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        時  間:2022年7月4日 (周一) 20:30-22:30

        地  點:線上,Zoom會議號:84552623338,密碼:177545

        主  題:Structural Deep Learning in Conditional Asset Pricing

        主講人:范劍青 普林斯頓大學教授

        主持人:於州 教授

        主  辦:金沙9001w以誠為本、華東師范大學中國經濟研究中心

        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 )


        Jianqing Fan(范劍青),美國普林斯頓大學終身教授,Frederick L. Moore'18冠名金融講座教授,運籌與金融工程系教授和前任系主任,國際數理統計學會前主席,《Journal of Business and Economics Statistics》的主編。2000年榮獲國際統計學領域最高獎項COPSS總統獎,2006年榮獲洪堡基金會終身成就獎,2007年榮獲晨興華人數學家大會應用數學金獎,2013年獲泛華統計學會(International Chinese Association)“許寶祿獎”,2014年榮獲英國皇家統計學會授予的“Guy Medal”銀質獎章,2018年榮獲諾特資深學者獎(Noether Senior Scholar Award),此外,他還是國際統計學會(ISI)、國際數理統計學會(IMS)、美國科學促進會(AAAS)、美國統計學會(ASA)、計量金融學會(SOFIE)的會士,以及國際頂尖統計期刊《Annals of Statistics》、《Probability Theory and Related Field》及《Journal of Econometrics》等的前主編等。他的主要研究領域包括高維統計、機器學習、計量金融、時間序列、非參數建模,并在這些領域著有4本專著。