*** ECONOMETRICS FOR ASSET AND RISK MANAGERS (for sub-track ARM) and EMPIRICAL CORPORATE FINANCE (for sub-track Corporate Finance)” ***
Basic statistics and probability
Knowledge of financial markets and products
Econometrics (student must be very familiar with linear regression))
Knowledge of the R software, including at a minimum: reading data from a file; running different regression models; for loops and if-clauses.
At the end of the course, the student should be able to:
1. Describe how machine learning is used in general and in the field of Finance.
2. Compare and explain methods of machine learning such as supervised and unsupervised learning, tree-based methods and dimension reduction methods.
3. Implement several machine learning methods using the software R.
*Additional Assurance of Learning (“AOL”) objectives:
LO5B. Construct expert knowledge from cutting-edge information
LO6A. Thoroughly examine a complex business situation
This is a technical course for students looking to deepen their knowledge in quantitative methods applied to finance. The course aims to cover the following:
– what is machine learning: understanding data and making predictions
– supervised vs unsupervised learning
– clasification versus regression problems
– assessing model accuracy
– trade-off between model interpretability and forecasting accuracy
– resampling methods: cross-validation and bootstrap
– regularization in linear models: LASSO, Ridge Regression
– tree-based methods, boosting, bagging and random forests
– dimension reductions techniques
