One of the hottest tools in the investment industry is Monte Carlo simulation or, if you would like the $20 term, stochastic modeling. Why has this esoteric modeling methodology garnered so much attention so quickly? How is it different than what we have done in the past? What is the best way to use it, and how can we avoid misusing it?
In an uncertain world, there is comfort in having confidence. The bear market has shaken the confidence not only of investors, but also of their financial advisors, and rightly so. What investors and advisors have always desired was some certainty in what they were doing. Of course, our industry is founded and regulated on the premise that there are no guarantees. We may have price targets for thoroughly analyzed stocks and track records that supposedly demonstrate investing skill, but in reality we all know that despite these efforts and information, things do not always go as planned.
So you see the apparent irony in Monte Carlo simulation. This methodology allows one to see the likelihood that things may actually end up far worse than anyone would normally plan, often with very scary results. But it ends up being a tool exploited to provide comfort and a sense of confidence.
If you have been living under a rock or hiding under your desk, you may still be unfamiliar with how Monte Carlo simulation works. For math and statistics geeks, the concept is brilliantly elegant and simple. For the rest of us, when we try to start to understand it a step at a time, it is a little like entering the Black Forest; each step takes us further into the darkness.
Simply put, the concept of Monte Carlo in investment planning is the ability to stress test a package of financial goals along with an investment strategy to measure the odds of achieving those goals. The odds exposed by Monte Carlo can provide confidence and, therefore, comfort. In contrast to what we have done in the past, these odds–this confidence and resulting comfort–have been, historically, completely avoided and ignored.
Traditionally, we made an assumption about investment returns. By merely disclosing to clients that all we did was make assumptions, we left him in the dark. We disclosed that past performance was no indication of future results, and that we had no idea of whether things would work out as planned. But, if the assumptions by luck happened to be similar to what we projected, things would be all right. Of course, we couldn't tell clients the odds of things going the way we assumed.
With Monte Carlo, if used correctly, instead of taking that uncertainty and simply leaving the client in the dark about it, we can actually exploit that uncertainty to know how confident and comfortable the client can be about achieving his goals.
Our traditional uncertain projections are normally based on a fixed average return assumption, which conceptually gives us 50/50 odds. Most clients would not be comfortable basing their entire financial future on the flip of a coin, and clearly they wouldn't pay for advice with those odds. Maybe this is why we have not historically exposed the odds. Fortunately, financial advisors are trained to be conservative and in a study we at Financeware Inc. did of over 13,000 clients of financial advisors, the average investor had nearly a 60% chance of things working out as planned.
This statistic is really not what is important, however. Monte Carlo is important because prior to it, the odds were completely unknown. Now we can harness the technology to calculate these odds and produce the comfort that goes with that knowledge.
A nuance that is often the focus of Monte Carlo corrects for another mistake in our prior projections. While historically we identified investor risk tolerance and thoroughly analyzed asset allocation for risk and return efficiency, the projections we have made assumed that regardless of how much risk the investor was taking, it would disappear when it came to projecting the financial future.
Think about this for just a moment and you will start to see the forest. What is the standard deviation, the maximum drawdown, or investment risk modeled in a planning projection that assumes the same return each year? It is zero. While historically we identified risk tolerance and analyzed asset allocation efficiency, and while we know portfolios with less volatility are more efficient, instead of planning on this volatility we assumed that it disappears when it comes to projecting the client's financial future. Just as a portfolio with equity-like returns and no volatility would look fabulous on our risk/return efficient frontier, so do the results of a wealth management plan that makes the same assumption. Table 1 demonstrates this effect.
Results for an investor with $100,000, spending approximately $11,600 a year for 10 years with three different portfolios, all with the same compound return: In Table 1 we see that our projection using the same return each year could have overstated the results by nearly forty-fold (if we started with a bear market) or understated the results by 60% (if we started with a bull market). You may notice that the returns for the bull and bear markets are the same. They just happened in the opposite order. Many advisors mistakenly assume the extra money produced in the "bull market first" example, or the shortfall in the "bear market first" is due to the compounding effect of the results in early years. This really is not completely the case, as it is dependent on the client's unique plan and the opposite relationship can easily be see in a different client scenario as shown in Table 2.
