MBAD/F 617: Optimization and
Financial Engineering
Instructor: Linda Leon
Fall 2011
http://myweb.lmu.edu/lleon/mbad617/
Course Background
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Financial engineering is a multidisciplinary
field involving the application of quantitative
methods to finance.
Used for quantitative analyst positions in
securities, banking, financial management
and consulting industries
Optimization models can help a manager
maximize/minimize objectives or just quickly
produce feasible solutions for highly
constrained problems
Financial Engineering Examples
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Grantham, May, Van Otterloo & Co., an investment
management firm with $26 billion assets, developed
a mixed integer programming model to design
portfolios that achieve investment objectives while
minimizing the number of stocks and transactions
required.
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GE Capital, a $70 billion subsidiary of GE financial
services business, developed an optimization
model to allocate and schedule the rental and debt
payments of a leveraged lease which allowed
analysts to target profitability as well as optimize
NPV of rental payments.
Another Example:
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TFM Investment Group, which was designated as a
market maker in exchange traded funds (ETFs) in
2001, used integer programming to minimize the cost
of producing creation units while remaining hedged.
A second optimization technique was used to
minimize the beta-dollar difference between the ETF
and the portfolio of constituent stocks which
minimized the tracking error between the current
position in the basket of stocks and the number of
short ETFs in TFM’s portfolio.
Optimization & Financial Engineering
Financial Management
Financial Markets
Working Capital Mgmt
Identifying Arbitrage Opportunities
Capital Investment Planning
Security Design
Short Term Financial Planning
Portfolio Management
Optimization & Financial Engineering
Financial Management
Working Capital Mgmt
Financial Markets
Identifying Arbitrage Opportunities
Portfolio Management
Portfolio Structuring
Efficient Frontiers
Cash Budgeting
Foreign Exchange Markets
Multiperiod LP Models
Security Design
NLP Models
Ethical Mutual Funds
Capital Investment Planning
Municipal Bond Underwriting
Data Envelopment Analysis
Capital Budgeting
IP Models
Short Term Financial Planning
Multiple Objectives
Goal Programming
Leveraged Leases
Financial Modeling
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Many financial models which use advanced
modeling and analytical techniques are
spreadsheet based
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There is a market demand for more
sophisticated models and analysis by
financial end-users
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Most end-users prefer to develop their own
models (cost,flexibility)
A model is valuable if you make better decisions
when you use it than when you don’t!
Symbolic World
Management
Situation
Real World
Results
Interpretation
Abstraction
Model
Analysis
Decisions
Intuition
Decision Support Models
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Force you to be explicit about your objectives
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Force you to identify the types of decisions that influence those
objectives
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Force you to think carefully about variables to include and their
definitions in terms that are quantifiable
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Force you to consider what data are pertinent for quantification
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Force you to recognize constraints on values that variables may
assume
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Allow communication of your ideas and understanding to
facilitate teamwork
Decision Models
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Inputs
• Decisions which are controllable
• Parameters which are uncontrollable
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Outputs
• Performance variables, or objective functions, that
measure the degree of goal attainment
• Consequence variables that display other
consequences so results can be better interpreted
Deterministic –vs- Probabilistic Models
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In deterministic models, all of the relevant
data (parameter values) are assumed to be
known with certainty.
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In probabilistic (stochastic) models, some
parameter input is not known with certainty,
thus causing uncertainty in the other
variables.
Two General Approaches to
Financial Modeling
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Simulation
• Process of imitating the firm so that the
possible consequences of alternative
decisions and strategies can be analyzed
prior to implementation (MBAD/F 619)
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Optimization
• Identifies which decision alternative leads
to a desired objective given a specified set
of fixed assumptions (MBAD/F 617)
Advantages of End-User Modeling
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End-users get closer to the raw data and the
assumptions being made
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End-users can customize the models to
generate information that fits their needs
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End-users can see results easily and
immediately, which enhances strategy
generation and encourages risk analysis
Disadvantages of End-User Modeling
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Incorrect information is generated by inappropriate or
inaccurate models (20 to 40% contain significant
errors)
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End-users are overconfident about the quality of their
own spreadsheets
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Poorly designed models can discourage strategy
generation and risk analysis
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End-users may not always employ the most
productive methods for generating insights or may
misinterpret the generated information
Recent spreadsheet research
shows…
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End users typically do not plan their
spreadsheets
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End users rarely spend time debugging their
models
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End users almost never let another person
review their spreadsheets
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Many end users do not consistently use tools
that can make modeling productive and
insightful
Course Objectives: Students
should be able to
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Construct decision-support spreadsheet
models to analyze various complex,
multi-criteria financial applications.
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Apply advanced analytical skills in
modeling and decision-making with an
emphasis on optimization techniques.
Course Objectives (continued)
Critically analyze and integrate
information provided by the use of
optimization techniques into the
decision-making process.
 Implement appropriate organizational
controls and spreadsheet design skills
to mitigate the risks of a misstatement in
a financial spreadsheet.
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