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Stochastic Decision Trees for the Analysis of Investment Decisions

By Richard F. Hespos and Paul A. Strassmann

Abstract

This paper describes an improved method for investment decision making. The method, which is called the stochastic decision tree method, is particularly applicable to investments characterized by high uncertainty and requiring a sequence of related decisions to be made over a period of time. The stochastic decision tree method builds on concepts used in the risk analysis method and the decision tree method of analyzing investments. It permits the use of subjective probability estimates or empirical frequency distributions for some or all factors affecting the decision. This application makes it practicable to evaluate all or nearly all feasible combinations of decisions in the decision tree, taking account of both expected value of return and aversion to risk, thus arriving at an optimal or near optimal set of decisions. Sensitivity analysis of the model can highlight factors that are critical because of high leverage on the measure of performance, or high uncertainty, or both. The method can be applied relatively easily to a wide variety of investment situations, and is ideally suited for computer simulation.

DOI identifier: 10.1287/mnsc.11.10.B244
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