Mathematics & Science Division
Graduate Electives
2008
QTM7570
Financial Data Analysis
This course will introduce students to mainstream financial problems present in investment
management and the benefits of using statistical techniques such as advanced multiple linear
regression and logistic regression to determine solutions. Among the problems to be considered
are modeling stock prices, earnings per share, mutual fund returns, trading volume, and the
volatility of a country's stock market based upon quantitative and qualitative variables such as the
exchange on which the stock is listed. In addition computer-intensive techniques will be
presented to reduce the number of possible variables in the model. The identification of unusual
observations in such models will be shown to sometimes represent opportunities. Models will
also be developed to determine the likelihood of bankruptcy and of an individual's credit risk.
Prerequisite: (FIN7000 & QTM7010) or (MBA8510 & QTM8400) or completion of one-year or
two-year modules
QTM7571
Business Intelligence and Applied Data Mining
(formerly Applied Data Mining)
This course will examine the methods and challenges faced in extracting meaningful information
from large databases and the growing number of applications for these techniques. Students will
learn new techniques for data gathering and data analysis including neural nets, correlative
webs, rule set development using classification nodes, and cluster analysis . These analysis
techniques will be used to understand how to turn the information into a knowledge base, to
synthesize enormous amounts of data, and to identify new or surprising patterns which can't be
found using classical statistical analysis. We will analyze databases from marketing, finance,
accounting (auditing) as well as genomic and on-line click streams data and online surveys.
Instructional modes (e.g. lecture, laboratory, seminar, internship, film, etc.): The course will
include hybrid delivery where some classes will take place online and others will be face-to-face.
The course will utilize a hands-on approach to understanding the methods of data mining, as
well as understanding its links to Statistics and Information Systems and its Applications in
business, finance, economics, physical and social science. Lectures, case presentations,
computer labs and guest speakers will be used. Students will learn Clementine, a widely used
data mining software package.
Prerequisite: QTM7010 or QTM8400 or completion of one-year or two-year modules
QTM7574
Advanced Decision Making
The primary objective of this course is to learn how to incorporate our values and objectives into
our decision-making. One of the most difficult aspects of decision-making is to know exactly
what is important to you (your values or objectives) within the context of a given problem and
why it is important. Your values drive your decisions and the better you identify and understand
your values, the better decisions you will make. Thus, instead of the business-as-usual approach
where you chose between the options placed in front of you, we will learn how to create new options
that better meet our needs. Each student will learn how to structure their own values, then apply
these to a personal decision they currently face. A unique aspect of this course will be the use
of Excel-based software as an aid in the decision process. We will be using Decision Tools,
which is a suite of interrelated programs by Palisade Corporation.
Prerequisite: (QTM7010 & QTM7020) or (QTM8400 or QTM8100 or QTM8200) or completion
of one-year or two-year modules
QTM7575
Financial Modeling using Simulation and Optimization
The focus of this course is on developing spreadsheet models for a wide variety of financial
concepts including, but not limited to portfolio optimization, option pricing, asset allocation, value
at risk, asset prices, etc. Students will gain familiarity with the financial instruments through the
construction of the models, and will gain greater insights by analyzing and solving the models.
Simulation and optimization are used extensively to analyze the models. Particular attention is
paid to modeling uncertainty via random variables and the mathematics of stochastic variables.
Prerequisite: QTM8400 or (QTM7010 and QTM7020) or completion of one-year or two-year modules.
QTM7580
Independent Research
Provides an opportunity to conduct in-depth research in areas of a student's own specific
interest. Students may undertake Independent Research for academic credit with the approval of
the dean of the graduate school, the appropriate division chair, and a student-selected faculty
adviser. Authorization for such a project requires submission of a formal proposal written in
accordance with standards set forth by the graduate school. Students work closely with the
faculty adviser throughout the project. The research project normally carries three semester
hours of academic credit (six semester hours if warranted and with the approval of the faculty
adviser, graduate dean, and division chair).
Prerequisite: QTM8400 or (QTM7010 and QTM7020) or completion of one-year or two-year modules.
QTM9501
Business Forecasting
This course will introduce elementary time series models and discuss advanced forecasting methods in the context of real business data and decision-making situations. “The objectives of the course are to provide experience in using time series data (e.g., sales, profits, stock prices, economic indicators, industry sector indicators) to explain the impact of various internal and external factors and predict future trends; to provide a framework for comparing alternative forecasting models for validity, accuracy and feasibility; to enhance an appreciation for the limitations of forecasting models; and to develop skills at communicating statistical concepts, methods, result, and inferences effectively in a managerial context.” Teamwork and professional presentation of analysis and recommendations will be required during this course.
Prerequisites: QTM8400 or (QTM7010) and (QTM7020) or completion of one-year or two-year modules.
QTM9510
Optimization
This course provides an introduction to optimization models and their applications to a variety of business decision problems. Linear, integer, and network models will be discussed. Emphasis will be placed on understanding the relevant applications for various models, the strengths and limitations associated with the models, using software tools to solve optimization problems, and interpreting and analyzing solution results.
In-class analysis of problems and/or cases in a team setting will be utilized to facilitate learning the material covered in lecture. Teamwork outside of class will be required in order to complete assignments. Evaluation will be based on both individual and team performance.
