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--- mycourse:machine_learning_and_finance [2024/05/23 21:01] – [主要参考文献] kk
+++ mycourse:machine_learning_and_finance [2024/09/30 12:00] (当前版本) – [教学计划] kk
@@ 行 11: / 行 11: @@
 | 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]] |
+| 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 | 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]] |
+| 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 | 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]] |
+| 4 | Literature Studies | wk5-wk8 | [[http://kktim.cn/teaching/mlinfin/MLinFin-LXX-Presentation-schedule-slides.html|Presentation Schedule]] |
-| 5 | SP1: Machine Learning & Asset Pricing & Financial Bigdata | by yourself | [[https://www.nber.org/papers/w31502|NBER Working Paper: Financial Machine Learning]] |
+| 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 | 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]] |
+| 6 | SP1: Machine Learning & Asset Pricing & Financial Bigdata | by yourself | [[https://www.nber.org/papers/w31502|NBER Working Paper: Financial Machine Learning]] |
+| 7 | 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]] |
@@ 行 28: / 行 29: @@
   - 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.
+  - Denev A, Amen S. The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers[M]. John Wiley & Sons, 2020.
 ===== 考核方式 =====
   - 考核方式：课程项目
+    - 项目展示：40%
+    - 项目报告：60%
   - 课程项目内容（**同时**）包括：
-    - （全部或部分）复现经典论文
+    - 文献评述
-    - 研究计划或综述
+    - （全部或部分）复现**经典**论文/其他（拓展）结果
+    - 进一步研究计划
+    - 主要参考文献
+    - 附件（数据、代码等）
   - 要求
-    - 内容必须**同时**与**机器学习**和**金融**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
-  - **DDL：15-Dec-2023, 20:00**
+  - **DDL：15-Dec-2024, 20:00**：项目报告+PPT
-  - 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 =====
@@ 行 71: / 行 76: @@
 **[[: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.
+**Machine Learning, Economics and Finance**
- [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
+ [1] Mullainathan S, Spiess J. Machine learning: an applied econometric approach[J]. Journal of Economic Perspectives, 2017, 31(2): 87-106.
- [5] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225.
+ [2] Athey S. The impact of machine learning on economics[J]. The economics of artificial intelligence: An agenda, 2018: 507-547.
+ [3] Athey S, Imbens G W. Machine learning methods that economists should know about[J]. Annual Review of Economics, 2019, 11: 685-725.
- [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.
+ [4] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225.
+ [5] Kelly B T, Xiu D. Financial machine learning[R]. National Bureau of Economic Research, 2023.
+Machine Learning and Asset Pricing
- [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.
+**Machine Learning and Asset Pricing**
- [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
+ [6] 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.
- [9] Gu S, Kelly B, Xiu D. Empirical asset pricing via machine learning[J]. The Review of Financial Studies, 2020, 33(5): 2223-2273.
+ [7] Aït-Sahalia Y, Xiu D. Principal component analysis of high-frequency data[J]. Journal of the American Statistical Association, 2019, 114(525): 287-303.
- [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
+ [8] 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.
- [11] Gu S, Kelly B, Xiu D. Autoencoder asset pricing models[J]. Journal of Econometrics, 2021, 222(1): 429-450.
+ [9] Adämmer P, Schüssler R A. Forecasting the equity premium: mind the news![J]. Review of Finance, 2020, 24(6): 1313-1355
- [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.
+ [10] 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
- [13] Kozak S, Nagel S, Santosh S. Shrinking the cross-section[J]. Journal of Financial Economics, 2020, 135(2): 271-292.
+ [11] 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
- [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.
+ [12] Gu S, Kelly B, Xiu D. Empirical asset pricing via machine learning[J]. The Review of Financial Studies, 2020, 33(5): 2223-2273.
- [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
+ [13] Karolyi G A, Van Nieuwerburgh S. New methods for the cross-section of returns[J]. The Review of Financial Studies, 2020, 33(5): 1879-1890.
