Discrete-Event Simulation-Based Q-Learning Algorithm Applied to Financial Leverage Effect

Abstract

Discrete-event modeling and simulation and machine learning are two frameworks suited for system of systems modeling which when combined can give a powerful tool for system optimization and decision making. One of the less explored application domains is finance, where this combination can propose a driven tool to investor. This paper presents a discrete-event specification as a universal framework to implement a machine learning algorithm into a modular and hierarchical environment. This approach has been validated on a financial leverage effect based on a Markov decision-making policy.