Monte Carlo & Forecasting Return

Posted By: Advisor Analyzer Team

The existence of fortune tellers in modern day society inevitably leads us to believe people would like to gain insight of their future. While an investment professional’s arsenal may lack a crystal ball and large hoop earrings, they have at their disposal a tool for probabilistic forecasting known as Monte Carlo Simulation (MCS).

Monte Carlo Simulation Overview

MCS is a process by which probability distributions are generated from thousands of simulated outcomes and is often used in the financial planning and the determination of a portfolio’s asset allocation. More simply, it is a way to create thousands of possible scenarios your portfolio and the factors that impact your overall net worth can take. For example, you could lose your job in 5 years, or maybe in the next 2 months. There’s the possibility for markets to experience significant declines or rises over the investor’s time horizon. It’s feasible for an investor to become suddenly ill or maybe win the lottery. Who knows?

 

While the possibility of that such events unfold exist, the likelihood of their occurrence will vary per event. Monte Carlo Simulation recognizes the possibility of such events and assigns probabilities for each result to create an overall distribution for the investor’s expected portfolio performance. For example, the likelihood you’ll win the lottery is very slim, so of the thousands of simulated outcomes, it may include winning the lottery once. After creating thousands of these potential scenarios your portfolio and financial situation can take, it aggregates each result to generate an overall probability distribution of returns.

The Flaws of Deterministic Approaches

Traditionally, advisors have used a deterministic approach where historical figures are used as a guide to estimate a single value of the portfolio’s future returns. However, as the notorious financial disclaimer goes “Past performance is not an accurate indicator of future performance”. While we often use history as a guide, one has to ponder how often it led investors astray. To make matters worse, some advisors use the simplifying “rule of 7” (where one can expect to double one’s investments every seven years) or other rules of thumb thereby ignoring returns calculations all together.

The main disadvantage of the deterministic approach is fairly obvious: the single calculation provided by the advisor is rendered utterly useless should relevant variable deviate from expectations. Suppose the market doesn’t perform as predicted, inflation, interest rates or taxes were over or underestimated, or unanticipated health and employment issues arise, the resulting portfolio will differ from the forecasted values obtained via the deterministic approach. While we should all have faith in our advisor’s abilities, few would naively believe their advisors are able to predict the future with such precision.

Benefits of Monte Carlo Simulation

While the deterministic approach assumes only one path for your portfolio exists, Monte Carlo Simulation takes a less presumptuous view. The approach takes into consideration thousands of potential pathways the investor’s portfolio may experience. Suppose gas prices tripled in the next year, the simulation would factor the likelihood of such impacts in its calculations. Worried about sudden medical needs or getting fired from work? The simulation is able to incorporate these issues in calculating your ending portfolio value given such episodes.

 

In fact, Monte Carlo Simulation doesn’t simply provide a single ending value like the deterministic approach, but a range of possible portfolio values coupled with the probability that each result will occur. The results of the simulation provide a more encompassing overview, and the investor is left with further insight on a range of potential ending values. This ultimately makes for better decision making capabilities for both advisors and investors alike.

Conclusion

Therefore, the deterministic approach appears to be an inaccurate science at best, and it’s questionable if even a single case exists in all of investment history, where an advisor accurately predicted all relevant factors perfectly. I’d definitely invest my money with that guy or at least ask him tomorrow’s winning lottery numbers. For the unfortunate rest of us who lack such foresight, Monte Carlo Simulation is a great tool to obtain a variety of potential scenarios and aid our financial planning and investment decision process.