Time Series Analysis for Business and Finance
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Instructor: Jeff Hamrick, Ph.D., CFA, FRM
Office: Masonic 211
Office Hours: By appointment.
Cell Phone: 617/943-4619
Office Phone: 415/422-6810
Email Address: email@example.com
Class Location: Presidio Campus
Class Time: 10:00 a.m. - 12:30 p.m., Tuesdays and Thursdays
ON COURSE GOALS. Any student who successfully completes this course should:
ABOUT ME. My name is Jeff Hamrick. I'm a term assistant professor of nance and business
analytics and I am affiliated with both the Master of Science in Business Analytics (MSAN) and
Master of Science in Financial Analysis (MSFA) programs at the University of San Francisco.
ner of Masonic and Turk. My e.mail address is firstname.lastname@example.org. My cell phone number is
617/943-4619 and my office number is 415/422-6810. If you're unable to discuss academic issues
with me at the Presidio campus before or after class, let me know and we may be able to schedule
an appointment (possibly over Google Hangout) at an alternate time.
you are a candidate for the Master of Science in Analytics at the University of San Francisco.
Linear Regression Analysis (MSAN 601) is a prerequisite for this course.
Tuesday, December 11, 2012. We will meet at the Presidio Campus. We will primarily use the fifth
and eighth chapters of the second edition of Introductory Econometrics for Finance by Chris Brooks
(ISBN 978-0521694681) and the first three chapters of the third edition of Analysis of Financial
Time Series by Ruey Tsay (ISBN 978-0470414354). You are responsible for the material in
all readings assigned for this course, regardless of whether or not the material from
those readings is included in my in-class lectures.
computing and graphics. The R language is used by many professional statisticians and is making
deep inroads in industry as well. R is equipped with a wide variety of statistical and graphical
techniques. It supports linear and nonlinear modeling, classical statistical tests, time series anal-
ysis, classication analysis, clustering, and much more. It will be extensively used in the MSAN
program. A set of screencast tutorials related to R will be available on my YouTube channel and
Consequently, you may only miss class under the most dire of circumstances. These circumstances
should be both unusual and documentable. For example, having a bad cold is documentable but
not unusual. On the other hand, being kidnapped by aliens is unusual but is most likely not doc-
umentable. A single absence, for any reason, is acceptable and will not be penalized.
Each absence in excess of one absence will cause your final letter grade in this class
to be lowered by one level (e.g., an A- will become a B+.)
that laptop before the course begins. You will be expected to use R on quizzes and on the final
examination, and sometimes we will use R in class. I would ask you to be respectful of your class-
mates and to refrain from surfing the web, checking out Facebook, tweeting people your various
tweets, etc. during the middle of my lectures.
including regressions or time series analyses for you to run using R) that I will make up myself or
assign from the Brooks or Tsay textbooks. You must work on these problem sets in groups of size
two to four and turn in a single assignment. While I encourage you to work with your colleagues
on the assigned problems as you prepare a common write-up, make sure that you are learning the
material individually rather than passively learning while somebody else does the work. Each day,
your group (which can vary from assignment to assignment) will turn in the entire collection of
problems and I will grade a random subset of them, or all of them. To facilitate efficient grading,
your weekly homework should have the following properties:
work problems in great detail. Instead, feel free to come to my virtual office hours or to schedule
an individual appointment with me. I will not accept late homework assignments under
the first week of class). Each in-class quiz will focus on material that we have recently discussed in
class (generally, the topics from the prior day). The in-class quizzes will be centered on denitions,
concepts, and simple computations, as well as interpretation of pre-generated statistical output.
At the end of the course, I will drop your lowest quiz grade.
in this course on December 11 during the regular course time, with some possibility for extra time
(say, 10:00 a.m. - 2:00 p.m.). The final examination will focus on concepts, i.e., you will not be ex-
pected to engage in tons of routine calculations, but you will be expected to know certain formulas
and relationships and you will be expected to interpret the outputs of various time series analyses.
In addition, you will be expected to use R to assist you with time series analysis.
ment of their own learning. If students put as much effort into actually learning material as they
did worrying about their grades, their performance would be much better. Nevertheless, part of
my job is to assign grades fairly and in a manner that reflects the high academic standards at the
University of San Francisco and in the MSAN program. In this class, we will use the standard
ten-point scale. "Plus" or "minus" grades will be assigned to students with grades close to the
extremes of each ten-point bracket (plus or minus three points from the boundary of each bracket).
Your grade in this course will be computed according to the following weights:
Homework Sets 25%
Final Project 25%
Final Examination 25%
of the whole person -- the University of San Francisco has an obligation to embody and foster the
values of honesty and integrity. The university upholds standards of honesty and integrity from all
members of the academic community, including faculty, students, and staff. All students are ex-
pected to know and to adhere to the university's honor code. You can find the full text of the code
online at http://www.usfca.edu/catalog/policies/honor/. Specifically, while you are required
to work in groups with students on the homework assignments, you should not allow your name
to be placed on a group write-up if it does not reect your own understanding of the material and
if you have not made an honest, equitable contribution to the group effort. Copying answers from
other students or sources during a quiz or examination is a violation of the university's honor code
and will be treated as such. You are also, of course, bound to the terms of the MSAN Code of
Conduct that you signed prior to matriculating in the analytics program. All incidents of cheating
or other academic misconduct will be reported to the director of the MSAN program.
you may have a disability, please contact USF Student Disability Services (SDS) at 415/422-2613
within the first week of class, or immediately upon onset of the disability, to speak with a disability
specialist. If you are determined eligible for reasonable accommodations, please meet with your
disability specialist so they can arrange to have your accommodation letter sent to me, and we will
discuss your needs for this course. For more information, please visit http://www.usfca.edu/sds/
or call 415/422-2613.