An Answer To An A4A Member's Question About How Advisor Software Inc's Approach To Goal-Based Planning Is Different From MoneyGuide Pro's
- Created: Thursday, 16 June 2011 22:20
An A4A member asked yesterday about the approach to goal-based financial planning in Advisor Software’s new app. “I still don't get how it is substantially different than, say, planning with MoneyGuidePro.” Here’s the answer.
MoneyGuide Pro is the most popular financial planning listed on A4A. It gets rave reviews. However, the way financial planning problems are solved by software for wealth management professionals is evolving, and Advisor Software’s approach is new, thoughtful, and significantly different from the good work done by MoneyGuide Pro.
As mentioned in a previous post, Advisor Software is run by Andrew Rudd, a Ph.D. whose company, Advisor Software has made apps that are private labeled by Schwab Institutional, TD Ameritrade, and other big players serving independent wealth advisors and used by thousands of advisors. Now, his company is rolling out a new goal-based planning app using a methodology that departs from the other popular goal-based planning software for professionals. It combines the private-labeled apps he’s built over the past decade into a single platform and Rudd is selling the platform directly to advisors, a first for ASI.
Examining Rudd’s different approach to goal-based planning is obviously pretty important to CFPs and other financial advice professionals trying to provide the best solutions to clients’ problems. So I asked Andrew Rudd, the chairman and CEO of Advisor Software, and Neal Ringquist, ASI’s president and chief operating officer, to answer reader Polarbear’s question.
“When one is doing long-term financial planning projections,” commented the A4A member, “our assumptions (i.e., client goals, inflation, asset class returns, standard deviations, tax rates, etc.) at best get swamped by the ever-changing realities of life. Thus, in my mind, long-term financial planning (although very useful) is just a guide or window to one's financial future. Nuances between programs like this may not be that big a deal.”
Rudd and Ringquist's response is below, and A4A members can also see (and get CE credit for) Rudd's presentation at our webinar where he explained his methodology.
As we see it, this question has three interesting and important parts: First, how does our application differ from standard planning applications, such as MoneyGuidePro (MGP); second, what is the value of doing long-term financial planning given the ever changing realities of life; and, third, are nuances between planning programs likely to be a big deal? Below we will answer these three questions in turn.
The key difference between standard planning programs such as MGP and the goal-driven investing approach of the ASI Goal-Based Proposal Solution (GBPS) is how the financial plan influences the investment strategy. As we understand it, the MGP Asset Allocation Plan module leverages a limited number of asset allocation models from vendors such as Ibbotson. Typically the models are assigned to a client’s account based purely on the answers to a risk tolerance questionnaire and resulting risk score. The financial plan does not drive the investment plan, the client’s point-in-time psychological assessment of risk tolerance does.
If the list of asset allocation models happens to include the optimal allocation (presumably the minimum cost allocation that funds the cash flows) then it will be selected in these types of applications only if the risk tolerance questionnaire yields a risk score that ranks it in first place. Of course, there is no reason why the risk tolerance of the investor should be aligned with the risk characteristics of the optimal allocation since tolerance to risk and optimal funding of cash flows are two entirely different constructs. Finally, if the list of models does not include the optimal allocation, which is presumably the most frequent situation, then the client is forced to accept a sub-optimal plan.
In summary, the financial planning software may be a terrifically useful tool and the model portfolios may be wonderfully efficient investment opportunities, but when put together in traditional financial planning software there is little chance of ever finding the most appropriate allocation!
With the ASI approach, the output of the financial plan directly drives the investment plan. The first step in this process, the development of key elements of the financial plan, is determining the affordability of a client’s plan - this is captured in the household balance sheet. Goals, either lump sum or future recurring cash flows, may be expressed with different priority classes. For example, for college funding, securing the cost of a state college may be considered “essential” while funding the cost of a private school may be considered “aspirational”. This differs from the traditional approach that assigns a priority class to an entire goal. The additional planning flexibility allows the advisor to determine the essential level of funding needed across all goals. The balance sheet analysis determines the funding ratio of each priority class across goals, and the ability of the investor to bear risk. Notice at this stage it is the ability to bear risk that is important; while the investor may be able to tolerate a drop of 10% in asset value, it may still be too much risk for the investment portfolio, and a downturn in the markets could force the essential goals to become unfunded.
This naturally leads to the second step in the ASI approach that uses the results to calculate the maximum risk levels in the investment accounts advised by the advisor. The dynamic asset allocation process will determine the appropriate asset allocation for each account based on these maximum risk levels, the cash flow needs and tax frictions of each account (asset location) – something the traditional planning software does not do. In other words, there is a direct link between the client’s financial plan and their investment plan. A risk tolerance assessment is used, but only to create an “upper bound” level of risk for each account – it is not the primary driver of the asset allocation for the household. The ASI approach will also map advisor constructed models to each account’s asset allocation based on the “best fit” of that model to the asset allocation recommendation for the account and show the mis-matches between models.
The end result of the ASI approach is a set of portfolios that are a more effective hedge against the risks that impact client goal achievement – inflation, interest rate, market risk and longevity risk.
Now to the second question listed above. The Captain of American Airlines #177, a flight that I take frequently, when taking off from JFK knows to head approximately west when making for San Francisco. Too much exactitude at the beginning is wasted, since as the flight unfolds the pilot knows that many changes in direction will be necessary. There may be bad weather or other planes to avoid, or strong winds that take the plane off course—these are the realities of flying that swamp the clean precision of setting a single navigation setting. On the other hand, too little exactitude may be dangerous—by going too much in the wrong direction so making too long a flight might cause the plane to run out of fuel.
Identifying the navigation setting on a plane or defining the asset allocation to fully fund retirement expenses are two examples of control variables required to exploit dynamic systems. We all know that the investment world is dynamic, and trying to force static allocations can only be optimal in unusual circumstances. In order to provide appropriate solutions for investors we believe the profession must embrace the dynamic nature of the capital markets. As a result ASI goal-driven applications adjust the investment risk according to the time until a goal is desired, the financial flexibility the investor has, and other characteristics of the household; in other words, we attempt to respond to the changing nature of the investment problem in order to keep on track.
If the investor is a long time from the goal there is less need for precision, but the broad outline of the plan must be visible. For example, consider the retirement planning problem. Many years from anticipated retirement, it is probably more important to save regularly irrespective of the identifying the absolutely best investment vehicle and allocation, than have the correct investment strategy but only save on an irregular basis. Closer to the goal, greater precision is typically required and the cost of inappropriate advice may be the difference between goal attainment and failure.
Finally, are nuances in planning programs likely to be a big deal? Many investors think so, particularly those that lost retirement savings in 2008 and have to work 5 years longer because their financial plan did not accurately translate their low capacity to bear risk into their investment plan. We think it probably depends on the circumstances, but we certainly believe it is better to use the best planning programs, particularly ones that drive the investment plan in a straight-through way. As a result, we strongly endorse goal-driven investment applications that take a holistic approach in a multi-period context over risk based solutions that are narrowly configured and tailored for single period problems. But then we are converts—that’s the reason we built ASI’s suite of wealth management applications.