  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.
