We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito’s lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights. (JEL G11, G12, G32)

中文翻译：

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连续时间金融的机器学习


我们开发了一种算法来求解金融领域的一大类非线性高维连续时间模型。我们使用深度学习来近似价值和策略函数，并表明自动微分和伊藤引理的结合可以计算精确的期望，从而导致与状态变量数量无关的可忽略不计的计算成本。我们说明了我们的方法对资产定价、公司财务和投资组合选择问题的适用性，并表明解决高维问题的能力使我们能够得出新的经济见解。 （捷尔 G11、G12、G32）