A DATA DRIVEN MACHINE LEARNING APPROACH TO DISCOVERING RULES OF PRICE BEHAVIOR IN A FINANCIAL MARKET SIMULATION

Abstract

The field of agent-based simulation of financial markets has grown considerably in the last decade. However, the interpretation of simulation results has received far less attention. Typically, the results of a large number of simulations are reduced to one or two summary statistics, such as sample moments. While such summarization is useful, it overlooks a vast amount of additional information that might be gleaned by examining patterns of behavior that emerge at lower levels. In this paper we propose an approach to interpreting simulation results that involves the use of so-called data mining techniques to identify the rules of behavior that govern an underlying system. We demonstrate the approach by using data from a single run of an order market simulation to derive rules about the behavior of prices in that simulation.