Instructional goals
This class will provide an advanced analysis of computational methods in finance and applications to investment strategies.
Prerequisites
Basic coding (any language). Basic math, statistics, and probability
Intended learning outcomes
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.
Course Contents
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
Reference Books
Python for Finance by Yves Hilpisch
and material supplied by the instructor (including, papers, lecture notes and slides).
Teaching Methods
In class lectures, assignments, in-class tutorials, investment games
Assessment Method
Problem sets, class discussions, writing a paper, midterm and final exams
Thesis assignment criteria
The criteria established by the Director of the LM in Finance as well as those stated by the Department of Economics and Finance.
Week 1
Introduction and stylized data, Bloomberg tour
Week 2
Review of finance notation, probability and statistics, matrix algebra
Week 3
Asset classes and financial instruments
Week 4
Utility based asset pricing models: theory and evidence
Week 5
Utility based asset pricing models: theory and evidence
Week 6
Utility based asset pricing models: theory and evidence
Week 7
Utility based asset pricing models: theory and evidence
Week 8
Expected Returns in the Time Series and in the Cross-Section
Week 9
Expected Returns in the Time Series and in the Cross-Section
Week 10
Factor models and investment strategies
Week 11
Cryptocurrency Finance
Week 12
Liquidity