Obiettivi formativi
This class will provide an advanced analysis of computational methods in finance and applications to 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) CAPM and Basic portfolio theory and practice
4) Market liquidity and liquidity strategies
5) Investment strategies for equities, bonds and currencies
6) Investing in the cryptocurrency
Testi Di Riferimento
Python for Finance by Yves Hilpisch
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, class discussions, writing a paper, midterm and final exams
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.
Settimana 1
Introduction and stylized data, Bloomberg tour
Settimana 2
Review of finance notation, probability and statistics, matrix algebra
Settimana 3
Asset classes and financial instruments
Settimana 4
Utility based asset pricing models: theory and evidence
Settimana 5
Utility based asset pricing models: theory and evidence
Settimana 6
Utility based asset pricing models: theory and evidence
Settimana 7
Utility based asset pricing models: theory and evidence
Settimana 8
Expected Returns in the Time Series and in the Cross-Section
Settimana 9
Expected Returns in the Time Series and in the Cross-Section
Settimana 10
Factor models and investment strategies
Settimana 11
Cryptocurrency Finance
Settimana 12
Liquidity