Machine Learning for Continuous-Time Finance
开发了一种求解金融中非线性高维连续时间模型的算法，利用深度学习和伊藤引理实现精确期望计算，计算成本与状态变量数无关，适用于资产定价、公司金融和投资组合选择问题。
Abstract 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.