Given the ongoing stock market downdraft since March 2000,
U.S. mutual fund inflows have dramatically slowed down while
hedge fund investing has exploded. As a matter of fact according
to Galbraith and Viviano (2002) of Morgan Stanley, net hedge
fund inflows grew to the same size as net mutual fund inflows
Some have argued that there is an accelerating convergence
between the hedge fund industry and traditional institutional
fund management. This article will argue the opposite: that
in a very fundamental way, these two investment industries
are still quite distinct.
This article will argue that the differences between traditional
investment programs, which are designed around outperforming
benchmarks, and alternative investment programs, which are
designed to deliver absolute returns, mainly result from competing
views on sources of investment returns.
These competing views then result in differing:
- Investment processes;
- Risk management practices; and
- Roles for financial service providers.
This article will also maintain that the differing types
of absolute-return programs result from varying investor preferences
in return-to-risk trade-offs.
One can summarize the competing views on sources of investment
returns as follows:
- Asset allocation is the dominant performance driver;
- Absolute returns should be expected from each investment;
- A hybrid view.
I. ASSET ALLOCATION AND BENCHMARKED-BASED
The view that the dominant source of investment returns is
from asset allocation has resulted in benchmark-based management.
Performance Attribution Studies
Performance attribution studies have historically shown that
the decision by an institutional investor on how to allocate
among stocks, bonds, and cash has been the key performance
This view was originally derived from studies by Brinson,
Hood, and Beebower  and Brinson, Singer, and Beebower
. In the 1986 study, the researchers looked at the returns
of 91 large pension plans and found that on average, 93.6%
of the total variation in actual plan results across time
could be attributed to the asset allocation decision. In contract,
less than 5% of the variation in returns on average was due
to security selection. In the updated 1991 study, the authors
confirmed that the asset allocation decision still explained
more than 90% of the variation in returns.
As a result, the idea that asset allocation is more important
than security selection has been very well ingrained in the
minds and practices of traditional investment practitioners.
For example, as discussed in a Harvard Business School case
study , the main issue for a university endowment to
resolve is what the very long-run policy portfolio
should be. The case study notes that the long-run expected
returns from reasonable asset allocations are expected to
equal the long-run average spending rate, which would then
result in maintaining the real value of the endowment over
very long periods of time.
Under the Capital Asset Pricing Model (CAPM), in equilibrium
all assets and portfolios have the same return after adjusting
for risk. This model was justified for a quarter of a century
by empirical studies.
Given that markets have been thought to be highly efficient,
investors have been wary about the ability of investment managers
to add value beyond a benchmark. Under CAPM, one would expect
that in the main, the only way to earn more returns is to
take on more market risk
Long-Term Structural Returns
The long-term average rate of return of the U.S. equity market
has been very attractive. From 1927 to 2001, the average return
of return for the S&P 500 has been 12.7% per year. With
rates of return of this magnitude, there has been very little
incentive to allow investment managers too much discretion
beyond equity benchmarks.
The investment industry is presently organized around the
idea that asset allocation is the most important investment
decision and that individual managers should be allowed limited
discretion around investment benchmarks.
Pension fund consultants and financial planners advise institutional
and retail clients respectively on the most appropriate long-term
asset allocation mix. These intermediaries assign benchmarks
for each asset class within the overall recommended portfolio.
These consultants also recommend particular funds or managers
to carry out a particular mandate with a specific benchmark.
The chosen funds are then responsible for providing investment
results that are relative to their benchmark.
The asset allocation choice and its benchmark are the investors
responsibility. Importantly, the investor owns the risk
of the benchmarks results.
The investment process is centered around ensuring that any
deviation from the benchmark is an active decision. Further,
the scaling of all active bets should correspond to the degree
of confidence in the bet. Figure 1 illustrates the investment
process for benchmark-based traditional investments.
Risk Measurement and Monitoring
The risks that are monitored in traditional asset management
are style drift, tracking error, and maverick risk.
In the event of style drift, the overall asset allocation
model could be invalidated. This would be very troublesome
since it is this plan, which is regarded as the dominant return
driver, as previously noted.
The structural returns due to the benchmark are sufficient,
so it is not advantageous to allow a manager too much discretion.
