Think of the world of financial services as a large, somewhat dysfunctional family with competing forces typified by the two eldest brothers in the family. The best seat at the familial dinner table is reserved for the brother who brings in the most revenue. The way he generates that revenue is less important than the end result. This brother has also been given the “parental blessing” of top management. Each brother has his own unique set of skills. One has such a keen ability to spin a good yarn he could sell bottles of Manhattan air on the street corner. The other possesses an acute aptitude for analyzing data. In some branches of the financial services extended family, the combination of the two is found to reside in one individual, but usually not.
Both have been taught the most effective catch phrases based on the finest psychological research available. These phrases are designed to entice the customer such as: “well diversified portfolio” and “the client comes first.” Even though these phrases often contain more style than substance, the favored brother eagerly accepts these appealing axioms and rushes to try them out on his next customer. But the other brother has some reservations. He asks questions such as, “How do we know if the portfolio is really well-diversified? If we have 10 investments in the portfolio, is it diversified? Or do we need 15? How do the specific types of investments in the portfolio affect true diversification?” In other words, brother number two wonders what assurance he can offer the client that his portfolio is as well diversified as claimed.
Sure, we can use mean-variance optimization, the clever brother admits, but without constraints on the investment categories, we’ll get a rather unusual asset allocation. Besides, by using constraints, are we not subverting the very purpose of optimization?
Under such a scenario, the other brother decides to leave the secure confines of his family and becomes an independent advisor.
This parable is meant to make a point about what we learned during the apocalyptic 15 months or so from 2008 to March 2009. We learned that whatever an industry could do to erode the trust of the consumer, we did. Not everyone, but the number of people acting in their own self-interest was large enough to cause damage rivaling that of Hurricane Katrina.
Now we must rebuild that foundation called trust. But what will we do differently? What have we truly learned from this debacle? I suspect the management of those dysfunctional familial behemoths will decide to change little, despite some rather loud comments to the contrary, for they have found a business model that makes money, at least for the shareholders and employees. That leaves it to us, independent advisors, to shoulder the responsibility and rebuild trust by changing the foundation of trust. We must embrace our own Hippocratic Oath, not by putting the customer first, but putting the interests of the customer first. Moreover, we must educate consumers since they are the final arbiter of our industry’s future path. If they continue to accept the same old sales pitches of the past, then that’s exactly what they’ll get.
It’s time to find a new perspective. With that thought in mind, I’d like to share with you, what I believe to be a completely different approach in the way we advise clients. While I cannot claim to be the creator or even the only practitioner of this approach, I can say that the vast majority of advisors do not practice this way. Before we get into a discussion of this new approach, let’s look at some issues which are foundational to the discussion.
One reminder provided by the financial crisis is that risk matters. When you think about it, it all comes down to risk, which can be defined as the possibility of an undesirable outcome. But how much does risk matter and how will we manage it? What process will we use to assure clients they will not have to endure losses of the magnitude they nearly all suffered during the crisis? How much does risk really matter? The “Making It Up” chart (top right) illustrates that while losses increase on a linear basis (in this sample, by increments of 5%), the return needed to break even increases exponentially. For example, a 10% loss would only require an 11% gain to return to break even. A loss of 50% would require a 100% gain and losing 80% would require a 400% gain just to recoup the loss. Moreover, even though past market returns hold no predictive value, past risk does. We should be very focused on the risk contained in the client’s portfolio.
Just as the horse and buggy gave way to the automobile, linear projections set the stage for more advanced forms of projecting the future. Today Monte Carlo simulation (MCS) is frequently touted as the supreme forecasting methodology, even by those who do not fully understand it. However, even MCS has its limitations. Although possible, most MCS software packages don’t model outliers well. Also, depending on the distribution curve chosen, the correlations between assumptions and other relevant factors can vary significantly from one MCS application to another. However, even with its shortcomings, it is a much better tool than the linear forecast. If used correctly, it may be the best analytical tool we have at our disposal today.
With the backdrop of risk as the focus, I’d like to begin explaining the method I referenced earlier. First, I propose that our role with clients is primarily to be their risk manager. For example, we need to ask: What’s the client’s risk of running out of money? What is the client’s risk of losing too much wealth to the Federal estate tax? What’s the client’s risk of having to retire later than expected or being forced to reduce her standard of living in retirement? What’s the risk to a client’s survivors if the client should meet an untimely death? Therefore, the goal must be to properly identify and manage risk, to increase the client’s probability of achieving her goals. If we can help our clients achieve this, we will have done our job.
