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A preference ranking model based on both mean-variance analysis and cumulative distribution function using simulation

Author

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  • Khwazbeen S. Fatah
  • Peng Shi
  • Jamal R.M. Ameen
  • Ronald J. Wiltshire

Abstract

In decision-making problems under uncertainty, mean-variance analysis consistent with expected utility theory plays an important role in analysing preferences for different alternatives. In this paper, a new approach for mean-variance analysis based on cumulative distribution functions is proposed. Using simulation, a new algorithm is developed, which generates pairs of random variables to be representative for each pair of uncertain alternatives. The proposed model is concerned with financial investment for risk-averse investors with non-negative lotteries. Furthermore, the proposed technique in this paper can be applies to different distribution functions for lotteries or utility functions.

Suggested Citation

  • Khwazbeen S. Fatah & Peng Shi & Jamal R.M. Ameen & Ronald J. Wiltshire, 2009. "A preference ranking model based on both mean-variance analysis and cumulative distribution function using simulation," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 5(3), pages 311-327.
    • Handle: RePEc:ids:ijores:v:5:y:2009:i:3:p:311-327
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    Cited by:

    1. Li, Xiang & Shou, Biying & Qin, Zhongfeng, 2012. "An expected regret minimization portfolio selection model," European Journal of Operational Research, Elsevier, vol. 218(2), pages 484-492.

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