Heterogeneous Trader Responses to Macroeconomic Surprises: Simulating Order Flow Dynamics

TL;DR

This paper develops a simulation framework with different trader archetypes to analyze how various market participants react to macroeconomic surprises, revealing insights into order flow dynamics and trader behavior.

Contribution

It introduces a calibrated simulation model embedding four trader types within an extended CAPM to study reactions to macro shocks, incorporating utility-based order choices.

Findings

Higher information and lower risk aversion lead to larger trades and higher wealth.

Retail investors tend to underreact with smaller, more dispersed trades.

Ambient liquidity increases the sensitivity of order flow to surprises.

Abstract

Understanding how market participants react to shocks like scheduled macroeconomic news is crucial for both traders and policymakers. We develop a calibrated data generation process DGP that embeds four stylized trader archetypes retail, pension, institutional, and hedge funds into an extended CAPM augmented by CPI surprises. Each agents order size choice is driven by a softmax discrete choice rule over small, medium, and large trades, where utility depends on risk aversion, surprise magnitude, and liquidity. We aim to analyze each agent's reaction to shocks and Monte Carlo experiments show that higher information, lower aversion agents take systematically larger positions and achieve higher average wealth. Retail investors under react on average, exhibiting smaller allocations and more dispersed outcomes. And ambient liquidity amplifies the sensitivity of order flow to surprise shocks. Our framework offers a transparent benchmark for analyzing order flow dynamics around macro releases and suggests how real time flow data could inform news impact inference.