论文标题
解决多周期财务计划模型：结合蒙特卡洛树搜索和神经网络
Solving Multi-Period Financial Planning Models: Combining Monte Carlo Tree Search and Neural Networks
论文作者
论文摘要
本文将MCTS算法介绍给金融界，并通过将蒙特卡洛树搜索算法与深层神经网络相结合来解决重要的多期财务计划模型。 MCT为神经网络提供了一个高级开始，因此合并的方法仅优于任何方法，从而产生竞争结果。几项创新改进了计算，包括应用于树木（UTC）的上限置信度的变体和特殊的查找搜索。我们将两步算法与采用动态程序/神经网络进行比较。两种方法都以50个时间步骤和交易成本解决了制度转换模型，而交易成本则为十二个资产类别。迄今为止，这些问题已经超出了通过传统算法的可解决优化模型的范围。
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.
