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mycourse:machine_learning_and_finance [2024/09/13 21:18] – [教材与参考书] kkmycourse:machine_learning_and_finance [2025/12/01 13:39] (当前版本) kk
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   * Wechat: {{ :mycourse:t7me_qr_code.jpg?nolink&150 |}}   * Wechat: {{ :mycourse:t7me_qr_code.jpg?nolink&150 |}}
 ===== 教学计划 ===== ===== 教学计划 =====
 +
 +  * ver2025
 +
 +| 序号 | 主题 | 时间 | Slides |
 +| 1 | Introduction | wk1 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L01-Introduction-slides-v2025.html|L01-introduction-slides]] |
 +| 2 | Shallow Learning | wk2 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L02-Shallow-slides-v2025.html|L02-shallow-slides]];[[http://kktim.cn/teaching/mlinfin/MLinFin-L02-Shallow-slides-v2025-CN.html|L02中文机翻版]] |
 +| 3 | Deep Learning | wk3 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L03-DL-slides-v2025.html|L03-DL-slides]];[[http://kktim.cn/teaching/mlinfin/MLinFin-L03-DL-slides-v2025-CN.html|L03中文机翻版]] |
 +| 4 | Reinforcement Learning | wk4 | [[http://kktim.cn/teaching/mlinfin/MLinFin-L04-RL-slides-v2025.html|L04-RL-slides]]; [[http://kktim.cn/teaching/mlinfin/MLinFin-L04-RL-slides-v2025-CN.html|L04中文机翻版]] |
 +| 5 | Big Data & ML | wk5 |  |
 +| 6 | Empirical Asset Pricing & ML | wk6 |  |
 +| 7 | Causal Inference & ML | wk7 |  |
 +| 8 | LLMs & Agents | wk8 |  |
 +
 +
 +  * ver2024
  
 | 序号 | 主题 | 时间 | Slides | | 序号 | 主题 | 时间 | Slides |
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 | 2 | Shallow Learning Algorithms | wk2-wk3 | [[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]] | | 2 | Shallow Learning Algorithms | wk2-wk3 | [[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 | wk4 | [[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]] | | 3 | Deep Neural Networks | wk4 | [[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 | Literature Studies | wk5-wk8 | - |+| 4 | Literature Studies | wk5-wk8 | [[http://kktim.cn/teaching/mlinfin/MLinFin-LXX-Presentation-schedule-slides.html|Presentation Schedule]] |
 | 5 | MDP & Reinforcement Learning | by yourself | [[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 | MDP & Reinforcement Learning | by yourself | [[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]] |
 | 6 | SP1: Machine Learning & Asset Pricing & Financial Bigdata | by yourself | [[https://www.nber.org/papers/w31502|NBER Working Paper: Financial Machine Learning]] | | 6 | SP1: Machine Learning & Asset Pricing & Financial Bigdata | by yourself | [[https://www.nber.org/papers/w31502|NBER Working Paper: Financial Machine Learning]] |
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   - Ashwin Rao, Tikhon Jelvis. Foundations of Reinforcement Learning with Applications in Finance[M]. Stanford University, 2022.   - Ashwin Rao, Tikhon Jelvis. Foundations of Reinforcement Learning with Applications in Finance[M]. Stanford University, 2022.
   - Denev A, Amen S. The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers[M]. John Wiley & Sons, 2020.   - Denev A, Amen S. The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers[M]. John Wiley & Sons, 2020.
 +  - Gaillac C, L'Hour J. Machine Learning for Econometrics[M]. Oxford University Press, 2025.
  
 ===== 考核方式 ===== ===== 考核方式 =====
  
   - 考核方式:课程项目   - 考核方式:课程项目
-    - 项目展示:40% +    - 平时(40%):论文展示 
-    - 项目报告:60%+    - 期末(60%):项目展示+ 项目报告
   - 课程项目内容(**同时**)包括:   - 课程项目内容(**同时**)包括:
     - 文献评述     - 文献评述
行 45: 行 61:
     - 内容必须**同时**与**机器学习**和**金融研究**密切相关     - 内容必须**同时**与**机器学习**和**金融研究**密切相关
     - 无任何学术不端行为     - 无任何学术不端行为
-  - **DDL:15-Dec-2024, 20:00**:项目报告+PPT+  - **DDL:31-Dec-2025, 20:00**:项目报告+PPT
   - 提交方式:百度网盘    - 提交方式:百度网盘 
  
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  [14] Kozak S, Nagel S, Santosh S. Shrinking the cross-section[J]. Journal of Financial Economics, 2020, 135(2): 271-292.  [14] Kozak S, Nagel S, Santosh S. Shrinking the cross-section[J]. Journal of Financial Economics, 2020, 135(2): 271-292.
  
