Probability-based retirement income strategies are highly sensitive to the capital market assumptions used in Monte Carlo analysis. Seemingly small changes in those assumptions can mean the difference between projecting a comfortable lifestyle and financial ruin.
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Many advisors use Monte Carlo analysis to evaluate withdrawal strategies for their retired clients. Using their financial planning tools, advisors calculate the “probability of success” of a given withdrawal strategy; if the probability is high enough, advisors may feel comfortable communicating that the plan is “safe.”
However, the results of Monte Carlo analysis depend heavily on the capital market assumptions (CMAs) used. It may seem that running thousands of Monte Carlo simulations is “scientific,” showing what would happen to a portfolio under all possible future scenarios.
But it is not.
The results from Monte Carlo are entirely determined by the CMAs used.
In this article, we evaluate the sensitivity of “safe” withdrawal rates and probability of success metrics to different CMAs, using a recent survey of investment firms’ CMAs. Our findings suggest that unless advisors are confident that they are using highly accurate CMAs, probability of success metrics will mislead clients into a false sense of security.
The standard model: Probability-based retirement income
We use the following example for our analysis in this article.
Jane is 65 and just retired. She has $1 million in an IRA, and her current allocation is 60% U.S. large-cap stocks and 40% U.S. investment-grade corporate bonds. She works with her advisor to arrive at a prudent portfolio and withdrawal strategy to make sure she does not run out of money by age 95.
Read the full article here by Massimo Young, Wade Pfau, Advisor Perspectives.
