Uncommon Factors for Bayesian Asset Clusters

主讲人:Lin William Cong(丛林)
主讲人简介:
康奈尔大学约翰逊商学院Rudd家族管理学讲席教授及金融学终身教授,联合创立了康奈尔金融科技中心并任主任,是中国经济研究,社科研究等中心的教授。丛林教授是美国国家经济研究局(NBER)资产定价部门研究学者, 考夫曼创业基金青年学者,2020年Poets & Quants世界最佳商学院教授,前华尔街区块链联盟顾问,任包括Management Science在内的多家顶级期刊主编。在加入康奈尔大学之前,丛林教授曾任斯坦福经济政策研究和发展中国家研究所杰出学者,芝加哥大学商学院金融助理教授和博导,东亚研究中心教授,并首创量本投资MBA/EMBA教程。他也是加密和区块链经济研究(CBER)和金融AI和大数据研究(ABFR)等国际论坛的发起人。
主持人:
简介:

 Extant model selection methods either assume homogeneous data observations which follow one common model or search in restricted space heterogeneous models for exogenous given subsets of observations. For panel data in economics or finance may require heterogeneous model selection for each (potentially unknown) clusters the observations naturally form. We invent a novel approach to solving the joint problem of observation clustering and model selection. 

Our Clustered Bayesian Model (CBM) combines tree-based supervised clustering algorithms and Bayesian modeling with the spike-and-slab prior distributions.
First, cross-sectional observations are clustered recursively into leaves by a tree that grows according to the marginal likelihood jointly for all selected leaf models. Second, observations in each leaf fit a model separately with uncommon variables using data in  all periods. Third, the Bayesian model allows time-varying coefficients driven by observation subject characteristics under modest computational costs.
 
We apply CBM to the (imbalanced) panel of individual stock returns for estimating and selecting observable factor models. CBM splits cross-sectional stock returns by firm characteristics and selects potentially distinct factor models for each leaf clusters. Empirically, we find most asset clusters can be explained by the list of published factors, but some have significant alphas. CBM provides a graphical tree-leaf path with firm characteristics to analyze these mispriced stocks. Finally, we provide Bayesian inference on factor usefulness and the fundamental and macroeconomic sources of mispricing clusters.
时间:2022-08-05(Friday)09:00-10:40
地点:zoom 线上会议 会议号:880 4630 2096 密码:023972
讲座语言:English
期数: “邹至庄讲座”杰出学者论坛(第七期)
主办单位:厦门大学邹至庄经济研究院、中国科学院预测科学研究中心、国家自然科学基金“计量建模与经济政策研究基础科学中心”
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类型:系列讲座
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