====== Topics in Machine Learning and 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 | Slides |
| 1 | A Brief Introduction to ML | wk1 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L01-Introduction-slides.html|L01-introduction-slides]] |
| 2 | Shallow Learning Algorithms | wk2-wk5 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L02-Regression-slides.html|L02-regression-slides]]\\ [[http://kktim.cn/teaching/mlinfin/MLinFin-L03-Classification-slides.html|L03-classification-slides]]\\ [[http://kktim.cn/teaching/mlinfin/MLinFin-L04-Trees-slides.html|L04-trees-and-ensemble-learning-slides]]\\ [[http://kktim.cn/teaching/mlinfin/MLinFin-L05-Unsupervised-Learning-slides.html|L05-unsupervised-learning-slides]] |
| 3 | Deep Neural Networks | wk6-7 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L06-Deep-Learning-slides.html|L06-deep-learning-I-slides]], [[http://kktim.cn/teaching/mlinfin/MLinFin-L06-Deep-Learning-II-slides.html|L06-Deep-Learning-II-slides]] |
| 4 | MDP & Reinforcement Learning | wk8 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L06-MDP-in-Finance-slides.html|L07a-MDP-slides]]\\  [[http://kktim.cn/teaching/mlinfin/MLinFin-L07-Advances-of-RL-in-Finance-slides.html|L07b-RL-slides]] |
| 5 | SP1: Machine Learning & Asset Pricing & Financial Bigdata | by yourself | [[https://www.nber.org/papers/w31502|NBER Working Paper: Financial Machine Learning]] |
| 6 | ST2: Machine Learning and Causal Inference | by yourself | [[https://www.bilibili.com/video/BV19Y4y117st/?vd_source=0a4eefc320d2637b710b584ebc1ce471|NBER SI 2015 Methods Lectures - Machine Learning for Economists]]\\ [[https://www.bilibili.com/video/BV1KY4y127Qs/?vd_source=0a4eefc320d2637b710b584ebc1ce471|2018 AEA Continuing Education Webcasts: Machine Learning and Econometrics (Susan Athey, Guido Imbens)]]\\ [[https://www.gsb.stanford.edu/faculty-research/centers-initiatives/sil/research/methods/ai-machine-learning/short-course|Machine Learning & Causal Inference: A Short Course]] | 



===== 教材与参考书 =====

  - Murphy K P. Probabilistic machine learning: an introduction[M]. MIT press, 2022.
  - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor. An Introduction to Statistical Learning with Applications in Python[M]. Springer Cham, 2023.
  - 邱锡鹏，神经网络与深度学习，机械工业出版社，https://nndl.github.io/, 2020.
  - 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.

===== 考核方式 =====

  - 考核方式：课程项目
  - 课程项目内容（**同时**）包括：
    - （全部或部分）复现经典论文
    - 研究计划或综述
  - 要求
    - 内容必须**同时**与**机器学习**和**金融**密切相关
    - 无任何学术不端行为
  - **DDL：15-Dec-2023, 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 
{{:mycourse:mlinfin2023-real-final.jpg?800|}}

===== 学习资源 =====

==== NBER SI 2015 Methods Lectures - Machine Learning for Economists ====
[[https://www.bilibili.com/video/BV19Y4y117st/?vd_source=0a4eefc320d2637b710b584ebc1ce471|bilibili video]]




==== 2018 AEA Continuing Education Webcasts: Machine Learning and Econometrics (Susan Athey, Guido Imbens) ====

[[https://www.bilibili.com/video/BV1KY4y127Qs/?vd_source=0a4eefc320d2637b710b584ebc1ce471|bilibili video]]

[[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-1|Part 1]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-2|Part 2]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-3|Part 3]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-4|Part 4]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-5|Part 5]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-6|Part 6]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-7|Part 7]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-8|Part 8]], [[https://www.aeaweb.org/webcasts/2018/machine-learning-and-econometrics-part-9|Part 9]]

==== Kaggle Data & Competitions ====

  - [[https://www.kaggle.com/competitions/ubiquant-market-prediction/overview|Ubiquant Market Prediction]]
  - [[https://www.kaggle.com/c/optiver-realized-volatility-prediction|Optiver Realized Volatility Prediction]]
  - [[https://www.kaggle.com/c/two-sigma-financial-modeling/|Two Sigma Financial Modeling Challenge]]
  - [[https://www.kaggle.com/c/jane-street-market-prediction|Jane Street Market Prediction]]
  - [[https://www.kaggle.com/c/g-research-crypto-forecasting|G-Research Crypto Forecasting]]
  - [[https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud|Credit Card Fraud Detection]]
  - [[https://www.kaggle.com/c/home-credit-default-risk|Home Credit Default Risk]]

===== 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 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.