexes are designed to measure the endogenous/exogenous liquidity risk, respectively. Bacterial foraging optimization (BFO) is employed to find the optimal set of portfolio weights in the improved Mean-Variance model. BFO-LDC which is a modified BFO with linear deceasing chemotaxis step is proposed to further improve the performance of BFO. With four benchmark functions, BFO-LDC is proved to have better performance than the original BFO. And then comparisons of the results produced by BFO, BFO-LDC, particle swarm optimization (PSO), and genetic algorithms (GAs) for the proposed portfolio optimization model are presented. Simulation results show that BFOs can obtain both near optimal and the practically feasible solutions to the liquidity risk portfolio optimization problem. In addition, BFO-LDC outperforms BFO in most cases.