Mean Field Games with Partial Information for Algorithmic Trading

Understand

Financial markets are often driven by latent factors which traders cannot observe.

• Here, we address an algorithmic trading problem with collections of heterogeneous agents who aim to perform optimal execution or statistical arbitrage, where all agents filter the latent states of the world, and their trading actions have permanent and temporary price impact.
• This leads to a large stochastic game with heterogeneous agents.
• We solve the stochastic game by investigating its mean-field game (MFG) limit, with sub-populations of heterogeneous agents, and, using a convex analysis approach, we show that the solution is characterized by a vector-valued forward-backward stochastic differential equation (FBSDE).