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.