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Machine Learning
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An Incomplete Reading List for Advanced Studies†

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  1. Auliy, M., Kräussl, R., Manso, G., & Spaenjers, C. (2022). Biased Auctioneers. Journal of Finance (Forthcoming).
  2. Avramov, D., Cheng, S., & Metzker, L. (2020). Machine Learning versus Economic Restrictions: Evidence from Stock Return Predictability. Management Science (Accepted)
  3. Azimi, M., & Agrawal, A. (2021). Is Positive Sentiment in Corporate Annual Reports Informative? Evidence from Deep Learning. The Review of Asset Pricing Studies.
  4. Bali, T. G., Goyal, A., Huang, D., Jiang, F., & Wen, Q. (2020). The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning. SSRN Electronic Journal.
  5. Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58, 199-235.
  6. Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2020). Using machine learning to detect misstatements. Review of Accounting Studies.
  7. Bianchi, D., Büchner, M., & Tamoni, A. (2020). Bond Risk Premiums with Machine Learning. The Review of Financial Studies.
  8. Bianchi, D., & McAlinn, K. (2020). Divide and Conquer: Financial Ratios and Industry Returns Predictability. Available at SSRN: https://ssrn.com/abstract=3136368.
  9. Bianchi, F., Ludvigson, S. C., & Ma, S. (2020). Belief Distortions and Macroeconomic Fluctuations. SSRN Electronic Journal.
  10. Borisenko, D. (2019). Dissecting Momentum: We Need to Go Deeper. SSRN Electronic Journal.
  11. Brown, N. C., Crowley, R. M., & Elliot, W. B. (2019). What are You Saying? Using topic to Detect Financial Misreporting. Journal of Accounting Research.
  12. Bryzgalova, S., Pelger, M., & Zhu, J. (2020). Forest Through the Trees: Building Cross-Sections of Stock Returns. Available at SSRN: https://ssrn.com/abstract=3493458
  13. Bybee, Leland, et al. (2021) Business News and Business Cycles. Available at SSRN: https://ssrn.com/abstract=3446225.
  14. ---. (2022) Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text. Available at SSRN: https://ssrn.com/abstract=3895277.
  15. Cao, K., & You, H. (2020). Fundamental Analysis Via Machine Learning. SSRN Electronic Journal.
  16. Cao, S. S., Jiang, W., Wang, J. L., & Yang, B. (2021). From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses. SSRN Electronic Journal.
  17. Cao, S. S., Jiang, W., Yang, B., & Zhang, A. (2020). How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI. SSRN Electronic Journal.
  18. Chaudhry, A., & Oh, S. (2020). High-Frequency Expectations from Asset Prices: A Machine Learning Approach. SSRN Electronic Journal.
  19. Chen, H., Didisheim, A., & Scheidegger, S. (2021). Deep Structural Estimation: With an Application to Option Pricing. SSRN Electronic Journal.
  20. Chen, L., Pelger, M., & Zhu, J. (2019). Deep Learning in Asset Pricing. SSRN Electronic Journal.
  21. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21, C1-C68.
  22. Chhaochharia, V., Kumar, A., Murali, S., & Rantala, V. (2021). Aggregating Artificially Intelligent Earnings Forecasts. SSRN Electronic Journal.
  23. Chinco, A., Clark‐Joseph, A. D., & Ye, M. (2018). Sparse Signals in the Cross‐Section of Returns. The Journal of Finance, 74, 449-492.
  24. Cong, L., Tang, K., Wang, J., & Zhang, Y. (2020). Deep Sequence Modeling: Development and Applications in Asset Pricing. SSRN Electronic Journal.
  25. Dautel, A. J., Härdle, W. K., Lessmann, S., & Seow, H.-V. (2020). Forex exchange rate forecasting using deep recurrent neural networks. Digital Finance.
  26. Davis, C. (2018). Predictable Downturns. SSRN Electronic Journal.
  27. DeMiguel, V., Gil-Bazo, J., Nogales, F. J., & A. P. Santos, A. (2021). Can Machine Learning Help to Select Portfolios of Mutual Funds? SSRN Electronic Journal.
