Bayesian Models for Forecasting Future Security Prices

Abstract

The field of investment analysis provides an example of a situation in which individuals or corporations make inferences and decisions in the face of uncertainty about future events. The uncertainty concerns future security prices and related variables, and it is necessary to take account of this uncertainty when modeling inferential or decision-making problems relating to investment analysis. Since probability can be thought of as the mathematical language of uncertainty, formal models for decision making under uncertainty require probabilistic inputs. In financial decision making, this is illustrated by the models that have been developed for the portfolio selection problem; such models generally require the assessment of probability distributions (or at least some summary measures of probability distributions) for future prices or returns of the securities that are being considered for inclusion in the portfolio (e.g., see Markowitz [11] and Sharpe [19]).

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