Obiettivi formativi
This class will provide an advanced analysis of asset pricing theory, financial instruments, and investment strategies.
Prerequisiti
Basic coding (any language). Basic math, statistics, and probability
Risultati di apprendimento attesi
Students are expected to learn state-of-the-art asset pricing models and understand the relationship between risk-return. The focus of the class is applied and students will use Matlab/Python for applications.
Contenuti Del Corso
This class will cover the following topics:
1) Introduction to financial time-series
2) Utility based asset pricing
3) Basic portfolio theory and practice
4) Equilibrium in capital markets
5) Investment strategies for equities, bonds and currencies
6) Advanced topics in asset pricing (factor models, machine learning, ESG factors, private equity, cryptocurrency).
Testi Di Riferimento
Python for Finance by Yves Hilpisch, Bodie, Kane and Marcus, Investments, Latest Edition.
and material supplied by the instructor (including, papers, lecture notes and slides).
Metodologie Didattiche
In class lectures, assignments, in-class tutorials, investment games
Modalità di verifica dell'apprendimento
Problem sets (30%), writing paper/presentation (bonus point), midterm (30%) and final (40%) exams.
Problem sets are 5 and the average will be computed on best 4. Problem sets are group work. Midterm and final are individual work.
Criteri per l’assegnazione dell’elaborato finale
The criteria established by the Director of the LM in Finance as well as those stated by the Department of Economics and Finance.