- [16] Chen L, Pelger M, Zhu J. Deep learning in asset pricing[J]. Management Science, 2023.
+ [14] Kozak S, Nagel S, Santosh S. Shrinking the cross-section[J]. Journal of Financial Economics, 2020, 135(2): 271-292.
- [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
+ [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
- [18] Giglio S, Liao Y, Xiu D. Thousands of alpha tests[J]. The Review of Financial Studies, 2021, 34(7): 3456-3496.
+ [16] Bianchi D, Büchner M, Tamoni A. Bond risk premiums with machine learning[J]. The Review of Financial Studies, 2021, 34(2): 1046-1089.
- [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.
+ [17] Giglio S, Liao Y, Xiu D. Thousands of alpha tests[J]. The Review of Financial Studies, 2021, 34(7): 3456-3496.
- [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
+ [18] 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
- [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.
+ [19] Gu S, Kelly B, Xiu D. Autoencoder asset pricing models[J]. Journal of Econometrics, 2021, 222(1): 429-450.
- [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.
+ [20] 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.
- [23] Kelly B T, Xiu D. Financial machine learning[R]. National Bureau of Economic Research, 2023.
+ [21] Chen Y, Kelly B T, Xiu D. Expected returns and large language models[J]. Available at SSRN 4416687, 2022.
- [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.
+ [22] Dong X, Li Y, Rapach D E, et al. Anomalies and the expected market return[J]. The Journal of Finance, 2022, 77(1): 639-681.
- [25] Yu S, Xue H, Ao X, et al. Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning[J]. arXiv preprint arXiv:2306.12964, 2023.
+ [23] Edmans A, Fernandez-Perez A, Garel A, et al. Music sentiment and stock returns around the world[J]. Journal of Financial Economics, 2022, 145(2): 234-254.
+ [24] Leippold M, Wang Q, Zhou W. Machine learning in the Chinese stock market[J]. Journal of Financial Economics, 2022, 145(2): 64-82.
+ [25] Bali T G, Beckmeyer H, Moerke M, et al. Option return predictability with machine learning and big data[J]. The Review of Financial Studies, 2023, 36(9): 3548-3602.
  [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.
+ [27] Brogaard J, Zareei A. Machine learning and the stock market[J]. Journal of Financial and Quantitative Analysis, 2023, 58(4): 1431-1472.
+ [28] Chen L, Pelger M, Zhu J. Deep learning in asset pricing[J]. Management Science, 2023.
+ [29] DeMiguel V, Gil-Bazo J, Nogales F J, et al. Machine learning and fund characteristics help to select mutual funds with positive alpha[J]. Journal of Financial Economics, 2023, 150(3): 103737.
+ [30] Drobetz W, Hollstein F, Otto T, et al. Estimating stock market betas via machine learning[J]. Journal of Financial and Quantitative Analysis, 2023: 1-56.
+ [31] Evgeniou T, Guecioueur A, Prieto R. Uncovering sparsity and heterogeneity in firm-level return predictability using machine learning[J]. Journal of Financial and Quantitative Analysis, 2023, 58(8): 3384-3419.
+ [32] Jiang J, Kelly B, Xiu D. (Re‐) Imag (in) ing price trends[J]. The Journal of Finance, 2023, 78(6): 3193-3249.
+ [33] Kaniel R, Lin Z, Pelger M, et al. Machine-learning the skill of mutual fund managers[J]. Journal of Financial Economics, 2023, 150(1): 94-138.
+ [34] Lopez-Lira A, Tang Y. Can chatgpt forecast stock price movements? return predictability and large language models[J]. arXiv preprint arXiv:2304.07619, 2023.
+ [35] Van Binsbergen J H, Han X, Lopez-Lira A. Man versus machine learning: The term structure of earnings expectations and conditional biases[J]. The Review of financial studies, 2023, 36(6): 2361-2396.
