MA Seminar – Machine Learning in Finance
General Literature:
Babii, A. and Ghysels, E. and Striaukas, J., Econometrics of Machine Learning Methods in Economic Forecasting (July 31, 2023). Kenan Institute of Private Enterprise Research Paper No. 4547321. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4547321
Chen, M. A. and Wu, Q. and Yang, B., How Valuable Is FinTech Innovation? (September 30, 2018). Review of Financial Studies, Forthcoming. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3106892
Gu, S. and Kelly, B. T. and Xiu, D., Empirical Asset Pricing via Machine Learning (July 21, 2018). Swiss Finance Institute Research Paper No. 18-71. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3281018
Kelly, B. and Xiu, D. Financial Machine Learning (July 1, 2023). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4501707
López de Prado, M., Advances in Financial Machine Learning (January 18, 2018). Advances in Financial Machine Learning, Wiley, 1st Edition (2018); ISBN: 978-1-119-48208-6. Available at SSRN: https://ssrn.com/abstract=3104847
Types of Machine Learning
1 Supervised, Unsupervised and Reinforcement Learning
Benhamou, E. and Saltiel, D. and Ungari, S. and Mukhopadhyay, A., Bridging the Gap Between Markowitz Planning and Deep Reinforcement Learning (September 30, 2020). Universite Paris-Dauphine Research Paper No. 3702112, Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3702112
Benhamou, E. and Saltiel, D. and Tabachnik, S. and Bourdeix, C. and Chareyron, F. and Guez, B., Adaptive Supervised Learning for Volatility Targeting Models (September 15, 2021). Universite Paris-Dauphine Research Paper No. 3924255, Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3924255
Bloch, D. A., Machine Learning: Models and Algorithms (February 7, 2019). Machine Learning: Models And Algorithms, Quantitative Analytics, 2018. Available at SSRN: https://ssrn.com/abstract=3307566
Frankel, R. M. and Jennings, J. N. and Lee, J. A., Using Natural Language Processing to Assess Text Usefulness to Readers: The Case of Conference Calls and Earnings Prediction (January 17, 2017). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3095754
Jethwani, G. and Sachdeva, A. and Goswami, M., An Empirical Analysis of ML Algorithms (February 23, 2019). Available at SSRN: https://ssrn.com/abstract=3358099
1 Support Vector Machine (SVM), Kernel Trick
Huerta, R. and Elkan, C. and Corbacho, F., Nonlinear Support Vector Machines Can Systematically Identify Stocks with High and Low Future Returns. Algorithmic Finance (2013), 2:1, 45-58. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1930709
Kozak, S., Kernel Trick for the Cross-Section (April 6, 2019). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3307895
Leung, T. and Zhao, T., Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning (May 11, 2020) Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3595914
Li, Z. and Tian, Y. and Li, K. and Zhou, F. and Yang, W., Reject Inference in Credit Scoring Using Semi-Supervised Support Vector Machines (March 1, 2016). Expert Systems with Applications, Volume 74, May 2017. Available at SSRN: https://ssrn.com/abstract=2740856
Verma, N. and Mohapatra, B., Stock Market Prediction Using Machine Learning (May 21, 2020). ICCIP 2020, Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3645875
1 Artificial Neural Networks (ANN)
Ferrario, A. and Noll, A. and Wuthrich, M. V., Insights from Inside Neural Networks (November 14, 2018). Available at SSRN: https://ssrn.com/abstract=3226852
Hutchinson, J. M. and Lo, A. W. and Poggio, T., A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks (April 1994). NBER Working Paper No. w4718. Available at SSRN: https://ssrn.com/abstract=236673
Kolm, P. and Turiel, J. and Westray, N., Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book (August 5, 2021). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3900141
Nunes, M. and Gerding, E. and McGroarty, F. and Niranjan, M., Artificial Neural Networks in Fixed Income Markets for Yield Curve Forecasting (March 20, 2018). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3144622
Zhou, B., Deep Learning and the Cross-Section of Stock Returns: Neural Networks Combining Price and Fundamental Information (March 3, 2019). Available at SSRN: https://ssrn.com/abstract=3179281
