Title: Trading algorithms with learning in latent alpha models,
Abstract: Alpha signals for statistical arbitrage strategies are often driven by latent
factors. This paper analyses how to optimally trade with latent factors that
cause prices to jump and diffuse. Moreover, we account for the effect of the
trader's actions on quoted prices and the prices they receive from trading.
Under fairly general assumptions, we demonstrate how the trader can learn the
posterior distribution over the latent states, and explicitly solve the latent
optimal trading problem. We provide a verification theorem, and a methodology
for calibrating the model by deriving a variation of the
expectation-maximization algorithm. To illustrate the efficacy of the optimal
strategy, we demonstrate its performance through simulations and compare it to
strategies which ignore learning in the latent factors. We also provide
calibration results for a particular model using Intel Corporation stock as an
example.