Trading with the Crowd

TL;DR

This paper models and analyzes how multiple financial agents optimally liquidate assets considering transient price impacts and predictive signals, revealing the relationship between order flow and market signals in crowded trading environments.

Contribution

It introduces a multi-player stochastic differential game and its mean field limit, providing explicit equilibrium strategies and convergence rates for large agent populations.

Findings

Derived explicit Nash-equilibrium strategies for finite agents.

Proved convergence of finite-player game to mean field game at rate O(N^{-2}).

Showed mean field strategies approximate finite-player Nash equilibria.

Abstract

We formulate and solve a multi-player stochastic differential game between financial agents who seek to cost-efficiently liquidate their position in a risky asset in the presence of jointly aggregated transient price impact, along with taking into account a common general price predicting signal. The unique Nash-equilibrium strategies reveal how each agent's liquidation policy adjusts the predictive trading signal to the aggregated transient price impact induced by all other agents. This unfolds a quantitative relation between trading signals and the order flow in crowded markets. We also formulate and solve the corresponding mean field game in the limit of infinitely many agents. We prove that the equilibrium trading speed and the value function of an agent in the finite $N$-player game converges to the corresponding trading speed and value function in the mean field game at rate $O(N^{-2})$. In addition, we prove that the mean field optimal strategy provides an approximate Nash-equilibrium for the finite-player game.