Trading algorithms with learning in latent alpha models
This work addresses the challenge of statistical arbitrage for traders by incorporating learning in latent factors, though it appears incremental as it builds on existing latent factor models with specific methodological enhancements.
The paper tackles the problem of optimal trading with latent alpha factors that cause price jumps and diffusions, while accounting for the trader's market impact. The result is an explicit solution to the latent optimal trading problem, with simulations showing improved performance over strategies that ignore learning in latent factors, and calibration demonstrated using Intel Corporation stock.
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.