MBAD/F 617: Optimization and Financial Engineering
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Transcript of MBAD/F 617: Optimization and Financial Engineering
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MBAD/F 617: Optimization and Financial Engineering
Instructor: Linda LeonFall 2011
http://myweb.lmu.edu/lleon/mbad617/
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Course Background
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
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Financial Engineering Examples
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.
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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Another Example:
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.
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W o rk in g C a p ita l M g m t
C a p ita l In ves tm e n t P la nn ing
S h o rt T e rm F ina n c ia l P lan n ing
F in a nc ia l M a na g em e nt
Id e n tifying A rb itrag e O p po rtu n it ies
S e cu rity D e s ign
F in a n c ia l M a rke ts P o rtfo lio M a na g e m e nt
O ptim ization & Financial Engineering
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C a sh B ud ge ting
M u lt ipe rio d L P M o d e ls
W o rk in g C a p ita l M g m t
C a p ita l B u d ge ting
IP M o d e ls
C a p ita l In ves tm e n t P la nn ing
M u lt ip le O b je c tives
G o a l P rog ra m m ing
S h o rt T e rm F ina n c ia l P lan n ing
F in a nc ia l M a na g em e nt
F o re ig n E xcha n g e M arke ts
Id e n tifying A rb itrag e O p po rtu n it ies
M u n ic ipa l B o nd U n d erw rit ing
L e ve rag ed Le ases
S e cu rity D e s ign
F in a n c ia l M a rke ts
P o rtfo lio S tru ctu ring
E ffic ien t F ron tie rs
N L P M o d e ls
D a ta E n ve lo pm e nt A na lys is
E th ica l M utu a l Fu n ds
P o rtfo lio M a na g e m e nt
Optim ization & Financial Engineering
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Financial Modeling
Many financial models which use advanced modeling and analytical techniques are spreadsheet based
There is a market demand for more sophisticated models and analysis by financial end-users
Most end-users prefer to develop their own models (cost,flexibility)
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A model is valuable if you make better decisions when you use it than when you don’t!
Model Results
Management Situation
Decisions
Analysis
Intuition
Interp
retatio
nAb
stra
ctio
n
Symbolic World
Real World
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Decision Support Models Force you to be explicit about your objectives
Force you to identify the types of decisions that influence those objectives
Force you to think carefully about variables to include and their definitions in terms that are quantifiable
Force you to consider what data are pertinent for quantification
Allow communication of your ideas and understanding to facilitate teamwork
Force you to recognize constraints on values that variables may assume
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Decision Models
Inputs• Decisions which are controllable• Parameters which are uncontrollable
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
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Deterministic –vs- Probabilistic Models
In deterministic models, all of the relevant data (parameter values) are assumed to be known with certainty.
In probabilistic (stochastic) models, some parameter input is not known with certainty, thus causing uncertainty in the other variables.
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Two General Approaches to Financial Modeling
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)
Optimization• Identifies which decision alternative leads
to a desired objective given a specified set of fixed assumptions (MBAD/F 617)
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Advantages of End-User Modeling
End-users get closer to the raw data and the assumptions being made
End-users can customize the models to generate information that fits their needs
End-users can see results easily and immediately, which enhances strategy generation and encourages risk analysis
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Disadvantages of End-User Modeling
Incorrect information is generated by inappropriate or inaccurate models (20 to 40% contain significant errors)
End-users are overconfident about the quality of their own spreadsheets
Poorly designed models can discourage strategy generation and risk analysis
End-users may not always employ the most productive methods for generating insights or may misinterpret the generated information
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Recent spreadsheet research shows…
End users typically do not plan their spreadsheets
End users rarely spend time debugging their models
End users almost never let another person review their spreadsheets
Many end users do not consistently use tools that can make modeling productive and insightful
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Course Objectives: Students should be able to
Construct decision-support spreadsheet models to analyze various complex, multi-criteria financial applications.
Apply advanced analytical skills in modeling and decision-making with an emphasis on optimization techniques.
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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.