People have been trying to perfect the art of portfolio construction for a long time. But as anyone who has ever worked with a real, live client knows, we still have a long way to go in learning how to design portfolios that make clients happy over the long haul. Recently, however, some innovative thinking has been done on this topic by Professor Meir Statman of Santa Clara University and some colleagues who work in the area of behavioral finance. Their work could have a profound effect on how advisors build portfolios for their clients in the future.
To set the context for our discussion of behavioral portfolio theory, let’s look back briefly at the history of portfolio construction. Thousands of years ago the writers of the Talmud said: “Let every man divide his money into three parts, and invest a third in land, a third in business, and a third let him keep in reserve.” In the early 17th century, Don Quixote’s sidekick, Sancho Panza, weighed in on the topic by observing: “It is the part of a wise man to keep himself today for tomorrow and not venture all his eggs in one basket.” These early attempts at asset allocation may be crude by modern standards, but they demonstrate just how long we have been thinking about this issue.
More recently, Harry Markowitz won a Nobel Prize for bringing a more scientific approach to portfolio construction. In 1952, he demonstrated how to build optimally diversified portfolios from any group of asset classes. The Markowitz mean-variance approach to portfolio construction taught us two important lessons: how to combine assets in a portfolio in a way that, at least theoretically, should allow us to generate the highest level of return for any given level of risk we assume; and, perhaps more importantly, we should consider each portfolio as a whole and not focus quite so much on the individual securities that comprise the portfolio. The brilliance of the Markowitz theory is inescapable and the mathematics backing up the theory are compelling. However, putting his theory into practice has been problematic.
Financial advisors utilizing the Markowitz mean-variance approach to portfolio construction often do so in the following way: First, they conduct a “needs assessment” to calculate the amounts their client will need to fund retirement, pay for college education, buy a vacation home, and so forth. Then they calculate the annual return that must be achieved to produce the required amounts. Next the advisor attempts to determine the client’s tolerance for risk to insure that the recommended portfolio does not expose him to more risk than he can endure. Finally, the advisor develops an asset allocation strategy that balances the client’s return needs with their tolerance for risk. This process results in an elegant solution: a single, optimal portfolio that can meet all the client’s financial needs without exposing them to unnecessary risk.
The Human Factor
Professor Statman’s research suggests that while this solution may be elegant from a mathematical perspective, it ignores some very important aspects of human behavior. He points out that it is the rare investor who seeks to limit risk by building a single, well-diversified portfolio using the Markowitz mean-variance approach. On the contrary, he points out in his recent article, “The Diversification Puzzle” (Financial Analysts Journal, July/August 2004), that investors tend to be woefully underdiversified. They also often appear to assume much more risk than the so-called “rational investor” would assume if they were using the traditional mean-variance framework.
To explain this, Statman weaves together a line of research that began over 50 years ago. It started in 1948 when Friedman and Savage made the profound observation that people who buy insurance policies often also buy lottery tickets. In other words, risk-seeking behavior and risk-averse behavior exist together at the same time in the same person.
In 1952, Markowitz wrote two papers. In the first he established the mean-variance framework for portfolio construction that won him the Noble Prize. In the other he noted that people aspire to move up from their current social class. So, for example, an investor with a $100,000 portfolio might accept lottery-like odds in hopes of winning $1 million, while an investor with a $1 million portfolio might accept lottery-like odds in hopes of winning $10 million. Where the desire for upward mobility exists, risk-taking behavior also exists, regardless of the size of the portfolio.
In 1979, Daniel Kahneman and Amos Tversky extended this concept with their research. They found that people are more likely to engage in risk-taking behavior when living below the level to which they aspire. On the other hand, they are less likely to take risks when living at or above the level to which they aspire. That is, people are most inclined to take risks when they are farthest from achieving their dreams and are less likely to take risks as they get closer to achieving their dreams.
