In this paper, we studied the problem of risky portfolio selection under uncertainty. Different from risk-return analytical methodology, we formulated a model under maximum minimal criterion of uncertain decision-making theory. If the investor had no any distribution information of the returns and (s)he knew the variation scopes of the returns by his/her knowledge of the market information or experts’ evaluations of the alternative risky assets, then we showed that the optimal portfolio strategy of the model under maximal minimal criterion could be obtained by solving linear programming. If the returns were known to be normal distributed, the investor’s optimal portfolio strategy could be obtained by solving a nonlinear programming. The paper also provided an algorithm to solve this programming. At last, the paper compared this model with Markowitz’s mean-varience (M-V) model and Young’s minmax model, and pointed out the distinctions and similarities between our model and the other two.
Quality & Quantity – Springer Journals
Published: Jul 19, 2005
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