Stochastic optimization determines the optimimum configuration for your decision when the outcome is uncertain. This seminar explains the various components of a stochastic optimization model: decision variables, decision constraints, simulation requirements and targets. Several example models are explained to illustrate these ideas with ModelRisk. These models are also provided free of charge.
Stochastic portfolio optimization techniques allow the user to determine the best investment decision in situations where there are considerable uncertainty and correlations between the levels of performance of investments in a portfolio. You will learn how to set up an optimization model with decision variables, constraints, and tailor the goals to suit your specific needs. Example applications that will be demonstrated include:
- The level of investment in a combination of stocks that will maximize the expected return for a given level of risk;
- Determining the efficient frontier for a combination of stocks;
- Determining the combination of projects that will return the greatest profit, or give the smallest risk of a particular level of loss (the example of new drugs that a pharma company is considering developing will be used);
- Determining the use of feedstock to produce a range of products to maximize revenue (example uses include production at an oil refinery, aluminum plant, or petrochemical plant).
Business analysts, business planners, management consultants, investment advisers, corporate financial analysts, traders.