Link to the University of Bologna webpage for the course
Thesis
Starting from the research carried out by professors Calvano, Calzolari, Denicolò, Pastorello, we know that algorithmic pricing softwares powered by Reinforcement Learning can autonomously learn to collude. The purpose of thesis is to address the following question:
When and how is it possible to detect the beginning of tacit collusive behaviour between reinforcement algorithmic powered agents?
As you can read in the section dedicated to this project in my website, this is a a binary classification on a time series and the ML tool used to make forecasts is a Random Forest classifier on some engineered moving averages.
Classes
First year
- Microeconomics 8 ECTS
- Macroeconomis 8 ECTS
- Foundations of law:
- Private law 6 ECTS
- Public law 9 ECTS
- Economic history 8 ECTS
- Calculus and linear algebra 12 ECTS
- Accounting and financial statements 8 ECTS
Second year
- Commercial law 8 ECTS
- Financial mathematics 8 ECTS
- Ethics and markets 4 ECTS
- Economics of financial intermediation 8 ECTS
- Statistics 11 ECTS
- Financial economics 8 ECTS
- Industrial organization 6 ECTS
- Public finance and public policy 6 ECTS
- Internship 16 ECTS
Third year
- Economic analysis 6 ECTS
- Corporate finance 8 ECTS
- Data science for economics and finance:
- Introduction to statistical learning 4 ECTS
- Machine learning 4 ECTS
- Econometrics 8 ECTS
- International finance 8 ECTS
- Globalization: trade, migrations and multinationals 8 ECTS
- Market analysis and reporting 5 ECTS
- Final exam 3 ECTS