B2216 - DECISIONS AND INVESTMENTS

Learning outcomes

At the end of the course, the student can use, derive, and understand the logic of rational decision theory (e.g., Expected Utility, Bayes' rule) and the main insight from Gambling Theory. Students will be able to understand the implications of the law of large numbers applied to repeated investment decisions in non-ergodic systems such as the stock market.

Course contents

The course is divided in two modules

Module 1- Prof. Filippo Massari:

1. Expected Utility theory
2. Probability
3. Bayes rule
4. Entropy
5. Gambling theory
6. Kelly rule

Module 2- Prof. Umberto Cherubini:

1. Mean-Variance PrincipleRisk Measures and Acceptable
2. InvestmentsDynamic Investments: Expected Utility vs Information Theory
3. Artificial Intelligence and Investments
4. Introduction to Reinforcement Learning

Readings/Bibliography

Lecture slides

papers

Teaching methods

Lectures

Homeworks

Assessment methods

The final grade will be based on:

1. a term paper (and a PPT or PDF presentation)
   
2. homework's.

The term paper will be sent 5 days before the exam, which will be individual and consist of

1. 10-15 minute presentation of the term paper (with PPT or PDF)
2. 5-10 minutes of questions on content of the course.
   

The term paper will account for up to 15 points in the final grade.

The term paper (about 10 pages) should consist of

1. an introduction to the problem or topic chosen
2. a review of the literature on the subject
3. a mathematical treatment of the problem
4. an illustrative example with data, either real or simulated

The maximum possible score is 30 cum laude.

The grades are described as follows

< 18 failed

18-23 sufficient

24-27 good

28-30 very good

30 cum laude Excellent

Teaching tools

Slides

Office hours

See the website of Filippo Massari

See the website of Umberto Cherubini