Therefore, managers are limited in the amount of tracking
error with respect to the benchmark that they are allowed.
Notably, the total risk of a managers portfolio is
not measured: the managers risk is always viewed
in relative terms.
One consequence of the way that traditional asset management
has evolved is that it can be acceptable for a U.S. mutual
fund to lose over 50% of its market value. This has been defensible
as long as these losses are consistent with the performance
of its benchmark or product category. In 2001, this was the
case for the aggressive equity growth style.
The manager can note that the performance of the fund is
a result of its particular design and also note that it will
continue offering the product. Press articles on the manager
are broadly sympathetic to the fund manager, noting the discipline
it takes for a manager to be faithful to the concept of style
purity through market cycles.
II. ABSOLUTE RETURNS
The post-2000 view is starting to depart from the assumptions
noted above, which has consequences for the investment industrys
organization, investment processes, risk management and monitoring,
and public expectations for managers.
Long-Term View on Structural Returns Has Been Shaken
It may be the case that the long-term average returns in
the U.S. equity market have been above 10% annually since
1927. But there have been long periods of time where one had
to be very patient, as illustrated by Figure 2.
Performance Attribution Studies Questioned
It may be the case that institutional investors have indeed
chosen asset allocation as the key area to exercise investment
discretion. But as argued by Kritzman and Page , it
may be that the natural opportunity set presented by
the capital markets could be far greater than what is
offered through discretion in asset allocation.
The authors simulate thousands of portfolios to determine
the natural dispersion of performance arising from five different
investment activities, including security selection, country
sector allocation, country allocation, global sector allocation,
and asset allocation. Their proposition is that if investors
have skill, the most important investment areas are the ones
that have the greatest dispersion of results from which to
As shown in Figure 3, Kritzman and Page found that skill
in security selection dominated all other investment activities
with skill in asset allocation providing the least benefit,
given the possible investment opportunities in each investment
Downside Risk Protection
Once one no longer has faith in equity benchmarks providing
target returns, downside risk management becomes crucial.
Ineichen  noted that:
Investors are not indifferent [to] whether an
active manager simply captures the premium of the asset
class or whether he or she tilts the return distribution
of the portfolio to the right.
The author notes that long-short equity sector hedge funds
have opportunity sets that are correlated to their respective
sectors, resulting in the active sector funds having returns
that are correlated to their sector indices. But even so,
the hedge funds control their downside risk so that ultimately
their returns compound at a higher rate than their respective
Ineichen contends that long-term superiority in investing
is due to balancing investment opportunities with total risk.
Two illustrations of this point are shown in Figures 4 and
5. Each figure shows that the recovery to peak investment
levels is considerably briefer with an active sector hedge
fund than with its corresponding sector index.
Once one expects absolute returns from each investment, one
correspondingly expects their investment managers to keep
losses under control. Specifically, it is unacceptable
for a manager to lose more than 50% of market value.
Therefore when a hedge fund loses more than 50% of market
value, it closes down and liquidates all of its positions,
as was the case in the fall of 2002 with the fixed income
arbitrage funds operated by Beacon Hill Asset Management.
Event Risk: Individual Managers
Since it is unacceptable for a manager to have large losses,
individual hedge fund managers pay particular attention to
event risks. An example of an event risk analysis
for a total-return portfolio is as follows.
This example portfolio consists of a long Russell 2000 vs.
short S&P 500 futures trade and a long Municipal Bond
vs. short Long Bond futures trade. These trades are normally
unrelated. During a scenario test of the portfolios
sensitivity to event risk, one finds that the combination
of these two strategies results in an exposure to a liquidity
shock, as shown in Figure 6.
The short legs of each spread are the more liquid of the
pair. As a result, both of these trades are at risk to a flight-to-quality
event as happened in the Fall of 1998. The scenario tests
also show that the Fall of 1998 scenario is the worst case.
One response to a concentrated risk to a liquidity shock
has been to purchase out-of-the-money fixed-income calls.
These hedges would cushion the portfolio in the event of another
liquidity crisis, assuming that interest-rate levels start
out at sufficiently high levels.
Event Risk: Funds of Hedge Funds
Similarly, fund of hedge fund managers attempt to model
their portfolios return distribution when all the strategies
are influenced by a dominant event.
