Teaching Economics to the Machines
演讲者
时间
2025年11月28日 16:00 至 17:30
地点
Shuangqing-B627
线上
Zoom 230 432 7880
(BIMSA)
摘要
While structural models in economics can offer valuable insights, they often suffer from a poor fit with the data and demonstrate suboptimal forecasting performances. Machine learning models, in contrast, offer rich flexibility but are prone to overfitting and tend to struggle to generalize beyond the confines of training data. We propose a novel framework that incorporates economic restrictions from a structural model into a machine learning model through transfer learning. Specifically, we first construct a neural-network representation of the structural model by training it on the synthetic data generated by the structural model, and then fine-tune the network using real data. In an application to option pricing, the transfer learning model significantly outperforms both the structural model and a conventional data-driven deep neural network. The out-performance is more significant when the sample size of real data is small or under volatile market conditions.
演讲者介绍
清华大学社会科学学院经济学研究所教授，至善书院院长。主要研究方向为商品市场（包括数字资产）、金融科技和数字经济。在Journal of Finance, Review of Financial Studies, Management Science等顶级英文期刊上发表多篇论文，目前担任国际期刊Quantitative Finance的执行编辑以及Journal of Commodity Markets的副主编。研究成果得到美国期货管理委员会、联合国大宗商品报告以及多家媒体的报道。入选2020、2021年爱思唯尔中国高被引学者。