主题：Dissecting Characteristics Nonparametrically（运用非参数方法剖析特征）
主讲人：Michael Weber，芝加哥大学布斯商德州扑克大小
金融学助理教授
日期：2016年11月02日（周三）
时间：下午2:30-4:00
地点：德州扑克大小
金融德州扑克大小
4号楼101教室
语言：英文
摘要：
We propose a nonparametric methodology to test which characteristics provide independent information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics simultaneously and estimate how they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns and nonlinearities are important. Our proposed methodology has higher out-of-sample explanatory power compared to linear panel regressions and increases Sharpe ratios by 70%.
主讲人简介:
Michael Weber joined Chicago Booth in 2014 as an Assistant Professor of Finance. He is also a faculty research fellow at the National Bureau of Economic Research, a member of the Macro Finance Society, and a research affiliate at the CES-ifo Research Network. Weber earned a Ph.D. and an M.S. both in Finance from the Haas School of Business at the University of California, Berkeley. His research interests include asset pricing, macroeconomics, international finance, and household finance. His work on downside risk in currency markets and other asset classes earned the 2013 AQR Insight Award. He has published in leading economics and finance journals such as the American Economic Review and the Journal of Financial Economics. Weber frequently presents his research at major international conferences such as the American Economic Association or the NBER Summer Institute.