Applications of Supervised Learning
2 Loans and Insurance Underwriting
Fu, R. and Huang, Y. and Singh, P. V., Crowd, Lending, Machine, and Bias (June 30, 2018). Available at SSRN: https://ssrn.com/abstract=3206027
Maggio, M. and Ratnadiwakara, D., Invisible Primes: Fintech Lending with Alternative Data (May 28, 2022). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3937438
Peters, G., Statistical Machine Learning and Data Analytic Methods for Risk and Insurance (December 11, 2017). Available at SSRN: https://ssrn.com/abstract=3050592
Viswanatha, V. and Ramachandra, A. and Vishwas, K. and Adithya, G., Prediction of Loan Approval in Banks Using Machine Learning Approach (August 4, 2023). International Journal of Engineering and Management Research, Volume-13, Issue-4, Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4532468
Wuthrich, M. V. and Buser, C., Data Analytics for Non-Life Insurance Pricing (February 5, 2019). Swiss Finance Institute Research Paper No. 16-68. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308
2 Fraud Detection
Ahmadi, S., Open AI and its Impact on Fraud Detection in Financial Industry (December 2023). Journal of Knowledge Learning and Science Technology 2959-6386, 2(3), 263-281. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4684331
Carcillo, F. and Le Borgne, Y. and Caelen, O. and Kessaci, Y. and Oble, F. and Bontempi, G., Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection (May 2019). Information Sciences 557 (2021) 317-331 https://www.sciencedirect.com/science/article/pii/S0020025519304451
Hilal, W. and Gadsden, S. and Yawney, J., Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances (May 2022). Expert Systems with Applications Volume 193, 1 May 2022, 116429 https://www.sciencedirect.com/science/article/pii/S0957417421017164
Palacio, S. M., Detecting Outliers with Semi-Supervised Machine Learning: A Fraud Prediction Application (April 19, 2018). XREAP WP 2018-02. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3165318
Panigrahi, S. and Saitejaswi, K. and Devarapalli, D., TEJU: Fraud Detection and Improving Classification Performance for Bankruptcy Datasets Using Machine Learning Techniques (February 24, 2019). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3356511
2 Natural Language Processing (NLP)
Bommarito, M. J. and Katz, D. M. and Detterman, E., LexNLP: Natural Language Processing and Information Extraction For Legal and Regulatory Texts (June 6, 2018). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3192101
Chew, M. and Puri, S. and Sood, A. and Wearne, A., Using Natural Language Processing Techniques for Stock Return Predictions (March 7, 2017). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2940564
Cong, L. and Liang, T. and Zhang, X., Textual Factors: A Scalable, Interpretable, and Data-driven Approach to Analyzing Unstructured Information (September 1, 2019). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3307057
Glasserman, P. and Mamaysky, H., Does Unusual News Forecast Market Stress? (March 2018). Columbia Business School Research Paper No. 15-70. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2632699
Huang, A. and Wang H., and Yang, Y., FinBERT – A Large Language Model for Extracting Information from Financial Text (July 28, 2020). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3910214
Applications of Reinforcement Learning
3 Portfolio Optimization
Cong, L. and Tang, K. and Wang, J. and Zhang, Y., AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI (August 1, 2021). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3554486
Gu, S. and Kelly, B. T. and Xiu, D., Empirical Asset Pricing via Machine Learning (June 11, 2018). Chicago Booth Research Paper No. 18-04; 31st Australasian Finance and Banking Conference 2018. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3159577
Heaton, J. B. and Polson, N. and Witte, J., Deep Learning for Finance: Deep Portfolios (September 5, 2016). Applied Stochastic Models in Business and Industry 33 (1), 3-12. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2838013
Messmer, M., Deep Learning and the Cross-Section of Expected Returns (December 2, 2017). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3081555
