The School of Business
FIN 419-OL1 Finance Analytics and Modeling
Instructor: Dr. Jing Zhao
Online Course
Office Hours: By appointment (via Zoom) or emails
Course Website: https://canvas.pdx.edu/courses/
Email: jizhao@pdx.edu
Important Message about Recording Our Zoom Class Meetings
We will use technology for virtual meetings and recordings in this course. Our
use of such technology is governed by FERPA, the Acceptable Use Policy and
PSU’s Student Code of Conduct. A record of all meetings and recordings is kept
and stored by PSU, in accordance with the Acceptable Use Policy and FERPA.
Your instructor will not share recordings of your class activities outside of course
participants, which include your fellow students, TAs/GAs/Mentors, and any
guest faculty or community based learning partners that we may engage with.
You may not share recordings or the lecture videos provided by the instructor
outside of this course. Doing so may result in disciplinary action.
COURSE DESCRIPTION
This course will teach students how to apply analytical tools to analyze big data in
financial, accounting and other business issues faced by financial analysts, corporate
managers, fund managers, and investors, etc. This is an applied course where we combine
both lectures (i.e., based upon analytical and statistical topics) and cases/projects (i.e.,
applications of analytical tools to real world problems and data). We will also learn
software programming in order to implement the analytical and statistical methods learnt
in class. At the end of day, students are expected to (1) master the concepts of various
analytical and statistical methods; and know how they can be used to solve specific
financial and business issues; and (2) be able to carry out any empirical project in the
finance/accounting/business area including collecting relevant data, selecting the correct
analytical models, conducting econometric analysis, reporting and interpreting the results,
making inferences, and drawing conclusions. Using real financial & business data and
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problems, students have opportunities, for instance, to employ various market and
macroeconomic information to predict future stock returns; examine the effects of antitakeover provisions on corporate innovation; analyze the influence of corporate social
responsibility (CSR) ratings of a company and its market value; investigate the impacts
of executive compensation and board governance on firm performance.
The analytical/modeling topics included are: simple and multiple regression models,
hypothesis testing and inferences, panel data regressions and other advanced topics such
as endogeneity and sample selection.
PRE-REQUISITE
BA 303
COURSE OBJECTIVES
This course is designed so that students are enabled to:
• Explore and appreciate the critical role of financial analytics and statistics in
managerial and investors’ problem-solving and strategic decision-making.
• Master the concepts of various analytical and statistical methods; and know how
they can be applied to solve specific financial and business issues using real world
data.
• Learn to conduct data analytics using statistical software
• Learn to carry out any empirical project in the finance and business area including
collecting relevant, real-world data, selecting correct analytical models, and
conducting appropriate financial analyses.
• Learn how to report and interpret analytical results, and make sound decisions.
LEARNING OUTCOMES
At the end of the course, student should be able to:
• Analyze business issues, solve real world problems, and make informed, strategic
financial/business decisions (Problem Solving & Critical Thinking).
• Present, interpret, and communicate important information to interested audience
(Communication).
• Use statistical software to compute, analyze, graph, and present data and
information (Technical Knowledge).
• Apply concepts and theories from various areas in business to real world data and
problems (Integrative Learning)
• Explore various social, environmental and governance related policies and their
impacts on firm value or performance (Sustainability)
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COURSE MATERIALS
Text Book: Title: Introductory Econometrics A Modern Approach
Edition: 6th ed. (other versions are fine); Author: Jeffrey Wooldridge
(ISBN: 978-1-305-27010-7)
Articles:
Additional readings including articles and academic papers will be
provided by the instructor
SAS Program: Available at the SB computer labs and for free installation on personal
laptops at the University OIT help desk. Students can download and install
the free University version of SAS through the SAS website:
https://www.sas.com/en_us/software/university-edition.html
Alternatively students may remotely access SAS software through PSU’s
Virtual Lab: vlab.pdx.edu
WRDS Class Account: Wharton business school of UPenn’s data service account for
class - User name: fin4192022
Password: fin419summer2022
GRADING
Final Exam
Group Project & Presentation
Assignments/Presentation:
Total
40%
30%
30%
100%
Final Exam:
A final exam will be given at the end of the term, which is due roughly in 2 weeks from
the date given. Specifically, it will be given at the beginning of week 7 and due at the
end of week 8. This is a group exam, where each group should consist of 3 members and
submit one solution with all group members’ names on it. This exam will be an openbook, open-notes, take-home exam based upon the textbook, lecture notes, assignments,
and the computer lab exercises using SAS.
