Towards Evology: a Market Ecology Agent-Based Model of US Equity Mutual Funds II

TL;DR

This paper introduces Evology, an agent-based model of the US stock market that simulates heterogeneous investor behaviors and their interactions, enabling testing of trading strategies and integration with machine learning.

Contribution

It presents the latest advances in a calibrated, heterogeneous market ecology ABM, linking agent interactions with endogenous price formation and ML strategy testing.

Findings

Prices emerge endogenously from agent interactions

Model supports testing of trading strategies

Encourages integration of ABMs with ML algorithms

Abstract

Agent-based models (ABMs) are fit to model heterogeneous, interacting systems like financial markets. We present the latest advances in Evology: a heterogeneous, empirically calibrated market ecology agent-based model of the US stock market. Prices emerge endogenously from the interactions of market participants with diverse investment behaviours and their reactions to fundamentals. This approach allows testing trading strategies while accounting for the interactions of this strategy with other market participants and conditions. Those early results encourage a closer association between ABMs and ML algorithms for testing and optimising investment strategies using machine learning algorithms.