论文信息 - Liquidity Risk Portfolio Optimization Using Swarm Intelligence

Liquidity Risk Portfolio Optimization Using Swarm Intelligence

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.

Ben Niu | Lijing Tan | Yan Fan | Han Xiao | Junjun Rao

[1] F. Diebold,et al. Modeling Liquidity Risk with Implications for Traditional Market Risk Measurement and Management , 2008 .

[2] Kevin M. Passino,et al. Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[3] W. Sharpe. CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* , 1964 .

[4] Tae Yoon Kim,et al. Using genetic algorithm to support portfolio optimization for index fund management , 2005, Expert Syst. Appl..

[5] A. Stuart,et al. Portfolio Selection: Efficient Diversification of Investments , 1959 .

[6] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[7] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8] Ganapati Panda,et al. Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques , 2009, Expert Syst. Appl..

[9] Giorgio Consigli,et al. Tail estimation and mean–VaR portfolio selection in markets subject to financial instability , 2002 .

[10] Alexandre M. Baptista,et al. Economic implications of using a mean-VaR model for portfolio selection: A comparison with mean-variance analysis , 2002 .