This line of research forms the foundation for behavioral portfolio theory as first described by Statman and his colleague, Hersh Shefrin, in an article aptly entitled “Behavioral Portfolio Theory” (Journal of Financial and Quantitative Analysis, June 2000). Statman and Shefrin observed that people act as if they were inhabited by many “doers,” each with a different goal and attitude toward risk. So, for example, a person might have a “downside protection” doer who purchases a life insurance policy and an “upside potential” doer who purchases a lottery ticket.
In more complex versions of the theory, which more accurately mirror real life, investors divide their money into different “mental accounts,” corresponding to their goals, aspirations, and fears. So an investor might have a mental financial framework organized around an “I don’t want to end up poor like Uncle Ned” account; an “I want to retire and live a better lifestyle than my parents did” account; an “I want all my kids to go to Harvard” account; an “I really want a home in Malibu” account; and an “I want more money than my brother” account.
The Practical Ramifications
There are a number of important implications that flow from this theory. The first is that developing portfolios for clients requires more than mathematically calculating their “needs” in the strictest sense. It means understanding their dreams, fears, experiences, preferences, biases, and other emotional drivers, to the extent they are discoverable.
This puts a premium on the ability to quickly establish deep, trust-based relationships with your clients. For only through such relationships will you be able to unlock the information you will need to construct portfolios that will truly satisfy your clients.
Here is a story to illustrate the point. Jim Clark is a hugely successful Silicon Valley entrepreneur. He founded a number of technology companies that produced well over $1 billion of wealth for him and his family. In the strictest sense, he and generations of his family to come have no financial needs, except to preserve what they already have. Yet Clark told The New York Times in 1999: “I just want to have more money than Larry Ellison [CEO of Oracle]. I don’t know why.” So even a very, very wealthy man like Jim Clark has an aspiration that could drive him to take what we might perceive as “unnecessary” portfolio risk.
Another implication of behavioral portfolio theory is that determining a client’s “risk tolerance” is tricky at best. If a client truly has a different attitude toward risk for each of her mental accounts, then trying to identify a single risk tolerance level for a client is probably a futile effort. Each client can be better understood as a bundle of risk tolerances that change over time as her goals and aspirations change and as she gets closer to realizing each one of those goals and aspirations. So a client’s risk profile has multiple dimensions that must be measured on an ongoing basis, not just once at the beginning of a relationship.
Perhaps the most significant implication of behavioral portfolio theory is that it underscores the difference between how clients and their advisors see the world. Clients compartmentalize their money and associate each mental account with a range of fears, concerns, goals, or cherished dreams. Advisors convert the client’s view of the world into a mean-variance portfolio solution based on expected return assumptions, standard deviations, and correlation coefficients. If that is all that happens, advisors may offer up solutions that do not translate well into the client’s mental framework.
Imagine how the clients feel in the following scenario. A couple describes to their advisor their deep-seated fear of running out of money in retirement and becoming dependent on their children in their old age. They have saved quite a bit for retirement and the wife participates in a very generous retirement plan at work, but their fears persist. They go on to share how they would very much like their two children to follow in their footsteps and attend Princeton, even though it is one of the most expensive schools in the country. They have put relatively little aside for this purpose and they know they need to catch up, but they are willing to do whatever it takes to make attendance at Princeton a family tradition. They also share their dream of owning a house someday in the south of France and attending cooking school together in Provence. They have done nothing yet to fund this dream and are looking to their advisor for ideas. What do they get in return? A giant asset allocation pie that consolidates all their assets into a brightly colored optimal combination of 10 asset classes. So much for communication.
The Theory Applied
Behavioral portfolio theory tells us that this couple has different attitudes toward the amount of risk they are willing to take to achieve each of their goals. We also know intuitively that the consequences of not achieving each goal are different. For example, the consequences of running out of money during retirement are more severe than the consequences of not getting that house in the south of France. Yet too often the way we build portfolios for our clients fails to take these factors into account. Instead, we end up homogenizing our client’s fears, concerns, aspirations, and dreams into a single solution that does not line up well with how they see or experience the world.
