The Problems with Monte Carlo are in Your Mind

There are mixed feelings about Monte Carlo projections in our profession. Lots of the folks who don’t like Monte Carlo don’t understand what it is. At its core, Monte Carlo is a limitless tool to incorporate uncertainty into a given projection.

The issue is that many tools use overly basic assumptions, particularly around static withdrawals, and outcomes metrics that don’t provide useful context around accomplishing a financial goal, like the probability of success.

I’ll provide context on how we can improve financial planning projections to result in better forecasts, advice and guidance to households.

What is Monte Carlo?

Monte Carlo modeling was developed by John von Neumann and Stanislaw Ulam during World War II to improve decision making under uncertain conditions (related to the development of nuclear weapons) and named after the Monte Carlo Casino in Monaco. Over the last few decades, Monte Carlo projections have become a common way for financial advisors to demonstrate the uncertainty associated with planning projections and accomplishing various financial goals (e.g., retirement). Monte Carlo represents a notable evolution in deterministic forecasts that typically employed a constant assumed return using a single assumed run (or trial).