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普林斯顿大学 范剑青教授:Statistical machine learning for Financial Prediction and Inference

([西财新闻] 发布于 :2018-06-11 )

光华讲坛——社会名流与企业家论坛第4998

 

Statistical machine learning for Financial Prediction and Inference

主讲人普林斯顿大学 范剑青教授

主持人统计学院 林华珍教授

2018611日(星期一)下午4:00-5:00

西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心  统计学院  科研处

 

主讲人简介:

范剑青(Jianqing Fan),普林斯顿大学Frederick L. Moore18金融学讲座教授,复旦大学大数据学院及大数据研究院双院院长;2000年荣获COPSS总统奖(该奖为国际统计学领域的最高奖);2006年当选国际数理统计学会主席,同年获洪堡基金会的终身成就奖;2007年获晨兴华人数学家大会应用数学金奖(该奖被誉为华人应用数学界的最高奖);2008年获泛华统计学会授予的杰出成就奖;2009年获美国文理与艺术界著名的GUGGENHEIM Fellow2012年获入选国家“千人计划”项目并当选台湾“中央研究院”院士;2013年获泛华统计学会的“许宝禄奖”(首届);2014年获英国皇家统计学会的“Guy 奖”的银质奖章(仅有两位华人统计学家获此荣誉);2018年获诺特资深学者奖(Noether Senior Scholar Award)。 

现为国际一流期刊 JoE的联合主编;JASA的副主编;国际统计学会(International Statistical InstituteFellow;国际数理统计学会(Institute of Mathematical StatisticsFellow;美国统计学会(American Statistical AssociationFellow、美国科学促进会(American Association for the Advancement of ScienceFellow、计量金融学会(The Society for Financial EconometricsFellow

主要研究领域为高维统计、机器学习、大数据科学、经济学、金融学、生物信息等。在AoSJASAJMLREconometrica, JRSSB, BiometrikaJoE Journal of Financial Economics等国际一流期刊上发表学术论文近120篇。

更多详情见:

http://orfe.princeton.edu/~jqfan/publications-general.html

内容提要:

This talk will introduce recent developments of statistical machine learning methods for analysis of Big Data in Finance. Motivated by stylized features such as heavy-tails and cross-sectional dependence, we introduce simple a simple method for dependence adjustment and robustfication principles for dealing with heavy tail Big-Data issues in financial data. We then apply factor models to extract latent factors for prediction, Factor-Adjusted Robust Multiple test (FARM-test) and model selection (FARM-select), and to complete large covariance matrices for high-frequency financial data.  We provide new statistical machine learning methods that extract latent factors that can partially be explained by several observed explanatory proxies such as the Fama-French factors in financial returns and consumption-wealth variables, financial ratios, yield spreads, and term structures in diffusion index construction. These latent factors are then further combined to predict economic and financial outcomes such as bond risk premia and high-frequency financial returns, via a multi-index model.  Empirically, we apply the model to combine high-frequency signals for predicting financial returns, to forecast US bond risk premia, and to test Fama-French factor models.


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