经管学部“全球经管大师云讲堂”线上境外专家讲座

时间:2022-07-03浏览:10设置

时  间:2022年7月4日 (周一) 20:30-22:30

地  点:线上,Zoom会议号:84552623338,密码:177545

主  题:Structural Deep Learning in Conditional Asset Pricing

主讲人:范剑青 普林斯顿大学教授

主持人:於州 教授

主  办:经济与管理学部、华东师范大学中国经济研究中心

摘  要:

We develop new financial economics theory guided structural nonparametric methods for estimating conditional asset pricing models using deep neural networks, by employing time-varying conditional information on alphas and betas carried by firm-specific characteristics. Contrary to many applications of neural networks in economics, we can open the “black box” of machine learning predictions by incorporating financial economics theory into the learning, and provide an economic interpretation of the successful  predictions obtained from neural networks,  by decomposing the neural predictors as risk-related and mispricing components. Our estimation method starts with period-by-period  cross-sectional deep learning, followed by local PCAs to capture time-varying features such as latent factors of the model.  We formally establish the asymptotic theory of the structural deep-learning estimators, which apply to both in-sample fit and out-of-sample predictions. We also illustrate the “double-descent-risk” phenomena associated with over-parametrized predictions, which justifies the use of over-fitting machine learning methods. (Joint with Tracy Ke, Yuan Liao, and Andreas Neuhierl )

报告人简介:

Jianqing Fan(范剑青),美国普林斯顿大学终身教授,Frederick L. Moore'18冠名金融讲座教授,运筹与金融工程系教授和前任系主任,国际数理统计学会前主席,《Journal of Business and Economics Statistics》的主编。2000年荣获国际统计学领域最高奖项COPSS总统奖,2006年荣获洪堡基金会终身成就奖,2007年荣获晨兴华人数学家大会应用数学金奖,2013年获泛华统计学会(International Chinese Association)“许宝禄奖”,2014年荣获英国皇家统计学会授予的“Guy Medal”银质奖章,2018年荣获诺特资深学者奖(Noether Senior Scholar Award),此外,他还是国际统计学会(ISI)、国际数理统计学会(IMS)、美国科学促进会(AAAS)、美国统计学会(ASA)、计量金融学会(SOFIE)的会士,以及国际顶尖统计期刊《Annals of Statistics》、《Probability Theory and Related Field》及《Journal of Econometrics》等的前主编等。他的主要研究领域包括高维统计、机器学习、计量金融、时间序列、非参数建模,并在这些领域著有4本专著。

 


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