  We consider a trader who aims to liquidate a large position in the presence
of an arbitrageur who hopes to profit from the trader's activity. The
arbitrageur is uncertain about the trader's position and learns from observed
price fluctuations. This is a dynamic game with asymmetric information. We
present an algorithm for computing perfect Bayesian equilibrium behavior and
conduct numerical experiments. Our results demonstrate that the trader's
strategy differs significantly from one that would be optimal in the absence of
the arbitrageur. In particular, the trader must balance the conflicting desires
of minimizing price impact and minimizing information that is signaled through
trading. Accounting for information signaling and the presence of strategic
adversaries can greatly reduce execution costs.