- [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+ [15] Baba Yara, Fahiz and Boyer, Brian H. and Davis, Carter, Messy Asset Pricing: Can AI Models Lead to a Consensus? (November 19, 2021). Available at SSRN: https://ssrn.com/abstract=3967588 or http://dx.doi.org/10.2139/ssrn.3967588
  
  [16] Bianchi D, Büchner M, Tamoni A. Bond risk premiums with machine learning[J]. The Review of Financial Studies, 2021, 34(2): 1046-1089.  [16] Bianchi D, Büchner M, Tamoni A. Bond risk premiums with machine learning[J]. The Review of Financial Studies, 2021, 34(2): 1046-1089.
行 174: 行 190:
  [45] Bybee L, Kelly B, Manela A, et al. Business news and business cycles[J]. The Journal of Finance, 2021.  [45] Bybee L, Kelly B, Manela A, et al. Business news and business cycles[J]. The Journal of Finance, 2021.
  
- [46] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225.+ <del>[46] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225. 
 +</del>
  
  [47] Bose D, Cordes H, Nolte S, et al. Decision weights for experimental asset prices based on visual salience[J]. The Review of Financial Studies, 2022, 35(11): 5094-5126.  [47] Bose D, Cordes H, Nolte S, et al. Decision weights for experimental asset prices based on visual salience[J]. The Review of Financial Studies, 2022, 35(11): 5094-5126.
行 200: 行 217:
  [51] Kim, Alex G. and Muhn, Maximilian and Nikolaev, Valeri V., Financial Statement Analysis with Large Language Models (May 20, 2024). Chicago Booth Research Paper Forthcoming, Fama-Miller Working Paper, Available at SSRN: https://ssrn.com/abstract=4835311 or http://dx.doi.org/10.2139/ssrn.4835311  [51] Kim, Alex G. and Muhn, Maximilian and Nikolaev, Valeri V., Financial Statement Analysis with Large Language Models (May 20, 2024). Chicago Booth Research Paper Forthcoming, Fama-Miller Working Paper, Available at SSRN: https://ssrn.com/abstract=4835311 or http://dx.doi.org/10.2139/ssrn.4835311
  
- [52] Murray S, Xia Y, Xiao H. Charting by machines[J]. Journal of Financial Economics, 2024, 153: 103791. + [52] Potluru V K, Borrajo D, Coletta A, et al. Synthetic Data Applications in Finance[J]. arXiv preprint arXiv:2401.00081, 2024.
- +
- [53] Potluru V K, Borrajo D, Coletta A, et al. Synthetic Data Applications in Finance[J]. arXiv preprint arXiv:2401.00081, 2024.+
  
 **Machine Learning and Financial Risk Management** **Machine Learning and Financial Risk Management**
  
- [54] 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+ [53] 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
  
- [55] Fuster A, Goldsmith‐Pinkham P, Ramadorai T, et al. Predictably unequal? The effects of machine learning on credit markets[J]. The Journal of Finance, 2022, 77(1): 5-47.+ [54] Fuster A, Goldsmith‐Pinkham P, Ramadorai T, et al. Predictably unequal? The effects of machine learning on credit markets[J]. The Journal of Finance, 2022, 77(1): 5-47.
  
- [56] Luong T M, Scheule H, Wanzare N. Impact of mortgage soft information in loan pricing on default prediction using machine learning[J]. International Review of Finance, 2023, 23(1): 158-186.+ [55] Luong T M, Scheule H, Wanzare N. Impact of mortgage soft information in loan pricing on default prediction using machine learning[J]. International Review of Finance, 2023, 23(1): 158-186.
  
- [57] Koelbl M, Laschinger R, Steininger B I, et al. Revealing the risk perception of investors using machine learning[J]. The European Journal of Finance, 2024: 1-27.+ [56] Koelbl M, Laschinger R, Steininger B I, et al. Revealing the risk perception of investors using machine learning[J]. The European Journal of Finance, 2024: 1-27.
  
 **Machine Learning and Corporate Finance** **Machine Learning and Corporate Finance**
  
- [58] 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.+ [57] 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.
  