Group Project & Presentation:
Week 9&10 are scheduled for group project write-up & presentations. No lectures or
other activities will be scheduled for these two weeks to better prepare students for
project & presentation submission. Each group will submit via email the power point
slides of team presentation of the project of about 15-20 minutes, along with a short, brief
project paper (approximately 10 pages including exhibits and references etc., double
spaced, 12 font).
This project should begin as soon as possible and cover the entire term. Each team will
identify/ask an interesting question in the area of economics, finance, accounting,
management, and/or other businesses. Then students will do literature review on the
question being posed, form hypotheses to be tested, collect and clean the data needed,
conduct statistical/regression analyses, report and interpret their findings, and finally
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draw conclusions in an empirical paper. Examples from prior students’ papers are
available on Canvas “Week 9 & 10” folder for your reference.
Assignments/Group Presentation:
Three (3) assignments will be given over the term. One assignment is scheduled to be
given in week 2, 4 and 5, and due at the end of week 3, 5 and 6 respectively. For each
one of these assignments, four groups will be presenting their findings. Each presenting
group is expected to submit a power point slide of their team presentation of 3-5 minutes.
The rest of the class need to submit their individual analysis output via email. Students
are graded primarily based upon their efforts rather than accuracy of their solutions, and
will typically be assigned full credits for their assignment submissions. Please note the
assignments are used to prepare students for the final exam and team project. Flexibility
will be exercised.
Important Dates:
Final Exam
Project Presentation Recording & Paper
Three Group Assignments
Due at the end of Week 8 (via email)
Due at the end of Week 10 (via email)
Due at the end of Week 3, 5 and 6
Academic Integrity:
Each student is expected to honor and adhere to University’s standards of academic
integrity, which can be found at http://www.pdx.edu/dos/academic-misconduct.
Examples of academic misconduct include, but are not limited to cheating on an exam,
copying the homework of someone else, submitting for credit work done by someone else
(include plagiarism and paraphrasing without citing sources), stealing examinations or
course materials, tampering with the University’s grade records, or with another student’s
work, and knowingly and intentionally assisting another student in any of the above
misconduct.
ACCESS AND INCLUSION FOR STUDENTS WITH DISABILITIES
PSU values diversity and inclusion; we are committed to fostering mutual respect and full
participation for all students. My goal is to create a learning environment that is
equitable, useable, inclusive, and welcoming. If any aspects of instruction or course
design result in barriers to your inclusion or learning, please notify me. The Disability
Resource Center (DRC) provides reasonable accommodations for students who encounter
barriers in the learning environment.
If you have, or think you may have, a disability that may affect your work in this class
and feel you need accommodations, contact the Disability Resource Center to schedule
an appointment and initiate a conversation about reasonable accommodations. The DRC
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is located in 116 Smith Memorial Student Union, 503-725-4150, drc@pdx.edu,
https://www.pdx.edu/drc.
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If you already have accommodations, please contact me to make sure that I have
received a faculty notification letter and discuss your accommodations.
Students who need accommodations for tests and quizzes are expected to
schedule their tests to overlap with the time the class is taking the test.
Please be aware that the accessible tables or chairs in the room should remain
available for students who find that standard classroom seating is not useable.
For information about emergency preparedness, please go to the Fire and Life
Safety webpage(https://www.pdx.edu/environmental-health-safety/fire-and-lifesafety) for information.
