Dr. Kekun Wu

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

1. Murphy K P. Probabilistic machine learning: an introduction[M]. MIT press, 2022.
2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor. An Introduction to Statistical Learning with Applications in Python[M]. Springer Cham, 2023.
3. 邱锡鹏，神经网络与深度学习，机械工业出版社，https://nndl.github.io/, 2020.
4. Dixon M F, Halperin I, Bilokon P. Machine learning in Finance[M]. Springer International Publishing, 2020.
5. Nagel S. Machine learning in asset pricing[M]. Princeton University Press, 2021.
6. de Prado M M L. Machine learning for asset managers[M]. Cambridge University Press, 2020.
7. Ashwin Rao, Tikhon Jelvis. Foundations of Reinforcement Learning with Applications in Finance[M]. Stanford University, 2022.

1. 考核方式：课程项目
2. 课程项目内容（同时）包括：
   1. （全部或部分）复现经典论文
   2. 研究计划或综述
3. 要求
   1. 内容必须同时与机器学习和金融密切相关
   2. 无任何学术不端行为
4. DDL：15-Dec-2023, 20:00
5. 提交内容：课程报告+PPT+项目展示视频
6. 提交方式：百度网盘

bilibili video

bilibili video

Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, Part 8, Part 9

1. Ubiquant Market Prediction
2. Optiver Realized Volatility Prediction
3. Two Sigma Financial Modeling Challenge
4. Jane Street Market Prediction
5. G-Research Crypto Forecasting
6. Credit Card Fraud Detection
7. Home Credit Default Risk

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 L, Pelger M, Zhu J. Deep learning in asset pricing[J]. Management Science, 2023.

[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] Aït-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.

[23] Kelly B T, Xiu D. Financial machine learning[R]. National Bureau of Economic Research, 2023.

[24] Lopez-Lira A, Tang Y. Can chatgpt forecast stock price movements? return predictability and large language models[J]. arXiv preprint arXiv:2304.07619, 2023.

[25] Yu S, Xue H, Ao X, et al. Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning[J]. arXiv preprint arXiv:2306.12964, 2023.

[26] Blitz D, Hanauer M X, Hoogteijling T, et al. The Term Structure of Machine Learning Alpha[J]. Available at SSRN, 2023.

[27] Hambly B, Xu R, Yang H. Recent advances in reinforcement learning in finance[J]. Mathematical Finance, 2023, 33(3): 437-503.

[28] Murray S, Xia Y, Xiao H. Charting by machines[J]. Journal of Financial Economics, 2024, 153: 103791.

[29] Potluru V K, Borrajo D, Coletta A, et al. Synthetic Data Applications in Finance[J]. arXiv preprint arXiv:2401.00081, 2024.

[30] Murray S, Xia Y, Xiao H. Charting by machines[J]. Journal of Financial Economics, 2024, 153: 103791.