- [59] 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.+ [58] 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.
  
- [60] Bubb R, Catan E M. The party structure of mutual funds[J]. The Review of Financial Studies, 2022, 35(6): 2839-2878.+ [59] Bubb R, Catan E M. The party structure of mutual funds[J]. The Review of Financial Studies, 2022, 35(6): 2839-2878.
  
- [61] Cao S, Jiang W, Yang B, et al. How to talk when a machine is listening: Corporate disclosure in the age of AI[J]. The Review of Financial Studies, 2023, 36(9): 3603-3642.+ [60] Cao S, Jiang W, Yang B, et al. How to talk when a machine is listening: Corporate disclosure in the age of AI[J]. The Review of Financial Studies, 2023, 36(9): 3603-3642.
  
- [62] Babina T, Fedyk A, He A, et al. Artificial intelligence, firm growth, and product innovation[J]. Journal of Financial Economics, 2024, 151: 103745.+ [61] Babina T, Fedyk A, He A, et al. Artificial intelligence, firm growth, and product innovation[J]. Journal of Financial Economics, 2024, 151: 103745.
  
- [63] Gofman M, Jin Z. Artificial intelligence, education, and entrepreneurship[J]. The Journal of Finance, 2024, 79(1): 631-667.+ [62] Gofman M, Jin Z. Artificial intelligence, education, and entrepreneurship[J]. The Journal of Finance, 2024, 79(1): 631-667.
  
- [64] Halskov K. Improving Merger Arbitrage Returns with Machine Learning[J]. Available at SSRN, 2024.+ [63] Halskov K. Improving Merger Arbitrage Returns with Machine Learning[J]. Available at SSRN, 2024.
  
- [65] Hansen J H, Siggaard M V. Double machine learning: Explaining the post-earnings announcement drift[J]. Journal of Financial and Quantitative Analysis, 2024, 59(3): 1003-1030.+ [64] Hansen J H, Siggaard M V. Double machine learning: Explaining the post-earnings announcement drift[J]. Journal of Financial and Quantitative Analysis, 2024, 59(3): 1003-1030.
  
 **Machine Learning and Portfolio Management** **Machine Learning and Portfolio Management**
  
- [66] 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.+ [65] 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.
  
- [67] Pinelis M, Ruppert D. Machine learning portfolio allocation[J]. The Journal of Finance and Data Science, 2022, 8: 35-54.+ [66] Pinelis M, Ruppert D. Machine learning portfolio allocation[J]. The Journal of Finance and Data Science, 2022, 8: 35-54.
  
- [68] Chen A Y, McCoy J. Missing values handling for machine learning portfolios[J]. Journal of Financial Economics, 2024, 155: 103815.+ [67] Chen A Y, McCoy J. Missing values handling for machine learning portfolios[J]. Journal of Financial Economics, 2024, 155: 103815.
  
 **Other Topics** **Other Topics**
  
- [69] Dai R, Donohue L, Drechsler Q, et al. Dissemination, publication, and impact of finance research: When novelty meets conventionality[J]. Review of Finance, 2023, 27(1): 79-141.+ [68] Dai R, Donohue L, Drechsler Q, et al. Dissemination, publication, and impact of finance research: When novelty meets conventionality[J]. Review of Finance, 2023, 27(1): 79-141.
  
- [70] Hambly B, Xu R, Yang H. Recent advances in reinforcement learning in finance[J]. Mathematical Finance, 2023, 33(3): 437-503.+ [69] Hambly B, Xu R, Yang H. Recent advances in reinforcement learning in finance[J]. Mathematical Finance, 2023, 33(3): 437-503.
  
- [71] Sautner Z, Van Lent L, Vilkov G, et al. Firm‐level climate change exposure[J]. The Journal of Finance, 2023, 78(3): 1449-1498.+ [70] Sautner Z, Van Lent L, Vilkov G, et al. Firm‐level climate change exposure[J]. The Journal of Finance, 2023, 78(3): 1449-1498.
  
- [72] Rossi A G, Utkus S. The diversification and welfare effects of robo-advising[J]. Journal of Financial Economics, 2024, 157: 103869.+ [71] Rossi A G, Utkus S. The diversification and welfare effects of robo-advising[J]. Journal of Financial Economics, 2024, 157: 103869.
  
  
mycourse/machine_learning_and_finance.1726233484.txt.gz · 最后更改: 2024/09/13 21:18 由 kk

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