TITLE IX REPORTING OBLIGATIONS
(incidents of sexual harassment and interpersonal violence)
As an instructor, one of my responsibilities is to help create a safe learning environment
for my students and for the campus as a whole. Please be aware that as a faculty member,
I have the responsibility to report any instances of sexual harassment, sexual violence
and/or other forms of prohibited discrimination. If you would rather share information
about sexual harassment, sexual violence or discrimination to a confidential employee
who does not have this reporting responsibility, you can find a list of those individuals.
For more information about Title IX please complete the required student module
Creating a Safe Campus in your D2L.
Library Resources: Library resources are available through the D2L widget (on the
lower right hand side of a D2L course site) and the library website at
https://library.pdx.edu/ The library homepage provides easy access to books, journals,
articles, and databases. Check out the Subject Guides or Course Guides for curated
resources and research strategies on a topic or a course. Use Ask a Librarian (link on the
upper right hand side of every library page) to contact us if you have any questions.
Diversity Perspectives (Diversity of voice): Both the business environment, local
community and our student population are becoming increasingly diverse. Thus it is of
ample importance to adopt a broad and diverse set of views and recognize diverse voices
in business and financial decision making. Where possible, this course will attempt to
choose cases, readings, and data sources in analytics to reflect perspectives of women,
minorities, cultural viewpoints, and other diverse populations. For instance, in one
project, student will employ data on US corporations’ pro-diversity policies, that is, firm
policies that promote women, minorities, the disabled, and LGBTQ as well as promoting
an inclusive firm culture, and analyze how these pro-diversity policies will affect market
valuation and innovative capacity of the companies. In addition, our reading list includes
articles/cases written by woman and/or minority authors to reflect diverse perspectives
and voices.
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Diversity Engagement (Diversity of learning styles): Giving the changing
demographics of our student population and local business community, we endeavor to
provide culturally diverse and relevant approaches towards learning so as to foster
student engagement and appreciation for diversity and inclusion. Our use of a variety of
modalities of coursework preparation and deliverables allows students with a variety of
learning styles and diverse backgrounds to be successful. Through the inclusion of
diverse perspectives, examples, and learning activities, this course provides a space for
all students to engage with concepts of diversity in business. The focus on inclusion and
diversity is one of the core values of the School of Business and this course will provide a
space for all perspectives and opinions. For example, this course requires student group
projects on analytics. While accommodating students’ preferences, wherever possible, we
will try to pair students with different backgrounds and learning styles.
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Tentative Class Schedule*
Part I: Regression Analysis with Cross-Sectional Data:
Chapter 2: The Simple Regression Model (WK 1&2)
Group Project & SAS Programming: Estimating Company’s Market Risk via
CAPM and Predicting Future Stock Returns
Introduction of WRDS database & Virtual Lab at PSU
Assignment #1 is Given in Week 2
Chapter 3: Multiple Regression Analysis: Estimation (WK 3)
Introduction of Data Source on WRDS for Multi-factors Data
Assignment #1 is Due at End of Week 3
Chapter 4: Multiple Regression Analysis: Inference (WK 4)
Group Project & SAS Programming: Estimating Fama-French Three-Factor Asset
Pricing Model and Carhart Four-Factor Asset Pricing Model; Predicting Future
Stock Returns Using Multi-factor Pricing Models
Assignment #2 is Given in Week 4
Part II: Advanced Topics & Issues:
Chapter 8: Heteroskedasticity (WK 5 & 6)
Group Project & SAS Programming: Estimating Fama-French Three-Factor Asset
Pricing Model and Carhart Four-Factor Asset Pricing Model; Predicting Future
Stock Returns Using Multi-factor Pricing Models Under Heteroskedasticity
Assignment #2 is Due at End of Week 5
Assignment #3 is Given in Week 5
Assignment #3 is Due at End of Week 6
Part IV: Preparation for Group Project:
Chapter 1: The Nature of Econometrics and Economic Data (Students’ Own Readings)
Chapter 19: Carrying Out an Empirical Project (Students’ Own Readings)
*Subject to changes and adjustments without prior notice. WK 7&8 are the final exam
weeks and WK 9&10 are the final team project weeks, respectively.
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