The Full Monte Carlo
Common pitfalls and leading practices in the application of Monte Carlo simulation
At one point in recent history, present value techniques were viewed as a leading practice for evaluating investments and transactions. Today, decision makers rarely choose a course of action without seeing the financial forecast, its net present value, and its rate of return. More recently, risk-adjusted forecasting approaches such as Monte Carlo simulation appear to be following a similar trend. Their appeal follows from their power to address a critical question posed by management: i.e., what is the likelihood my decision will result in the outcome I'm seeking?
Monte Carlo simulation is becoming more and more prevalent as an approach for risk-adjusted forecasting and planning not only because of its ability to capture uncertainties and risks, but also because it layers on top of financial spreadsheets, thereby leveraging existing approaches and capabilities. The outputs, such as value distributions and "tornado" sensitivities, are also visually appealing. Nevertheless, and as with any new tool, users should exercise caution to understand whether a simulation analysis yields relevant information. While Monte Carlo simulation may be a powerful predictive analytics tool, many organizations are concerned about arming analysts with fancy tools that do not yield transparent or insightful analyses (i.e., the potential for "fools with tools").
Download "The Full Monte Carlo" to learn how careful framing and data analyses, thoughtful selection, and communication of outputs may go a long way toward enhancing the quality of the decision-making process.
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