Gerard Dougherty: Monte Carlo Analysis and Investment Risk
Gerard “Gerry” Dougherty, a nationally recognized retirement specialist and president of Boston Independence Group, Inc., has spent more than 30 years helping individuals build secure and sustainable retirement plans. A recipient of the National Social Security Advisor designation and author of Uncomplicated Money, Gerard Dougherty is known for translating complex financial concepts into practical strategies for retirees. He is also the longtime host of the radio program Making Money Last and the podcast Retirement Is Within Reach, where he addresses income planning, investment risk, and wealth preservation. With academic training from the University of Massachusetts, Amherst, the American College of Financial Services, and Penn State University, Mr. Dougherty brings deep expertise to evaluating tools such as Monte Carlo analysis. Here, he highlights how this method helps investors understand risk, evaluate uncertainty, and build retirement strategies based on probability rather than assumption.
Monte Carlo Analysis and Investment Risk
Monte Carlo analysis calculates risk in various portfolio holdings, especially when targeting a specific retirement income amount. It takes a set of fixed input values and leverages principles of probability distribution in predicting a set of outcomes across an estimated range of values.
The roots of this method stem from the pioneering work of mathematician Stanislaw Ulam, who worked on the Manhattan Project at Los Alamos during World War II. Ulam escaped persecution in Europe and became known for applying esoteric math skills to solve complex physics challenges. Along with Edward Teller, he was one of the main designers of the hydrogen bomb.
In 1946, following the war, Ulam took a teaching position at Los Alamos and experienced alarming headaches and numbness that led to emergency brain surgery. Unable to speak during his convalescence, he did not stop pursuing mathematical enquiry, and often played solitaire as a way of staying active. During one such card session, he posed himself the question of the odds of a hand laid out with 52 cards coming out successfully (playing all one’s cards in red-black or black-red combinations, and winning the game). There are a vast number of ways to sort a card deck, approximately equal to the number of atoms estimated to exist in the observable universe. To simplify matters, Ulam forewent combinatorial calculations and laid out the deck 100 times, counting how many plays were successful (assuming that each play began with randomized conditions).
With colleague John von Neumann recommending the use of statistics and probability in creating a computational method, they developed a random sampling tactic that generated a wide range of possible solutions, assigning likelihoods to each. They assigned an uncertain variable multiple values, which gave multiple results. The average provided an estimate. From here, they employed statistical analysis in calculating potential neutron diffusion after a hydrogen bomb detonation.
Over the decades, professionals have applied this method to various situations involving a large number of random variables, ranging from forecasting stock market volatility to understanding how an electromagnetic pulse (EMP) attack is likely to impact the environment. The Monte Carlo simulation models how randomness affects any system, producing a range of possible results.
Financial planners often use Monte Carlo analysis to predict the likelihood that a client will deplete their portfolio funds in retirement, considering various market scenarios and investment outcomes. Expressed as a percentage, a Monte Carlo score of 70 for a specific allocation pathway indicates that 70 percent of test simulations resulted in a positive balance at the end of the assigned period. In comparison, 30 percent of simulations ran out of funds.
The advantage of this approach is that it acknowledges variability and unpredictability, hallmarks of complex stock markets and macroeconomic environments. Other financial planning approaches tend to assign a set annual rate of return, such as eight percent, across the planning period duration. The problem with this is the assumption that market returns steadily increase in a straight line, year after year, which they do not.
With Monte Carlo analysis, investors typically aim for a risk-reward matrix in the 80 to 95 percent range, indicating a relatively high certainty that the investment strategy will yield a positive return. A perfect score of nearly 100 is problematic because any investment with growth potential also necessarily assumes some level of risk.
About Gerard Dougherty
Gerard “Gerry” Dougherty is the president of Boston Independence Group, Inc., a retirement planning firm dedicated to helping clients build stable and sustainable financial futures. A National Social Security Advisor and author of Uncomplicated Money, he has more than 30 years of experience simplifying complex financial concepts for retirees. Mr. Dougherty hosts Making Money Last on AM830 WCRN and the podcast Retirement Is Within Reach, where he shares insights on income planning, investment strategy, and wealth preservation.