Naïve learning algorithms utilized for the prediction of stock prices to compare economic models of decision making

Abstract

An advance in economic thought is in the area of behavioral economics where traditional models of rational decision-making are challenged by newer models of behavior such as Prospect Theory. This is coupled with a world where algorithms have abilities to learn, remember and evolve over time to make better decisions. The advances on these two fronts are forcing the world of markets to be analyzed from a different angle. This work is a look at markets to compare traditional expected utility theory of economic decision-making to the newer idea of Prospect Theory. Two learning algorithms, based on traditional expected utility and Prospect Theory, are designed and then compared under several scenarios designed to replicate various market conditions faced by investors. Deviations were analyzed to measure the effectiveness of the two algorithms and also the two models of economic decision making, where it was found that risk averseness described by Prospect Theory will lead to greater deviations in expected prices than more traditional models of economic decision making. This is for several reasons, including risk aversion can, in most situations, lead to suboptimal economic decisions.

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