Instructional goals
This class will provide an advanced analysis of asset pricing theory, financial instruments, and 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) 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).
Reference Books
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).
Teaching Methods
In class lectures, assignments, in-class tutorials, investment games
Assessment Method
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.
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.