Dr. Kekun Wu

• Email: kkwu#zuel.edu.cn (Pls replace # with @)
• Wechat:

Jupyter Notebooks for the course are accessible via the cloud service with “t7me” as the password.

1. Murphy K P. Probabilistic machine learning: an introduction[M]. MIT press, 2022.
2. Dixon M F, Halperin I, Bilokon P. Machine learning in Finance[M]. Springer International Publishing, 2020.
3. Nagel S. Machine learning in asset pricing[M]. Princeton University Press, 2021.
4. de Prado M M L. Machine learning for asset managers[M]. Cambridge University Press, 2020.
5. Ashwin Rao, Tikhon Jelvis. Foundations of Reinforcement Learning with Applications in Finance[M]. Stanford University, 2022.

查找文献的方法

[1] Athey S. The impact of machine learning on economics[J]. The economics of artificial intelligence: An agenda, 2018: 507-547.

[2] Athey S, Imbens G W. Machine learning methods that economists should know about[J]. Annual Review of Economics, 2019, 11: 685-725.

[3] Mullainathan S, Spiess J. Machine learning: an applied econometric approach[J]. Journal of Economic Perspectives, 2017, 31(2): 87-106.

[4] Cohen, Samuel N. and Snow, Derek and Szpruch, Lukasz, Black-Box Model Risk in Finance (February 9, 2021). Available at SSRN: https://ssrn.com/abstract=3782412 or http://dx.doi.org/10.2139/ssrn.3782412

[5] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225.

[6] Erel I, Stern L H, Tan C, et al. Selecting directors using machine learning[J]. The Review of Financial Studies, 2021, 34(7): 3226-3264.

[7] Li K, Mai F, Shen R, et al. Measuring corporate culture using machine learning[J]. The Review of Financial Studies, 2021, 34(7): 3265-3315.

[8] Amel-Zadeh, Amir and Calliess, Jan-Peter and Kaiser, Daniel and Roberts, Stephen, Machine Learning-Based Financial Statement Analysis (November 25, 2020). Available at SSRN: https://ssrn.com/abstract=3520684 or http://dx.doi.org/10.2139/ssrn.3520684

[9] Gu S, Kelly B, Xiu D. Empirical asset pricing via machine learning[J]. The Review of Financial Studies, 2020, 33(5): 2223-2273.

[10] Giglio, Stefano and Kelly, Bryan T. and Xiu, Dacheng, Factor Models, Machine Learning, and Asset Pricing (October 15, 2021). Available at SSRN: https://ssrn.com/abstract=3943284 or http://dx.doi.org/10.2139/ssrn.3943284

[11] Gu S, Kelly B, Xiu D. Autoencoder asset pricing models[J]. Journal of Econometrics, 2021, 222(1): 429-450.

[12] Kelly B T, Pruitt S, Su Y. Characteristics are covariances: A unified model of risk and return[J]. Journal of Financial Economics, 2019, 134(3): 501-524.

[13] Kozak S, Nagel S, Santosh S. Shrinking the cross-section[J]. Journal of Financial Economics, 2020, 135(2): 271-292.

[14] Tobek O, Hronec M. Does it pay to follow anomalies research? machine learning approach with international evidence[J]. Journal of Financial Markets, 2021, 56: 100588.

[15] Baba Yara, Fahiz and Boyer, Brian H. and Davis, Carter, The Factor Model Failure Puzzle (November 19, 2021). Available at SSRN: https://ssrn.com/abstract=3967588 or http://dx.doi.org/10.2139/ssrn.3967588

[16] Chen, Luyang and Pelger, Markus and Zhu, Jason, Deep Learning in Asset Pricing (April 4, 2019). Available at SSRN: https://ssrn.com/abstract=3350138 or http://dx.doi.org/10.2139/ssrn.3350138

[17] Bryzgalova, Svetlana and Pelger, Markus and Zhu, Jason, Forest Through the Trees: Building Cross-Sections of Stock Returns (September 25, 2020). Available at SSRN: https://ssrn.com/abstract=3493458 or http://dx.doi.org/10.2139/ssrn.3493458

[18] Giglio S, Liao Y, Xiu D. Thousands of alpha tests[J]. The Review of Financial Studies, 2021, 34(7): 3456-3496.

[19] Duarte V, Fonseca J, Goodman A S, et al. Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle[R]. National Bureau of Economic Research, 2021.

[20] Jiang, Jingwen and Kelly, Bryan T. and Xiu, Dacheng, (Re-)Imag(in)ing Price Trends (December 1, 2020). Chicago Booth Research Paper No. 21-01, Available at SSRN: https://ssrn.com/abstract=3756587 or http://dx.doi.org/10.2139/ssrn.3756587

[21] Ait-Sahalia Y, Xiu D. Using principal component analysis to estimate a high dimensional factor model with high-frequency data[J]. Journal of Econometrics, 2017, 201(2): 384-399.

[22] Aït-Sahalia Y, Xiu D. Principal component analysis of high-frequency data[J]. Journal of the American Statistical Association, 2019, 114(525): 287-303.

1. 考核方式：课程项目
2. 课程项目形式：（全部或部分）复制经典论文
3. DDL：15-Jun-2022, 20:00
4. 提交内容：课程报告+PPT+项目展示视频
5. 提交方式：百度网盘（提前30天提供搜集二维码）
6. 奖励：主动参加17周课堂展示的起评分以100分计（其他人起评分按95计算）