====== Machine Learning in Finance ======

===== Instructor =====

Dr. Kekun Wu

  * Email: kkwu#zuel.edu.cn (Pls replace # with @)
  * Wechat: {{ :mycourse:t7me_qr_code.jpg?nolink&150 |}}
===== 教学计划 =====

| 序号 | 主题 | 时间 | 讲义 |
| 1 | Introduction | wk1 (wk10) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L01-Introduction.html|L01]]|
| 2 | Linear Regression | wk2 (wk11) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L02-Linear-Regression.html|L02]] | 
| 3 | Dimension Reduction | wk3 (wk12) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L03-Dimensionality-Reducion.html|L03]] |
| 4 | Trees, Forest, and Boosting | wk4 (wk13) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L04-Trees-Forests-Bagging-and-Boosting.html|L04]] | 
| 5 | Deep Neural Networks | wk5-6 (wk14-15) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L05a-NN-for-Structured-Data.html|L05a]] | 
| 6 | DNN for Sequence Data | wk7 (wk16) | [[http://kktim.cn/teaching/mlinfin/MLinFin-L05d1-Sequence-Modeling.html|L05d1]], [[http://kktim.cn/teaching/mlinfin/MLinFin-L05d2-Probabilistic-Sequence-Modeling.html|L05d2]],  [[http://kktim.cn/teaching/mlinfin/MLinFin-L06-MDP-in-Finance.html|L06]] | 
| 7 | Course Project Presentation | wk8 (wk17) | x | 

**Jupyter Notebooks** for the course are accessible via the [[https://eyun.baidu.com/s/3ggfohk3|cloud service]] with "t7me" as the password.
===== 教材与参考书 =====

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


===== 主要参考文献 =====

**[[:literature_search|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.

===== 考核方式 =====

  - 考核方式：课程项目
  - 课程项目形式：（全部或部分）复制经典论文
  - DDL：15-Jun-2022, 20:00
  - 提交内容：课程报告+PPT+项目展示视频
  - 提交方式：百度网盘（提前30天提供搜集二维码） {{:mycourse:mlinfin-report-2022.jpg?400|}}
  - 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）