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.
8. Denev A, Amen S. The Book of Alternative Data: A Guide for Investors, Traders and Risk Managers[M]. John Wiley & Sons, 2020.

1. 考核方式：课程项目
   1. 项目展示：40%
   2. 项目报告：60%
2. 课程项目内容（同时）包括：
   1. 文献评述
   2. （全部或部分）复现经典论文/其他（拓展）结果
   3. 进一步研究计划
   4. 主要参考文献
   5. 附件（数据、代码等）
3. 要求
   1. 内容必须同时与机器学习和金融研究密切相关
   2. 无任何学术不端行为
4. DDL：15-Dec-2024, 20:00：项目报告+PPT
5. 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

Machine Learning, Economics and Finance

[1] Mullainathan S, Spiess J. Machine learning: an applied econometric approach[J]. Journal of Economic Perspectives, 2017, 31(2): 87-106.

[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.

[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

Machine Learning and Asset Pricing

[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.

[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.

[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.

[9] Adämmer P, Schüssler R A. Forecasting the equity premium: mind the news![J]. Review of Finance, 2020, 24(6): 1313-1355

[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

[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

[12] Gu S, Kelly B, Xiu D. Empirical asset pricing via machine learning[J]. The Review of Financial Studies, 2020, 33(5): 2223-2273.

[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.

[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, 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.

[17] Giglio S, Liao Y, Xiu D. Thousands of alpha tests[J]. The Review of Financial Studies, 2021, 34(7): 3456-3496.

[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

[19] Gu S, Kelly B, Xiu D. Autoencoder asset pricing models[J]. Journal of Econometrics, 2021, 222(1): 429-450.

[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.

[21] Chen Y, Kelly B T, Xiu D. Expected returns and large language models[J]. Available at SSRN 4416687, 2022.

[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.

[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] 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.

[46] Goldstein I, Spatt C S, Ye M. Big data in finance[J]. The Review of Financial Studies, 2021, 34(7): 3213-3225.

[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

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

[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.

[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.

[72] Rossi A G, Utkus S. The diversification and welfare effects of robo-advising[J]. Journal of Financial Economics, 2024, 157: 103869.