Hedging of Financial Derivative Contracts via Monte Carlo Tree Search

The construction of approximate replication strategies for derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for pricing and hedging under realistic market conditions have attracted significant interest. While financial research mostly focused on variations of $Q$-learning, in Artificial Intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search for the hedging of financial derivatives in realistic markets and shows that there are good reasons, both on the theoretical and practical side, to favor it over other Reinforcement Learning methods.