With the imminent rollout of Morningstar’s new risk evaluation service, advisors have a new way to assess whether or not their portfolios are overexposed across a wide book of business.
This should give them greater control – and more restful nights – over their client portfolios.
But there’s more to what Morningstar is doing than meets the eye. With a fin/tech bent, it’s a new era in risk management, with more portfolio risk models operating in a data-driven, algorithm-flavored environment.
Here’s the deal. The Morningstar rollout was announced April 26 at the company’s annual investment conference.
The new product is called Morningstar Data Catalyst and is designed to help investment management professionals “gain a clearer picture of business risk and portfolio suitability across an entire book of business,” Morningstar said.
The Morningstar software aims to aid financial advisors in targeting “unintended overexposure across multiple client portfolios managed at advisors' discretion,” the company explained.
The approach “identifies a suitable portfolio given an investor's goals, prioritizes investigation into sources of overexposure, and informs a firm's strategy for defining advisor roles and pinpoint opportunities for advisor training and development,” the company added.
The product is derived from Morningstar’s Global Risk Model, released in 2016, which tracks portfolio risk by monitoring each stock’s underlying economic exposure to 36 factors.
'Unmeasured Sources of Risk'
“Morningstar sees an unmet need to view risk through multiple, additional lenses,” noted Tricia Rothschild, chief product officer for Morningstar. “We work to understand risk at the firm level, and how it may create unintended risk consequences within individual client portfolios.
“We examine how the psychology and related choices of the individual investor can introduce unintended and often unmeasured sources of risk, particularly in the face of significant market events.”
Morningstar, like most fin-tech companies building their own risk assessment products, are using proprietary algorithms to provide advisors with more clarity on key risk and portfolio suitability scenarios.
That approach is opening some eyes on Wall Street.
“Determining the ‘right’ risk directive is a bit of a Goldilocks exercise,” stated Mark Friedenthal, founder of Tolerisk, a unique risk tolerance tool that uses a two-dimensional approach. “You don’t want the porridge to be too hot or too cold, with too much risk or too little risk. You want it to be just ‘right.’”
If clients are taking too much risk, they might not meet important financial obligations or take a big decline in their portfolio. That’s what the new risk assessment models are trying to avoid.
“Taking too little risk is a problem too,” Friedenthal added. “The client won’t get to retire as early or as well, or won’t leave as much to their heirs as they would have liked.”
Old Methods Fail in Two Ways
The problem is that most advisors still use rudimentary personality profiles to help clients determine the right level of risk.
“Unfortunately, they fail in two ways,” Friedenthal explained. “Most notably, they don’t incorporate actual cash flows, so they don’t contemplate an actual ability to take risk – just merely a client’s willingness to accept risk, which is only half of a complex equation.
“Additionally, they don’t evaluate the reasonableness of their own assumptions. It’s important for the advice they give clients to be based on a good premise and the advisor might need some help validating that.
No matter what algorithm-based risk assessment tool you pick, make sure it’s built on a “two-dimensional assessment of risk tolerance,” Friedenthal advised.
With clients growing preference to work with advisors who know how to blend financial technology tools with old-fashioned, face-to-face investment advice, a new era of portfolio risk management solutions should be a boon for wealth advisors.
There’s a strong connection there. Clients want advisors “to grasp the broader (technology) trends, zero in on what matters most, and take a more holistic approach to help them at every level achieve their long-term financial goals,” said Mitchell H. Caplan, CEO of Jefferson National.
The takeaway here is a compelling one - a better grasp on client investment risk, using data-driven algorithm tools like the Morningstar model, should bolster and safeguard client investment results.
That will pave the way for smoother advisor-client relationships going forward.
Brian O'Connell is a former Wall Street bond trader, and author of the best-selling books, The 401k Millionaire and CNBC's Guide to Creating Wealth. He's a regular contributor to major media business platforms, including CBS News, The Street.com, and Bloomberg. Brian may be contacted at firstname.lastname@example.org.
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