  We study a dynamic stopping game between a principal and an agent. The agent
is privately informed about his type. The principal learns about the agent's
type from a noisy performance measure, which can be manipulated by the agent
via a costly and hidden action. We fully characterize the unique Markov
equilibrium of this game. We find that terminations/market crashes are often
preceded by a spike in (expected) performance. Our model also predicts that,
due to endogenous signal manipulation, too much transparency can inhibit
learning. As the players get arbitrarily patient, the principal elicits no
useful information from the observed signal.
