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Decision Tree Analyses
Abstract
As a branch of statistics that uses probability, decision trees have been widely applied to variety problems from numerous disciplines and serve two primary goals. First, they help us to resolve uncertainties in making investment decisions. Second, using decision trees we can determine which alternatives, at any point in time produces the most favorable, or least painful, consequences. In contrast, classical statistics focus on estimating a parameter, such as the population means, constructing a confidence interval, or conducting a hypothesis test. Classical statistics do not address the possible consequences of a decision. In this chapter I illustrate the essentials of using a decision tree for making financial decisions, and demonstrate how a decision is made using both criteria: expected monetary value and expected utility. At the end, I discuss the imperfectability of the traditional techniques and tools and suggest alternative decision tools inspired by some areas of research in signal processing, known as wavelet analysis. To set up and solve decision tree problems, TreePlan, and add-in for Excel, is used.
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