+ [36] Yu S, Xue H, Ao X, et al. Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning[J]. arXiv preprint arXiv:2306.12964, 2023.
+ [37] Aleti S, Bollerslev T. News and Asset Pricing: A High-Frequency Anatomy of the SDF[J]. The Review of Financial Studies, 2024: hhae019.
+ [38] Alexander N, Scherer W. Using machine learning to forecast market direction with efficient frontier coefficients[J]. arXiv preprint arXiv:2404.00825, 2024.
+ [39] Cakici N, Fieberg C, Metko D, et al. Do anomalies really predict market returns? New data and new evidence[J]. Review of Finance, 2024, 28(1): 1-44.
+ [40] Dessaint O, Foucault T, Frésard L. Does alternative data improve financial forecasting? the horizon effect[J]. The Journal of Finance, 2024, 79(3): 2237-2287.
+ [41] Kelly B, Malamud S, Zhou K. The virtue of complexity in return prediction[J]. The Journal of Finance, 2024, 79(1): 459-503.
+ [42] Murray S, Xia Y, Xiao H. Charting by machines[J]. Journal of Financial Economics, 2024, 153: 103791.
+ [43] Shen Z, Xiu D. Can Machines Learn Weak Signals?[J]. University of Chicago, Becker Friedman Institute for Economics Working Paper, 2024 (2024-29).
+ [44] Wolff D, Echterling F. Stock picking with machine learning[J]. Journal of Forecasting, 2024, 43(1): 81-102.
+**Machine Learning and Alternative Data in Finance**
+ [45] Bybee L, Kelly B, Manela A, et al. Business news and business cycles[J]. The Journal of Finance, 2021.
+ <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.
+ [21] Chen Y, Kelly B T, Xiu D. Expected returns and large language models[J]. Available at SSRN 4416687, 2022.
+ [23] Edmans A, Fernandez-Perez A, Garel A, et al. Music sentiment and stock returns around the world[J]. Journal of Financial Economics, 2022, 145(2): 234-254.
+ [48] Obaid K, Pukthuanthong K. A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news[J]. Journal of Financial Economics, 2022, 144(1): 273-297.
+ [25] Bali T G, Beckmeyer H, Moerke M, et al. Option return predictability with machine learning and big data[J]. The Review of Financial Studies, 2023, 36(9): 3548-3602.
+ [32] Jiang J, Kelly B, Xiu D. (Re‐) Imag (in) ing price trends[J]. The Journal of Finance, 2023, 78(6): 3193-3249.
+ [49] Garcia D, Hu X, Rohrer M. The colour of finance words[J]. Journal of Financial Economics, 2023, 147(3): 525-549.
+ [34] Lopez-Lira A, Tang Y. Can chatgpt forecast stock price movements? return predictability and large language models[J]. arXiv preprint arXiv:2304.07619, 2023.
+ [37] Aleti S, Bollerslev T. News and Asset Pricing: A High-Frequency Anatomy of the SDF[J]. The Review of Financial Studies, 2024: hhae019.
+ [50] Cao S, Jiang W, Wang J, et al. From man vs. machine to man+ machine: The art and AI of stock analyses[J]. Journal of Financial Economics, 2024, 160: 103910.
+ [40] Dessaint O, Foucault T, Frésard L. Does alternative data improve financial forecasting? the horizon effect[J]. The Journal of Finance, 2024, 79(3): 2237-2287.
+ [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
+ <del>[52] Murray S, Xia Y, Xiao H. Charting by machines[J]. Journal of Financial Economics, 2024, 153: 103791.</del>
+ [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**
+ [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
+ [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.
+ [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.
+ [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.
+**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.
+ [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.
+ [60] 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.
+ [62] 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.
+ [64] 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.
+**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.
+ [67] 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.
+**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.
+ [70] 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.
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