本课程的最终成绩, 由
共同组成. 两者各占 50 分.
如果你缺席了最后的小组报告, 也没有提前向教师说明原因. 小组报告视作0分; 根据上面的成绩算法, 你这门课的最终成绩也不会超过50分.
两人或三人一组, 自行组队. 组队完成后, 派一位组员告知教师小组的成员信息. (通过QQ或邮箱说明均可)
如果有同学5月25日前, 还没有告诉教师你的组员情况, 会被教师视作"落单成员". 教师会在 5月26日(周五)的课堂中, 在落单的成员中进行随机匹配.
每个小组的论文报告时间为 20 到 25 分钟. 可以全程只让一位同学进行报告, 也可以多位小组成员依次报告.
每个小组选讲一篇论文, 论文挑选范围见下面的 Paper List.
Lasso and market returns predictions
Machine learning v.s. traditional methods in measuring asset risk premiums
Trees and portfolio
Trees and consumer credit card behavior
Khandani, A. E., A. J. Kim, and A. W. Lo. 2010. Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance.
Butaru, F., Q. Chen, B. Clark, S. Das, A. W. Lo, and A. Siddique. 2016. Risk and risk management in the credit card industry. Journal of Banking & Finance.
Neural network and deep learning
Apaar Sadhwani, Kay Giesecke, Justin Sirignano. 2021. Deep Learning for Mortgage Risk. Journal of Financial Econometrics
J. B. Heaton, N. G. Polson, J. H. Witte. 2018. Deep Learning in Finance. Unpublished.
Bootstrap
Dimension reduction
Kelly, B., S. Pruitt, and Y. Su. 2019. Characteristics Are Covariances: A Unified Model of Risk and Return. Journal of Financial Economics.
Stefano Giglio and Dacheng Xiu. 2021. Asset Pricing with Omitted Factors Journal of Political Economy.
Shrinkage
Kozak, S., S. Nagel, and S. Santosh. 2019. Shrinking the Cross Section. Journal of Financial Economics.
Freyberger, J., A. Neuhierl, and M. Weber. 2019. Dissecting Characteristics Nonparametrically. Review of Financial Studies.