But it’s not always so easy to do so. Often, we must deal with resistance arising from our clients’ past experiences. For example, they may have been sold a product which was within the realm of suitability at the time, but isn’t suitable today. What’s worse, many clients realize this and have become more suspicious of financial advisors. Therefore, we must consider how past events have influenced our clients’ thinking and how our risk analysis and probability forecasting will bring clarity to their situation.
Part One: The Required Return
Let’s say you have a client who has accumulated a healthy sum of money and would like to know if he will be able to maintain his desired lifestyle for as long as he lives. As an advisor, we would ascertain the amount of any future liquidity events, his current income sources and budgetary requirements, the date he would like to retire, and his general tolerance for risk. Armed with this information, we would construct a portfolio that would enable him to fully realize his goals.
The next step would be to determine the return needed, on a linear basis, to realize those goals, then design a portfolio that would offer a reasonable chance of achieving this return. Without getting into a debate on accepted capital market assumptions, clearly a higher required return will require a higher level of risk. Moreover, the higher the risk, the less predictable the return.
Referring to the “Higher Return, Lower Risk” chart (page 54, middle) let’s assume our client has a required return of 8.0% at the beginning of year one. Over the next 12 months the markets are kind to us and we earn 12.0%. Because we’ve earned more than was required, our required return falls, let’s say to 7.0%. During the subsequent 12 months we squeak out a 7.5% return, slightly above the new target of 7.0%, and the required return falls to 6.75%. Each time we exceed the required return it declines. As it declines we can also reduce the risk in the portfolio which will reduce its variance, thereby increasing the probability of goal achievement . In short, this new paradigm in advising would be to drive down the required return, and hence, the risk in the portfolio. However, to do this, we must carefully monitor the clients situation on a regular basis. By monitoring, I would suggest a comprehensive financial plan, updated on an annual basis.
Part Two: How Much Capital?
Here’s another unique aspect to this advising process that I added recently. How much capital does the client need today to enable him to retire and not run out of money? While every situation will differ, there is a general rule of thumb which may be applied. That is, as we grow older, our life expectancy is shortened and our capital need declines. For example, if a client needed $2 million as a lump sum at age 50 to maintain a certain income stream, then at age 75 that same client should need a smaller amount since she is closer to her life expectancy. Although this is theoretically true, inflation places an upward pressure on this need, and as I said, every situation is different. To illustrate this, refer to the “Defining Need” chart (p. 54, bottom). The Total Need at any point is calculated as the net present value (NPV) of all future annual cash shortfalls, discounted by the rate of inflation. For example, if 95 years is the assumed age at death, and the current age is 60, then there would be 34 years to discount (95-61). A year later, the number of years would be reduced by one and the NPV of the shortfalls would be adjusted accordingly.
Notice how the need begins to decline around the 13th year in this particular case. Additionally, as the financial assets grow there is a point at which the two lines intersect. This “crossover point” is the point where there are sufficient financial assets to sustain the client. However, this is based on a linear analysis. Hence, Monte Carlo simulation (MCS) should be employed to assess the probability of reaching this crossover point.
The same type of analysis can be used to determine the amount of life insurance a client needs to provide for survivors. Use the same data as in the “Defining Need” chart, but add the amount of life insurance on the client’s life for each year in the future. That should account for all life insurance policies, including term policies which will be discontinued due to unreasonable premiums in the future. In this case, a gap would exist until year 12. Therefore, it may be prudent to purchase a 10- or 15-year term policy to cover the expected shortfall until such time as the clients reaches their crossover point.
Only You Can Do It
These are only a few examples of why I believe the independent advisor has such a distinct advantage. While in most large firms the expectation is to produce revenue, period, among independent advisors, we can look at the client’s needs holistically and build solutions that meet those needs, regardless of whether we are selling a specific product to meet those needs. Culturally, many companies are geared more like a sales organization masquerading as a distributor of advice. Although they may have the financial resources to create and implement the very finest analytical tools, this money is often allocated more to advertising and compensation. Moreover, these large firms will probably never enter, or ever define “Comprehensive Wealth Management” because they will usually not advise in areas in which they cannot make money. Personally, I’m not aware of any company that has effectively executed the all-in-one business model, and I don’t believe shareholders would wait patiently while they figure it out. Therefore, the independent advisor has a real opportunity and, I would argue, a moral obligation to fill the void.
Michael J. Patton is an independent advisor based in Baton Rouge, Louisiana. He can be reached
at firstname.lastname@example.org or via his blog, The Road to Independence.