教机器学习经济学
发布时间：2025-06-24
6月
26
|
时间和日期
|
2025-06-26 (星期四) 01:30 上午
-
12:00 下午
|
| 标题: | 教机器学习经济学 |
| 日期和时间: |
2025年6月26日（周四） 10:30-12:00 |
| 地点 | 综合教学楼D804会议室 |
| 主讲人: |
陈晖 麻省理工大学 |
| 摘要: | Structural models in economics 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 struggle to generalize beyond the confines of training data. We propose a transfer learning framework that incorporates economic restrictions from a structural model into a machine learning model. Specifically, we first construct a neural network representation of the structural model by training on the synthetic data generated by the structural model and then fine-tune the network using empirical data. When applied to option pricing, the transfer learning model significantly outperforms the structural model, a conventional deep neural network, and several alternative approaches for bringing in economic restrictions. The out-performance is more significant i) when the sample size of empirical data is small, ii) when market conditions change relative to the training data, or iii) when the degree of structural model misspecification is likely to be low. |
| 主讲人简介: | 陈晖现任麻省理工学院斯隆管理学院野村金融讲席教授。他的研究领域是资产定价及其与公司金融的关系。陈教授尤其专注于宏观经济和利率期限结构、信用风险、融资及投资决策之间的相互影响。他近期的研究项目包括应用经济周期模型来解释企业融资行为和公司债券定价，以及不完全市场对创业融资和投资的影响分析。陈晖教授于2000年获得中山大学经济金融学学士学位，2002年获得密歇根大学数学硕士学位，2007年获得芝加哥大学金融学博士学位。 |
