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.