Pederson, L. and Babu, A. and Levine A., Enhanced Portfolio Optimization (January 2, 2020). Financial Analysts Journal, 77:2, 124-151. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3530390
4 Risk Management
Aziz, S. and Dowling, M., AI and Machine Learning for Risk Management (2019). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3201337
Brummelhuis, R. and Luo, Z., CDS Rate Construction Methods by Machine Learning Techniques (May 12, 2017). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2967184
Kakushadze, Z. and Yu, W., Machine Learning Risk Models (January 1, 2019). Journal of Risk & Control 6(1) (2019) 37-64. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3308964
Lopez Lira, A., Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns (April 2019). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3313663
Peters, G., Statistical Machine Learning and Data Analytic Methods for Risk and Insurance (December 11, 2017). Available at SSRN: https://ssrn.com/abstract=3050592
5 Trading (high frequency, algorithmic trading, bagging, boosting, meta labelling)
Cartea, A. and Jaimungal, S. and Sanchez-Betancourt, L., Deep Reinforcement Learning for Algorithmic Trading (March 25, 2021). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3812473
Dixon, M. F., A High Frequency Trade Execution Model for Supervised Learning (December 5th, 2017). Forthcoming in High Frequency. Available at SSRN: https://ssrn.com/abstract=2868473
Marti, G., Frome Data to Trade: A Machine Learning Approach to Quantitative Trading (December 31, 2022). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4315362
Rechenthin, M. and Street, W. N. and Srinivasan, P., Stock Chatter: Using Stock Sentiment to Predict Price Direction (2013). Algorithmic Finance 2013, 2:3-4, 169-196. Available at SSRN: https://ssrn.com/abstract=2380419
Ritter, G., Machine Learning for Trading (August 8, 2017). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3015609
Pitfalls
6 The Overfitting Problem and Solutions
Aparicio, D. and López de Prado, M., How Hard Is It to Pick the Right Model? MCS and Backtest Overfitting (December 2017). Available at SSRN: https://ssrn.com/abstract=3044740
Arnott, R. D. and Harvey, C. R. and Markowitz, H., A Backtesting Protocol in the Era of Machine Learning (November 21, 2018). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3275654
Bailey, D. H. and López de Prado, M., The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality (July 31, 2014). Journal of Portfolio Management, 40 (5), pp. 94-107. 2014 (40th Anniversary Special Issue). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2460551
López de Prado, M., A Data Science Solution to the Multiple-Testing Crisis in Financial Research (May 11, 2018). Available at SSRN: https://ssrn.com/abstract=3177057
Wiecki, T. and Campbell, A. and Lent, J. and Stauth, J., All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms (March 9, 2016). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2745220
6 Other Pitfalls: Low Signal to Noise Ratio, Weighting of non-IID Data, Structural Breaks
Heaton, J.B., Quantitative Investing and the Limits of (Deep) Learning from Financial Data (March 2, 2018). 47 Journal of Financial Transformation, 117-122 (2018). Available at SSRN: https://ssrn.com/abstract=3133110
Israel, R. and Kelly, B. and Moskowitz, T., Can Machines ‘Learn’ Finance? (January 10, 2020) Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3624052
Leung, E. and Lohre, H. and Mischlich, D. and Shea, Y. and Stroh, M., The Promises and Pitfalls of Machine Learning for Predicting Stock Returns (March 31, 2021). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3546725
López de Prado, M., The 10 Reasons Most Machine Learning Funds Fail (January 27, 2018). Journal of Portfolio Management, Forthcoming. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3104816
Pepelyshev, A., and Polunchenko, A., Real-time Financial Surveillance via Quickest Change-point Detection Methods (2015) Statistics and Its Interface (2015) 1-14. Available at https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=14&ved=2ahUKEwi9tvSTzrHiAhUFLewKHec7DpM4ChAWMAN6BAgEEAI&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1509.01570&usg=AOvVaw2kKVHKEVZjqgDqaaycy3NN