CHICAGO – The problem with target-date plans is no one really knows if the plan hits the target until the date arrives. And then it is usually too late for a new plan.
Thomas Idzorek, chief investment officer of retirement at Morningstar, said that is why adding data science to the art of financial planning is imperative. And now is the perfect time to do it.
“The world is moving toward big data,” said Idzorek, who has been working with a dream team of researchers to develop a new framework.
By pulling together that data into one tool, it takes a “meaningful step forward” to eliminate the guesswork that comes with building a retirement plan with optimal target-date funds based on plan characteristics, investment needs and participant demographics, he said.
Since 2006, plan sponsors have been given conditional fiduciary safe harbor when selecting default investment options for plan participants for three different types of funds with the Pension Protection Act. Those funds include target-risk or balanced funds, target-date funds or retirement managed accounts.
The Pension Protection Act, however, did not give plan sponsors much guidance on how and what should be considered when planning.
In other words, the “money wasn’t working for them,” Idzorek said.
With myriad technological advancements raising the bar, Idzorek and his fellow researchers found a way to add data and analytics to the soft science of advising.
Using what Idzorek calls a Retirement Managed Account Engine that analyzes factors like retirement age, investment needs, state taxes, employee matches and costs to the participant, plan sponsors can stop guessing and start making data-driven decisions on what qualified default investment alternative would best suit the participant’s needs.
The RMAE solves for what it thinks is the ideal asset allocation (equity level) for a given participant based on their unique circumstances.
For example: Maria Fernandez is 39 years old, and the RMAE thinks the ideal asset allocation for her is 75% equity and 25% fixed income.
Next, let’s assume that the default investment option in Maria’s retirement plan is the XYZ target-date fund family. Based on Maria’s age, she would be defaulted or assigned to the 2045 target-date fund (corresponding to her expected retirement age of 65), in which the 2045 fund has an asset allocation that is 60% equity (and 40% fixed income).
So the ideal equity level for Maria is 75%, yet by being placed in the default investment option she will only have 60% equity.
The difference between the ideal 75% equity level and the actual amount she will be invested in of 60% equity is 15 equity percentage points. In other words, the mismatch or degree of misallocation (misfit) for Maria is 15 equity percentage points.
Idzorek believes a tool this precise can help advisors better forecast their client’s retirement needs.
“We can raise our game,” Idzorek said. “I hope that this awakens that desire to want to bring more science to the art that we have all been pursuing.”
For plan sponsors thinking, “well, don’t risk tolerance questionnaires do the same thing?”Idzorek said, “RTQs are an OK way of doing that, but we think there’s a much better way.”
AdvisorNews Managing Editor Cassie Miller may be reached at cassie.miller@Adnewsfeedback.com. Cassie has an extensive background in magazine writing, editing and design. Follow her on Twitter @ANCassieM.