So how can we use behavioral portfolio theory to better serve our clients? One way is to focus more on the client profiling process and learn what drives each investor’s behavior. On the upside this means determining not only what it will take to achieve their stated goals, like retirement, but also what it will take to turn their dreams into a reality, like getting that house in France. On the downside it means learning about your client’s fears, like becoming dependent on their children in retirement, even if those fears do not appear rational based on the facts.
The next step is to consider building what Statman refers to as “behavioral portfolios.” These differ from mean-variance portfolios. They are constructed like a layered pyramid. The bottom layers are designed for downside protection and the top layers are designed to capture upside potential. Risk aversion gives way to risk seeking as the desire to avoid poverty gives way to the desire for riches. Risk reduction is always a benefit for mean-variance investors. However, behavioral investors prefer low risk in the downside layers of their portfolios, but may actually prefer risk in the upside potential layers. Because of the way they are constructed, behavioral portfolios can bridge the gap between how advisors and clients see the world.
Let’s look at how a behavioral portfolio could be used to help the couple in the example above. As shown in the Getting Practical chart on the previous page, the bottom layer of the behavioral portfolio pyramid is designed to alleviate the couple’s fear about running out of money in retirement. A separate “retirement funding portfolio” is constructed using relatively low-risk investments to supplement the couple’s retirement income. Separate illustrations, using Monte Carlo simulations, could be prepared showing the couple how the retirement funding portfolio provides a high degree of downside protection when combined with Social Security and the income from the wife’s retirement plan.
Next a separate “college funding portfolio” could be created to help the couple realize their dream of sending their children to Princeton. This portfolio is not well funded to start with, but the children are still relatively young. So you recommend a moderately aggressive strategy to help the couple make up ground and generate the needed college funding. They understand there is more risk associated with this portfolio, but are more willing to take more risk here. Their Princeton dream is important and they realize they have to make up for the fact that they haven’t saved much for college up to this point. But they don’t want too much risk because they want to be able to afford a less expensive school even if they fall short of their Princeton dream.
Finally, you create a “south of France house” portfolio. This portfolio is the most aggressive of all. The couple realizes that they have little chance of actually reaching this goal, but they are willing to set aside a little each month on the off chance they can turn their fantasy into reality. In fact, they share with you that it is important to them to fund this account just to keep their dream alive, even if that dream never becomes a reality. You do your best to build a portfolio that gives the couple a remote chance of seeing their French fantasy come to life. You may even include a few lottery tickets. You illustrate to them that the likelihood of success is small, but they are happy, because your solution gives them hope while falling short of their goal will not seriously impact their lifestyle.
The Benefits Gained
By using a behavioral portfolio approach with these clients, you have accomplished a number of important objectives. First, you have translated your investment solution into terms that line up with the clients’ fears, concerns, aspirations, and dreams. Because you have addressed their needs in terms they can relate to, you are more likely to keep them as clients and they are more likely to stick with the program you have designed for them. You have also designed tailored solutions that recognize each client goal while taking into account the multiple risk personalities displayed by these clients toward each goal. In addition, you have incorporated into your solutions recognition that the consequences of reaching or not reaching each client goal are different. A mean-variance solution that seeks to reduce a client to a single risk profile cannot do this.
You did not have to abandon the science of portfolio construction to take into account clients’ behavioral profiles. Each layer of the behavioral pyramid can be constructed by simply incorporating the different return and risk parameters associated with each layer into the mean-variance calculation.
The rules of diversification in behavioral portfolio theory are not as precise as they are in mean variance portfolio theory. There is no optimizer that can balance the need for downside protection with the need for a chance to realize dearly held dreams. Building behavioral portfolios is as much art as science. But by exploring the concept of behavioral portfolio theory developed by Statman and others, you may find a way to provide more customized solutions to your clients that will help them better stick with their investment program and reach long-term investment success.
Scott MacKillop is president of USF Services, a U.S. Fiduciary company based in Sugar Land, Texas, that provides managed account and investment consulting services to financial advisors. He acknowledges the sponsorship of OppenheimerFunds in the preparation of this article.