Machine learning for Portfolio Management and Trading

ECTS:
2

Course Hours:
12

Tutorials Hours:
0

Language:
English

Examination Modality:
mém.

This course introduces notebook-based data-science applications for quantitative portfolio management and trading. More precisely, it shows:

What kind of data is used by hedges and investment bank trading desks
How traditional data (e.g. prices and volume), but also alternative data (e.g. text) is used to predict the returns of stocks and other assets.
How machine-learning is used to extract features and train predictive models.

A strong emphasis is on running statistical tests in practice with:

Replicable python notebooks
Public datasets
Open-source code
Best practices for data exploration, hyperparameter tuning, building pipelines, etc.

References:

Moskowitz, T. J., & Grinblatt, M. (1999). Do industries explain momentum?. The Journal of finance, 54(4), 1249-1290.
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10?Ks. The Journal of finance, 66(1), 35-65.
Jegadeesh, N., & Wu, D. (2013). Word power: A new approach for content analysis. Journal of financial economics, 110(3), 712-729.
Savor, P., & Wilson, M. (2014). Asset pricing: A tale of two days. Journal of Financial Economics, 113(2), 171-201.
Ledoit, O., & Wolf, M. (2004). Honey, I shrunk the sample covariance matrix. The Journal of Portfolio Management, 30(4), 110-119.
Lo, A. W. (2002). The statistics of Sharpe ratios. Financial analysts journal, 58(4), 36-52.
Frazzini, A., Kabiller, D., & Pedersen, L. H. (2018). Buffett’s alpha. Financial Analysts Journal, 74(4), 35-55.
Lo, A. W., & MacKinlay, A. C. (1990). When are contrarian profits due to stock market overreaction?. The review of financial studies, 3(2), 175-205.

Optional background reading:

Isichenko, M. (2021). Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage. Wiley.