  28. Dessaint, O., Foucault, T., & Frésard, L. (2020). Does Big Data Improve Financial Forecasting? The Horizon Effect. SSRN Electronic Journal.
  29. Easley, D., López de Prado, M., O’Hara, M., & Zhang, Z. (2020). Microstructure in the Machine Age. The Review of Financial Studies.
  30. Edmans, A., Fernandez-Perez, A., Garel, A., & Indriawan, I. (2021). Music sentiment and stock returns around the world. Journal of Financial Economics.
  31. Engle, R. F., Giglio, S., Kelly, B., Lee, H., & Stroebel, J. (2020). The Review of Financial Studies. The Review Of Financial Studies, 33, 1184-1216.
  32. Erel, I., Stern, L. H., Tan, C., & Weisbach, M. S. (2021). Selecting Directors Using Machine Learning. The Review of Financial Studies, 34, 3226-3264.
  33. Evgeniou, T., Guecioueur, A., & Prieto, R. (2020). Modeling Heterogeneity in Firm-level Return Predictability with Machine Learning. SSRN Electronic Journal.
  34. Fan, J., Xue, L., & Zhou, Y. (2021). How Much Can Machines Learn Finance From Chinese Text Data? SSRN Electronic Journal.
  35. Feng, G., Polson, N., & Xu, J. (2021). Deep Learning in Characteristics-Sorted Factor Models. Available at SSRN: https://ssrn.com/abstract=3243683
  36. Fuster, A., Goldsmith‐Pinkham, P., Ramadorai, T., & Walther, A. (2021). Predictably Unequal? The Effects of Machine Learning on Credit Markets. The Journal of Finance, 77, 5-47.
  37. Garcia, D., Hu, X., & Rohrer, M. (2020). The Colour of Finance Words. Available at SSRN: https://ssrn.com/abstract=3630898
  38. Garzoli, M., Plazzi, A., & Valkanov, R. (2021). Backcasting, Nowcasting, and Forecasting Residential Repeat-Sales Returns: Big Data meets Mixed Frequency. SSRN Electronic Journal.
  39. Geertsema, P. G., & Lu, H. (2020). Relative Valuation with Machine Learning. SSRN Electronic Journal.
  40. Gentzkow, Matthew, et al. Text as Data. (2017) Available at SSRN: https://ssrn.com/abstract=2934001.
  41. Goldstein, I., Spatt, C. S., & Ye, M. (2021). Big Data in Finance. The Review of Financial Studies.
  42. Gopalakrishna, G. (2020). Asset Pricing with Realistic Crises Dynamics. SSRN Electronic Journal.
  43. Goyenko, R., & Zhang, C. (2020). The Joint Cross Section of Option and Stock Returns Predictability with Big Data and Machine Learning. SSRN Electronic Journal.
  44. Grammig, J., Hanenberg, C., Schlag, C., & Sönksen, J. (2020). Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia. SSRN Electronic Journal.
  45. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies.
  46. Hafner, C. M., & Wang, L. (2022). Dynamic portfolio selection with sector-specific regularization. Econometrics and Statistics.
  47. Hassan, T. A., Hollander, S., van Lent, L., & Tahoun, A. (2019). Firm-Level Political Risk: Measurement and Effects. The Quarterly Journal of Economics, 134, 2135-2202.
  48. Hu, A., & Ma, S. (2020). Human Interactions and Financial Investment: A Video-Based Approach. SSRN Electronic Journal.
  49. Israel, R., Kelly, B. T., & Moskowitz, T. J. (2020). Can Machines Learn Finance? SSRN Electronic Journal.
  50. Jiang, H., Li, S. Z., & Yuan, P. (2020). Predicting High-Frequency Industry Returns: Machine Learners Meet News Watchers. SSRN Electronic Journal.
  51. Kan, R., Wang, X., & Zheng, X. (2019). In-Sample and Out-of-Sample Sharpe Ratios of Multi-Factor Asset Pricing Models. SSRN Electronic Journal.
  52. Karolyi, G. A., & Van Nieuwerburgh, S. (2020). New Methods for the Cross-Section of Returns. The Review of Financial Studies, 33, 1879-1890.
