John Ferry
10/01/2005
Leo Thomas Schrutt has spent decades contemplating investment markets. But in the past several years, as private investors have embraced hedge funds and other illiquid assets, the head of group investment research at Julius Baer in Zurich has had cause to ponder not just the markets themselves, but also on how investors view them. He is not happy. “I go to these conferences and hear hedge funds treated as an asset class,” he says. “But this is a major mistake.”
| TOP VIEW Illiquid assets such as hedge funds, private equity and real estate are difficult to analyze alongside traditional asset classes such as traded stocks and bonds, making it hard for an investor to decide how to best allocate capital to the various investments. In fact, wealth advisors trying to apply the standard tool of portfolio construction—called modern portfolio theory—to these unwieldy assets often find that it leads to incorrect and occasionally absurd outcomes. To address this difficulty, experts are trying to develop academically sound frameworks to help wealth advisors determine how to allocate their clients’ capital among liquid and illiquid investments in order to have the best chance of attaining their goals. Though some claim to have made progress in this endeavor, the fact remains that for now, these efforts are as much art as science. |
This is one of the major difficulties investment advisors face when trying to integrate illiquid assets into an investment portfolio. Products such as hedge funds, private equity holdings and even real estate are so iconoclastic that it is very difficult, if not impossible, to find measurable characteristics that can be used to analyze them in the context of a broader portfolio. Even so, a number of wealth advisors have attempted to tackle this issue and develop strategies for analyzing these assets in a meaningful way.
“We stepped forward a few years ago to develop a process that would place these assets in a rational and academically sound framework,” says Larry Tekler, Los Angeles–based managing director with Citigroup Private Bank. Tekler is referring to Citi’s Whole Net Worth Asset Allocation program, which uses the latest quantitative techniques to analyze both liquid and illiquid investments.
Accounting for illiquid assets within a portfolio is crucial to appropriate portfolio decision making, but it is by no means easy. Despite this, private investors have pursued illiquid asset classes with gusto, either to boost returns or to reduce their portfolio risk through diversification. “Typically, clients will be looking for somewhere between 4 to 6 percent added returns to compensate for the lack of liquidity,” says Doug Wurth, managing director in charge of the investment solutions group for clients in Europe, the Middle East and Africa at JPMorgan Private Bank in London.
Quantifiable Mechanics
To better understand the challenges involved in
achieving such returns on illiquid assets, consider modern portfolio theory
(MPT), the standard framework for optimizing a portfolio consisting of
traditional, liquid investment classes—equities, bonds and cash. Developed by
academics in the 1950s, this framework requires several types of data—a measure
for the volatility of each asset, the expected returns of the assets and their
correlations. These are entered into the MPT model, which calculates what
percentage of a portfolio should be invested in each asset class in order to
maximize its risk-adjusted returns.
Modern portfolio theory relies on a
number of assumptions that do not necessarily hold with illiquid assets, namely
that sound historical data on returns is available, that liquidity is roughly
the same across the asset classes and that the risk characteristics of the
assets are similar and well understood. “It assumes that returns and their
correlations are observable, and, more importantly, that they are ‘normal,’ but
that is not necessarily the case with illiquid assets,” explains Mary Duke, New
York-based head of wealth advisory services for the Americas at HSBC Private
Bank. By normal, Duke means that the probabilities of an asset making or losing
money, when plotted on a graph, are distributed around the mean and form a
so-called normal distribution (commonly called a bell curve). The normal
distribution allows an analyst to use the full power of statistical modeling on
an asset. But if the asset’s behavior is not normal—and few assets really
are—statistical model results can be profoundly wrong.
Nearly all the
factors MPT needs are absent in illiquid investments. Take hedge funds, for
example, which have only emerged in relatively recent times as important
investment vehicles. Unlike equities, which have been traded on established
markets for more than a century, reliable recorded data on hedge fund
performance is nearly impossible to find. In addition, hedge fund investments
are far less liquid than equity investments because they typically lock
investors in for a period of time. Similarly, it is impossible to derive risk,
return and correlation figures to private equity or private real estate in a
truly accurate way.
Experts who have attempted to apply MPT to illiquid
assets have found that if this process is done naively, it can lead to
outrageous assumptions. “The underlying asset would appear less risky than it is
in reality,” says Geneva-based Jacques Roulet, head of quantitative analysis and
risk management at ABN Amro Private Clients.
“There’s definitely an art to
this,” Wurth adds. “If you let the models run by themselves, then they may come
out with very impractical results, such as 100 percent of the portfolio should
be invested in hedge funds, because from a historical perspective they seem to
have attractive risk and return profiles. Or, because of liquidity issues the
model could come out with a zero allocation to alternatives, depending on how
much of a discount factor you put in for illiquidity.”
Art and Science
Because illiquid assets are now so important to private
investors, leading financial institutions are working hard to devise methods for
integrating assets within a traditional portfolio. Citigroup Private Bank’s
experts worked in conjunction with finance professor Rui de Figueiredo from the
University of California, Berkeley, Haas School of Business to come up with a
framework that they say greatly improves the asset allocation process. Citi’s
analysts attempt to improve the reliability of the data they use by, for
example, “unsmoothing” returns data. They do this by using their own complex
financial models to create an optimized portfolio, but one that is optimal not
just in terms of risk and return (and therefore diversification), but also in
terms of certain idiosyncratic characteristics, or biases, typically displayed
by illiquid assets.
Dealing with non-normally distributed asset behavior is
another challenge. The return probabilities are often biased on the positive or
negative side. Financial analysts refer to these asymmetric distributions as
showing “skewness.” Illiquid assets also often display kurtosis, which means
that the probability of realizing some extreme return (either positive or
negative) is greater than it would be for normally distributed assets. “When we
optimize, we look at the trade off between volatility, returns, skew and
kurtosis,” Tekler says. “So if you have an asset class with a lot of negative
skew, let’s say, then you may want to add an asset class that can help offset
that with positive skew.”
Taking account of these additional factors
associated with illiquid assets can lead to some unconventional recommendations,
as Tekler points out. “Take real estate,” he says. “Many investors would look at
a real estate portfolio and say they want to offset it with bonds, because bonds
are pretty safe and liquid. But actually bonds are susceptible to some of the
same economic factors as real estate interest rates. We would look at that and
say real estate has negative skew, and so a good offset for that would be
something with positive skew, and private equity has positive skew.”
These
days, this level of detailed technical analysis is where many advisors are
heading. “If you think about it, people used to optimize based on risk and
return. Now we are optimizing based on risk and return and, on another plane,
tolerance for illiquidity,” Tekler adds.
It should be remembered, however,
that these models are by no means perfect; assumptions still have to be made,
and proxies are used to fill in data gaps. As JPMorgan’s Wurth warns, “There are
a lot of dynamics that happen that a model can’t really account for.”
Ultimately, qualitative, as well as quantitative, judgments must still
figure into the equation. Different wealth advisors will rely more on one than
the other. No one yet knows what the correct balance should be, and ultimately
the uncertainties involved could make any quantitative approach flawed. However,
any competent wealth advisor should at least understand the problems associated
with integrating illiquid assets into a portfolio and, at the minimum, should
set a cap on exposure to alternatives. As time goes on, the models will improve,
but for now, investors should remember that the asset allocation process is as
much an art as science.
John Ferry is an Edinburgh, UK–based journalist who specializes in writing about financial markets and investments.
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