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

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

International Journal of Operational Research, 2009, vol. 5, issue 3, 311-327

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.

Keywords: mean variance theory; expected utility theory; cumulative distribution function; simulation; preference ranking; modelling; decision making; uncertainty; financial investment; risk-averse investors; non-negative lotteries; risk aversion. (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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