Speakers and Abstracts

The Government of Canada has instituted a requirement that the Chief Financial Officers of departments must personally attest to the reasonableness of financial information presented to the government for decision-making. If the information underlying a potential decision is flawed, executives need to factor this into their decision. Data integrity factors are used to assess the validity for the data underlying the financial information to develop a range for each cost element and Monte Carlo simulation is used to quantify the potential financial risk in a proposal. This increases the fiscal awareness of departmental executives in program development and has resulted and has caused multimillion-dollar proposals to be rewritten to reduce the potential unfunded liability to an acceptable level.

Using Monte Carlo Simulation for Valuation, Pricing, and Dealmaking

Dr. Richard Razgaitis, Senior Advisor, CRA International, Inc.

Technology expressed in various forms of Intellectual Property (IP)--such as patents, know how, copyrights, and trademarks--is more challenging to value and price, and transact, because of the challenges in forecasting the future utilization and benefits to its present or some number of potential owners/licensees. There are six methods for performing such valuations discussed in three John Wiley & Sons published books written by Dr. Razgaitis (aka "Raz"). The 'gold standard' of business valuation is usually taken to be the NPV (Net Present Value) of future DCF (Discounted Cash Flow) values. For technology licensing transactions, Monte Carlo simulation can be a significant tool with each aspect of valuation, pricing (ascribing an aspiration to a value), and dealmaking (the dance of persuasion around value and price). In addition there is increasing interest in Real Option techniques, particularly for 'look-see/standstill' transactions which may precede a subsequent valuation and license agreement.

Watch Your Back. What Today’s MBAs are Learning About Financial Simulation

Anthony Webster, Adjunct Professor, Columbia University

Advances in analytical tools like Crystal Ball have made sophisticated simulation and optimization tools accessible to today’s MBA students. Today, students master complex simulation methods that were typically taught in engineering schools 20 years ago. In the corporate world, freshly minted MBAs are using these methods to solve serious financial problems that would have required a consultant’s expertise in the past.

This presentation will provide an overview of the simulation and optimization techniques taught to Columbia University’s MBA students, and present real-world problems that our MBAs and alumni have solved during the past several years. Specific simulation-based topics covered include:

Risk policies and risk measures
Multi-period portfolio optimization
Pro forma projections
Valuation
Hedging

Applications of Monte Carlo Simulations to the Management of a Corporate Information Technology Investment Project Portfolio

Dr. Robert F. Brammer, Vice President and Chief Technology Officer, Northrop Grumman Information Technology

This presentation describes how Monte Carlo techniques and Crystal Ball software are used in the management of a portfolio of corporate information technology investments. The Information Technology Sector of the Northrop Grumman is a multi-$billion information systems integrator and managed services provider. Last year we were rated by outside industry analysts as the #1 Government systems integrator and the #1 provider of software and information technology services to the defense and intelligence sector.

To continue our industry leadership, we make investments in a variety of projects designed to develop leading edge capabilities. The results of these projects include unique intellectual property and prototype systems that enable us to compete successfully for large programs.

The definition and selection of these investment projects is a highly competitive process. Consequently, we value each proposed investment project according to revenue and profit generation potential, and we balance the portfolio among key technology areas that are necessary to address the full range of technical capabilities necessary to compete for the development of large-scale government information technology systems and services. The valuation techniques are based on probability models of winning competitions based on assumptions about the success of our investment projects and the risks of competition. This presentation will show examples of how these modeling techniques are used in both investment project definition and selection and in the ongoing portfolio management.

Capturing and Displaying Uncertainty in New Product Development Modeling

Joe DiGiacomo, Financial Analyst, W.L. Gore & Associates

In organizations where research and development (R&D) represents a significant investment in resources, a decision to expand or cut back a projects scope or to terminate altogether, involves a degree of risk. Effective decision making must include an understanding of a project’s potential value and inherent risk. In this discussion, I will review W.L. Gore & Associates' approach to assessing new product development and how Monte Carlo simulation, as enabled by Crystal Ball, provides the engine by which the effect of uncertainty is displayed in meaningful metrics that correspond to shareholder value.

Simulation and Optimization for Individual Financial Planning

Steve Hoye, Senior Risk Consultant, Oracle’s Crystal Ball GBU

Planning for an individual or couple's retirement is rich with many unknown and highly variable inputs, like longevity, capital market performance over time, possibility of severe health problems and long term care requirements, and the dependability and size of government contributions to retirement and health care costs. Given these many unknown critical inputs, Monte Carlo simulation is a highly effective technique for analyzing and quantifying the risk that peoples' financial plans will fail to meet their needs during their working years and into retirement.

In this presentation the author presents a series of interesting models and approaches to simulating financial strategies and plans, and quantifying the probability that certain strategies will result in successful investing and a timely and satisfying retirement. Specific techniques demonstrated include:

Investment Performance / Asset Allocation and Portfolio Optimization
Life Insurance Requirements - How much and for how long?
Mortality and Longevity Simulation - How long will my retirement income need to last?
Social Security / Pensions
Loss of Income