Hedging of Financial Derivative Contracts via Monte Carlo Tree Search
This addresses the key problem for financial engineers of constructing approximate replication strategies under realistic market conditions, representing an incremental improvement by applying an existing AI method to a new domain.
The paper tackles the problem of pricing and hedging derivative contracts in incomplete markets by introducing Monte Carlo Tree Search (MCTS) as a method to solve the stochastic optimal control problem, finding that MCTS learns stronger policies faster with higher sample efficiency and less over-fitting compared to Q-learning.
The construction of approximate replication strategies for pricing and hedging of derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for hedging under realistic market conditions have attracted significant interest. While research in the derivatives area 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 as a method to solve the stochastic optimal control problem behind the pricing and hedging tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework.