Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model

TL;DR

This paper introduces XGB-Chiarella, a machine learning calibrated agent-based model that accurately simulates intra-day financial price data, revealing the roles of different trader types in price formation.

Contribution

It presents a novel calibration method using XGBoost for the Extended Chiarella model, improving realism and universality in intra-day market simulations.

Findings

XGB-Chiarella accurately replicates real market stylized facts.

The model captures the interactions of trader types in price formation.

It generates realistic intra-day price series across multiple stocks.

Abstract

This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners.