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Financial advisers rely on the Monte Carlo method as the financial success predictor

McClatchy-Tribune Information Services

November 21, 2016


PITTSBURGH — There are a few common questions that many clients will eventually ask their financial adviser to answer.

How much will my portfolio be worth at retirement? Will I outlive my money? How would my plan be affected by a stock market downturn?

Chances are the adviser will not try to answer any of those questions before running the numbers through a computerized mathematical system that is practically unknown to the general public, yet widely used in the financial services industry. It is called the Monte Carlo method.

It has nothing to do with French Riviera, casinos, Grace Kelly or James Bond.

The Monte Carlo method used by financial advisers is a technology that analyzes the likelihood of a client’s portfolio being successful. The adviser inputs certain data, such as the client’s age and what assets are in the portfolio. The computer looks at thousands of portfolio return outcomes over multiple time periods and market conditions to arrive at the most likely — or highest probability — outcome.

Some form is used by just about every wealth management firm that has made an investment in financial planning tools and resources. But some industry players also warn that these simulations are only as good as the quality of the information provided, and that improper use can give users a false sense of security.

Robert Hapanowicz, president of Hapanowicz & Associates Financial Services in downtown Pittsburgh, said his firm has been using this sort of analysis in financial and retirement planning for more than five years. "Monte Carlo enables us to analyze a client’s plan across many different life scenarios, thus providing a very effective way to stress test the plan and estimate the probability of success."

One of the things advisers love so much about the method is its strong basis in mathematical statistics.

"With Monte Carlo, you can model thousands of potential life outcomes by using random variables — such as stock market returns, inflation rates, interest rates — to measure probability of success within a financial plan," Hapanowicz said.

A Monte Carlo report will assign different probabilities to different outcomes. A 95 percent probability is considered the gold standard.

GAMBLING AND SOLITAIRE

The method was named by mathematician Stan Ulam in 1946. The name refers to the casino in Monaco where his uncle would borrow money from relatives to gamble. According to mathematics website Motherboard, Ulam was inspired to develop the method with his partner John Von Neumann while playing solitaire and trying to calculate the likelihood of winning based on the initial layout of the cards.

The computational algorithms are used in a variety of fields outside of financial services, such as production management, engineering, physics and weather forecasting.

Robert Johnson, president and CEO of the American College of Financial Services in suburban Philadelphia, said those who rely too heavily on Monte Carlo reports should be reminded of previous instances where quantitative models have failed.

One of the greatest examples, he said, is the failure of Long Term Capital Management, a firm founded by some of the best minds in finance, including Nobel Prize winners. The Greenwich, Conn.-based hedge fund nearly collapsed the global financial system in 1998 as a result of high-risk arbitrage trading strategies.

"A more recent example is the failure of rating agencies to properly assess the risk of mortgage-backed securities failing," Johnson noted.

"Monte Carlo simulations give users the illusion of precision," he said. "That is, you can construct a retirement portfolio that, according to Monte Carlo simulation, will only result in a 3 percent chance of a shortfall. Yet, that assumes that your assumption and inputs accurately reflect the future, as are the interactions between variables consistent with the past."

A GREAT TOOL

Nick Besh, investment director for PNC Bank, sees Monte Carlo analysis as a great tool to show probability of outcomes of a total portfolio of diversified assets. But it is not a forecaster of investment returns, nor is it used to evaluate individual stocks.

"Financial planning uses capital market assumptions — expected long-term returns and standard deviation — for each asset class used in a portfolio," Besh said. "These assumptions are devised using historical data combined with projections of future returns.

"Monte Carlo analysis helps us to determine the most likely, or highest probability, outcome based on the assumptions used," he said. "This type of analysis is widely used and present in every financial planning software tool."

Besh said all projections PNC Bank shares with clients come with disclosures that past performance is not indicative of future results. "Again, Monte Carlo is just a quantitative method that gives us more confidence in our projections — but never 100 percent," he said.

A financial planner who is sold on the method is Martin Schamis, vice president and head of wealth management at Janney Montgomery Scott in Philadelphia.

"I am pro-Monte Carlo," he said. "I feel the Monte Carlo system is a very powerful set of tools for investment professionals. Many of the criticisms we see of Monte Carlo aren’t about the methodology or system itself, but about the input that we use to set the starting conditions.

"When we use the Monte Carlo in an investment portfolio, we have to put in a series of data that defines how we believe the market will behave. In many cases, we’re using historical data to predict what the future will look like. However, as we know past performance is no guarantee of future returns."