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

TL;DR

This paper introduces Evology, an agent-based market ecology model that simulates interactions among US equity mutual funds, helping to understand how endogenous strategy interactions influence market behavior and fund performance.

Contribution

The paper presents a novel, empirically calibrated multi-agent model of US equity mutual funds, integrating machine learning for strategy optimization.

Findings

Model accurately replicates market dynamics and fund interactions.

Endogenous interactions significantly impact investment style performance.

Potential for optimizing strategies using machine learning.

Abstract

The profitability of various investment styles in investment funds depends on macroeconomic conditions. Market ecology, which views financial markets as ecosystems of diverse, interacting and evolving trading strategies, has shown that endogenous interactions between strategies determine market behaviour and styles' performance. We present Evology: a heterogeneous, empirically calibrated multi-agent market ecology agent-based model to quantify endogenous interactions between US equity mutual funds, particularly Value and Growth investment styles. We outline the model design, validation and calibration approach and its potential for optimising investment strategies using machine learning algorithms.