  53. Katsafados, A. G., Androutsopoulos, I., Chalkidis, I., Fergadiotis, E., Leledakis, G. N., & Pyrgiotakis, E. G. (2020). Textual Information and IPO Underpricing: A Machine Learning Approach. SSRN Electronic Journal.
  54. Ke, Zheng Tracy, et al. Predicting Returns with Text Data. (2020) Available at SSRN: https://ssrn.com/abstract=3389884.
  55. Kelly, liyan T., et al. (2021) The Virtue of Complexity in Machine Learning Portfolios. Swiss Finance Institute Research Paper No. 21-90, Available at SSRN: https://ssrn.com/abstract=3984925.
  56. Kolm, P. N., Turiel, J., & Westray, N. (2021). Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book. SSRN Electronic Journal.
  57. Kwan, A., Philip, R., & Shkilko, A. (2020). The Conduits of Price Discovery: A Machine Learning Approach. SSRN Electronic Journal.
  58. Lee, G. M., Naughton, J. P., Zheng, X., & Zhou, D. (2020). Predicting Litigation Risk via Machine Learning. SSRN Electronic Journal.
  59. Li, K., Mai, F., Shen, R., & Yan, X. (2020). Measuring Corporate Culture Using Machine Learning. The Review of Financial Studies.
  60. Liu, Y., Zhou, G., & Zhu, Y. (2020). Maximizing the Sharpe Ratio: A Genetic Programming Approach. SSRN Electronic Journal.
  61. Loughran, T., & McDonald, B. (2020). Measuring Firm Complexity. SSRN Electronic Journal.
  62. Martin, I. W. R., & Nagel, S. (2021). Market efficiency in the age of big data. Journal of Financial Economics.
  63. Moritz, B., & Zimmermann, T. (2016). Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns. SSRN Electronic Journal.
  64. Paraschiv, F., Schmid, M., & Wahlstrøm, R. R. (2021). Bankruptcy Prediction of Privately Held SMEs Using Feature Selection Methods. SSRN Electronic Journal.
  65. Rapach, D., & Zhou, G. (2018). Sparse Macro Factors. SSRN Electronic Journal.
  66. Rapach, D., & Zhou, G. (2019). Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods. SSRN Electronic Journal.
  67. Rapach, D., & Zhou, G. (2021). Asset Pricing: Time-Series Predictability. SSRN Electronic Journal.
  68. Richmond, R. J. (2019). Trade Network Centrality and Currency Risk Premia. The Journal of Finance, 74, 1315-1361.
  69. Rossi, A. G. (2018). Predicting Stock Market Returns with Machine Learning. Manuscript.
  70. Song, S. (2021). The Informational Value of Segment Data Disaggregated by Underlying Industry: Evidence from the Textual Features of Business Descriptions. The Accounting Review.
  71. Stoffi, F. J. B., De Beckker, K., Maldonado, J. E., & De Witte, K. (2021). Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy. ArXiv:2102.04382 [Econ].
  72. Tobek, O., & Hronec, M. (2021). Does it pay to follow anomalies research? Machine learning approach with international evidence. Journal of Financial Markets, 56, 100588.
  73. van Binsbergen, J. H., Han, X., & Lopez-Lira, A. (2020). Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. SSRN Electronic Journal.
  74. Wu, W., Chen, J., Yang, Z. (Ben), & Tindall, M. L. (2021). A Cross-Sectional Machine Learning Approach for Hedge Fund Return Prediction and Selection. Management Science, 67, 4577-4601.
  75. Xiong, R., & Pelger, M. (2019). Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference. SSRN Electronic Journal.
  76. Zhang, Z., Zohren, S., & Roberts, S. (2019). DeepLOB: Deep Convolutional Neural Networks for Limit Order Books. IEEE Transactions on Signal Processing, 1-1.
  77. Zhu, C. (2019). Big Data as a Governance Mechanism. The Review of Financial Studies, 32, 2021-2061.
† The list is by no means exhaustive, but it is definitely a good starting point for those who are interested in the research of machine learning in finance.