MBAD/F 617: Optimization and Financial Engineering

1. MBAD/F 617: Optimization and Financial Engineering Instructor: Linda Leon Fall 2011 http://myweb.lmu.edu/lleon/mbad617/

2. 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

3. Financial Engineering Examples • 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. • 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.

4. 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.

7. 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)

8. A model is valuable if you make better decisions when you use it than when you don’t! Symbolic World Analysis Model Results Interpretation Abstraction Management Situation Decisions Intuition Real World

9. 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 • Force you to recognize constraints on values that variables may assume • Allow communication of your ideas and understanding to facilitate teamwork

10. 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

11. 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.

12. 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)

13. 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

14. 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

15. 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

16. 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.

17. 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.

18. Financial Modeling Competition http://www.modeloff.com