Trading Algorithms with Learning in Latent Alpha Models
38 Pages Posted: 28 May 2020
Date Written: July 2019
Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyzes 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 that ignore learning in the latent factors. We also provide calibration results for a particular model using Intel Corporation stock as an example.
Keywords: algorithmic trading, latent alpha, machine learning, partial information, statistical arbitrage, stochastic control
JEL Classification: G11, C61, C40
Suggested Citation: Suggested Citation