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How to Close the Retirement Income Uncertainty Gap: David Blanchett

Retirement Income

High-profile retirement income researcher David Blanchett came to the defense of Monte Carlo analysis this week, noting economist John Maynard Keynes’s famous quote that he’d prefer to be “vaguely right than precisely wrong.”

Calling it a powerful tool for good that provides valuable information about retirement income uncertainties, the bigger concerns are the outcome metrics used to determine success. For example, he argued that success rates, the most common Monte Carlo metric, can result in a suboptimal outcome. 

“Success rates are agnostic when you don’t accomplish your [retirement income] goal,” Blanchett, Managing Director, Head of Retirement Research with PGIM DC Solutions, explained. “You could accomplish your goal entirely up until the last year of retirement and fall a dollar short, and you would be defined as failing.”

While more advisors are getting comfortable with stochastic modeling over the past decade, the underlying tools used in Monte Carlo simulations have not kept pace. 

He pointed to concepts discussed in Redefining the Optimal Retirement Income Strategy, a paper he published last year, as possible solutions, referencing three in particular. The first is the notion people can change their consumption.

“That’s incredibly intuitive. Of course, they can, yet retirement is largely treated as a single liability. They need $100,000 a year, and they’re just angry if they fall a penny short. They can’t fathom the possibility of making a change. Well, that doesn’t reflect reality.”

He continued that the problem with most models is that they don’t allow for non-constant cash flows. 

“A lot of the metrics that have been used wouldn’t work in a real financial plan with all these crazy cash flows,” he continued. “What I suggest introduces the utility function. Technically, success rates utilize a utility function where you get a value of one if you accomplish your goal, or you get a zero if you do not. It’s binary. But this utility function is more granular and looks at how you accomplish the goal. One thing I actively recommend to advisors is to not focus on success rates and focus on things like goal completion. You could have a 0% success rate, but on average, complete 99.5% of your goal, but because you fell just a little short, it doesn’t register at all.”

Remove the binary success/failure metric for more realistic and encouraging retirement outcomes, he concluded. 

“Even if they fall short of savings goals, virtually every American has Social Security. They have pension benefits. Even if they’re not successful or deplete all their savings, they’re still going to have some income. So, changing your metrics and quantifying the outcomes can result in better decisions.”