FA631 Investment, Portfolio Construction, and Trading Analytics
Course Catalog Description
The significant amount of information available in any field requires a systematic and analytical approach to select the most important information and anticipate major events. Machine learning algorithms facilitate this process understanding, modeling and forecasting the behavior of major social or economic systems and their variables.
This course explores how to apply fundamental machine learning models to predict financial time series and solve financial problems. Some of the financial applications explored are algorithmic trading, model calibration, portfolio optimization, and risk management.
BIA656 Advanced machine learning and data analytics or FE590 Statistical learning in finance or FE690 Machine learning in finance or MIS 637 Data analytics and machine learning or CS559 Machine Learning or instructor’s authorization.
Students who successfully complete this course will be able to
- Apply statistical models and analytical methods to the finance domain.
- Recognize the value and limits of statistical learning algorithms to solve finance problems.
- Develop analytical models for financial forecasting, portfolio optimization and trading.
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer-Verlag, 2nd. Ed., New York, 2009
- Marco López de Prado, Advances in Financial Machine Learning, Wiley, 2018 (LP)
- Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press. 2008 (downloadable at http://nlp.stanford.edu/IR-book)
- Selected papers:
- G. Creamer and Y. Freund (2007). “A Boosting Approach for Automated Trading.” Journal of Trading 2 (3): 84-96.
- G. Creamer (2015). “Can a Corporate Network and News Sentiment Improve Portfolio Optimization Using the Black Litterman Model?” Quantitative Finance 15 (8): 1405-1416.
- G. Creamer (2012). “Model Calibration and Automated Trading Agent for Euro Futures.” Quantitative Finance 12 (4): 531-545.
- S. Gu, B. Kelly, D. Xiu, Empirical Asset Pricing via MachineLearning, The Review of Financial Studies 33 (2020): 2223–2273.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- Hal Daumé III, A Course in Machine Learning link
HW 25% Participation 5% Research paper 35% Final exam 35%
||Review of Python and data science concepts
||Classification models for risk management: decision trees, and support vector machine
||ESL 9.2 and 12
||1: Python review
||Ensemble methods for algorithmic trading: bagging, boosting, and random forests
||ESL 7.1-7.3, 10.1-10.9, 15, 16
||Technical analysis and algorithmic trading
||2: Classification: risk scores
||Clustering analysis, risk diversification and factor models
||Natural language processing I: Sentiment analysis and financial trends
||Natural language processing II: Extracting information and investment signals from financial reports
||3: Sentiment analysis
||Empirical asset pricing and machine learning I
||Empirical asset pricing and machine learning II
||4: Text analysis and financial reporting
||Financial data structures and cross-validation
||LP 2-4, 7, ESL 7.10
||Feature importance & model calibration
||LP 8-9, *4
Mean variance portfolio optimization and Black Litterman model
||ESL 8.6, 17.1-17.3
||5: Portfolio optimization