Some General Information
• This course has an above-average workload. You can assume that there is something due for every class.
Often, the assignments lead the class in the sense that you work on
something first and then we cover it in class. This is a good way to
learn, but not everybody likes it.
• Assignments may be done in groups.
There will be a final exam, and it will be open book, open notes. I am
posting last year’s final so you can see what we’ll be covering in this
• I consider Capital Markets and Investments a co-requisite
for this course, but it helps to have taken it before. The key
background we need from that course is the CAPM and its connections with
regression. If you are taking Capital Markets at the same time as this
course, you will probably be covering the CAPM right around the time
this course starts.
Overview of the Course
covers statistical concepts for investment analysis – measuring risk and
return and the factors that drive them. The course is organized around
three main topics:
• Equity factor models
• Fixed income and credit factors
The first topic will make up about half the course.
will use specific tools (especially regression), but the emphasis will
be on statistical thinking about financial data rather than on
technique. In particular, the course will focus on broadly applicable
features of market data and the statistical concepts that help us
understand them. Each class will pair an idea from investment analysis
with one or more statistical tools. All of the concepts and tools
developed in the course will be closely aligned with industry practice,
and we will frequently draw on both industry and academic research. No
familiarity with statistics beyond the MBA core course is required.
only formal prerequisite is the core class in Managerial Statistics. If
you exempted out of the core statistics class, be sure you are
comfortable interpreting regression output. I will assume familiarity
with financial markets and terminology at the level of Capital Markets
and Investments, so I strongly recommend taking that class in parallel
with this one if you have not taken it before. This course will also
have points of contact with the core courses in Corporate Finance and
Course Work and Grading
There is no textbook for the course. The course will be taught from lecture notes and background readings.
should assume that there is an assignment due for every class. Homework
assignments may be done individually or in groups of up to three
people, and you may work with different teams on different assignments.
If you would like help finding a team, email the TA. (We will discuss
this on the first day of class.)
Assignments will be a mix of data
analysis, thought questions, and reading. If you lose points on a data
analysis problem, you have the option redo your analysis and resubmit
your assignment for full credit.
In calculating your course grade, I will drop your lowest homework score.
will have a final exam, which will be based closely on the homework
assignments and class material. My intention is that if you have kept up
in class and understood the homework assignments, you will find the
final exam straightforward; and if you have not kept up in class or not
been conscientious about the homework, you will find the final exam
Class participation – meaning “present, prepared,
participating” – is important for learning and will help keep the class
interesting for all of us.
Grades will be based on the following weights:
• Assignments – 60%
• Final exam – 30%
• Class participation – 10%
Getting Help Outside of Class
am available. I will hold regular office hours (barring unexpected
constraints), but you should feel free to email me or talk to me after
class to set up a different time. We will also have a TA for the course,
and the TA’s office hours and contact information will be posted on
All data analysis assignments are
designed to be done in Excel, so there is no requirement to use
statistical software. However, if you happen to be familiar with a
statistical package or want to experiment with other software, you are
free to use any tools you wish. Many products offer free trial versions.
This is an overview of the topics in the course. Consult the course page on Canvas for detailed information on assignments.
2. Equity factor models
3. Equity factor models and performance evaluation
4. Risk models
5. Factor reduction through principal components analysis
6. Long-term trends
7. Properties of time series data
8. Volatility I
9. Volatility II
10. Dynamics of interest rates
11. Credit risk and credit scoring I
12. Credit risk and credit scoring II
Jack R. Anderson Professor of Business
Professor Glasserman's research and teaching address risk management, derivative securities, Monte Carlo simulation, statistics and operations. Prior to joining Columbia, Glasserman was with Bell Laboratories; he has also held visiting positions at Princeton University, NYU, and the Federal Reserve Bank of New York. In 2011-2012, he was on leave from Columbia and working at the Office of Financial Research in the U.S...