Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks

TL;DR

This paper presents a novel approach combining Monte Carlo Tree Search and neural networks to solve complex multi-period financial planning models with high-dimensional assets and transaction costs, outperforming traditional methods.

Contribution

It introduces a new two-step algorithm integrating MCTS and neural networks, enabling solutions to large-scale financial models previously unsolvable by traditional methods.

Findings

The combined MCTS and neural network approach outperforms individual methods.

Successfully solves models with 50 time steps and 12 assets.

Achieves competitive results on complex regime switching models.

Abstract

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined method outperforms either approach alone, yielding competitive results. Several innovations improve the computations, including a variant of the upper confidence bound applied to trees (UTC) and a special lookup search. We compare the two-step algorithm with employing dynamic programs/neural networks. Both approaches solve regime switching models with 50-time steps and transaction costs with twelve asset categories. Heretofore, these problems have been outside the range of solvable optimization models via traditional algorithms.