Multi-agent based analysis of financial data

TL;DR

This paper applies multi-agent systems to model nonlinear distributed signal processing in financial data, analyzing how agent interactions influence prediction robustness in stochastic currency exchange environments.

Contribution

It introduces a multi-agent model for financial signal processing and studies the effects of agent interactions on system stability and prediction accuracy.

Findings

Populations are sensitive to interaction strength and form.

Mean life-times and utilities vary with interaction dynamics.

Results support development of robust adaptive prediction systems.

Abstract

In this work the system of agents is applied to establish a model of the nonlinear distributed signal processing. The evolution of the system of the agents - by the prediction time scale diversified trend followers, has been studied for the stochastic time-varying environments represented by the real currency-exchange time series. The time varying population and its statistical characteristics have been analyzed in the non-interacting and interacting cases. The outputs of our analysis are presented in the form of the mean life-times, mean utilities and corresponding distributions. They show that populations are susceptible to the strength and form of inter-agent interaction. We believe that our results will be useful for the development of the robust adaptive prediction systems.