An investor frequently uses the normal distribution to represent
returns of a diversified portfolio since one assumes that
it is acceptable to use the Central Limit Theorem. Under this
theorem, as the number of randomly distributed independent
variables becomes large, the distribution of the collections
mean approaches normality.
This would be fine for a portfolios return if its
individual strategies would never be influenced by a dominant
event. But in practice, this does not happen as seen during
the October 1987 stock-market crash, the Fall of 1998 bond
debacle, and during the aftermath of the September 11th, 2001
Johnson et al  recommend addressing this problem
by representing an investments distribution as a combination
of two distributions: one for peaceful times and one for eventful
times. The distribution during eventful times would not just
include higher volatility, but also the greater correlation
among strategies that occurs during crises. A risk manager
would explicitly determine the proportion of crisis returns
in the combined distribution.
Agarwal and Naik  recommend applying the Conditional
Value-at-Risk (CVaR) framework to hedge funds. They advocate
replacing Value-at-Risk (VaR), which has been popular among
traditional asset managers. The authors explain that:
[Whereas] VaR measures the maximum loss for a
given confidence interval,
CVaR corresponds to
the expected loss conditional on the loss being greater
than or equal to the VaR.
If an investors goal is to create portfolios for which
the magnitude of extreme losses is kept under control, then
that investor should consider using CVaR as their risk constraint.
III. HYBRID VIEW
A third view on the sources of investment returns blends
both the asset allocation and absolute-return approaches.
Under this view,
- Markets are largely efficient;
- The average investor must hold the market portfolio;
- Some investors can achieve extra returns by in effect
selling insurance to other investors.
Those institutional investors who are not constrained by
market segmentation issues and liquidity concerns can take
advantage of niche opportunities. But their main source of
returns still derives from their asset allocation decision.
The latest stream of thought by financial economists is that
there are actually multiple sources of risk besides the market
risk factor, which can produce high average returns. If an
investor passively bears any of these risks, that investor
will earn a return, which is not conditioned upon superior
Frequently, there may be large losses from bearing one of
these risk factors, resulting in a short-option-like return
distribution, but the returns over time are sufficient to
make the activity profitable. These returns are called risk
This section will cover the unique investment processes,
performance metrics, risk management, and industry organization
considerations for niche, risk premia investment programs.
Investment Process for Risk Premia Strategies
In risk premia investment programs, an investment manager
must decide how much to leverage the strategy, and whether
to give up any of its returns to hedge out the strategys
When one earns a risk premium, an investor is implicitly
short options and is therefore exposed to asymmetric payoffs.
During portfolio construction, one should use a risk metric
that takes into consideration the potential asymmetry of an
In addition to CVaR, Signer and Favre  propose another
risk measure that takes into consideration the higher moments
of a return distribution, including how asymmetric the distribution
is and the likelihood of extreme observations. The authors
new measure is modified VAR. The authors examine
how the efficient frontier is affected when using modified
VaR rather than VaR as the risk constraint. They find that
the benefits of hedge funds are represented too positively
when not taking into consideration the extra moments of the
investments return distributions.
Due care must be used in relying on the Sharpe ratio as
a performance metric for risk premia strategies.
Four Yale University professors have derived an optimal
strategy for maximizing the Sharpe ratio. The optimal strategy
has a truncated right tail and fat left tail, as shown in
Figure 7. Furthermore, this strategy can be very nearly achieved
by selling certain ratios of calls and puts against a core
equity market holding, as illustrated in Figure 8.
Risk measures tend to solely focus on end-period losses.
But with the ability to leverage, one must also ensure that
investors can tolerate the potential within-period losses.
As a result, Kritzman and Rich  advocate the use of
the risk measure, within-period probability of loss,
which is illustrated in Figure 9.
Under the view that a number of hedge fund strategies are
in fact risk premia strategies, one can make the following
predictions about the hedge fund industry:
- Hedge fund managers with genuine structural niches will
- They will facilitate the ability of some investors to
earn extra returns by in effect selling insurance to others.
- Those managers with trading strategies that have no structural
edge will disappear.
IV. VARYING INVESTOR PREFERENCES
Siegmann and Lucas  note that varying investor preferences
result in different types of absolute-return products. They
argue that the optimal behavior of a loss-averse investor
depends on whether an investor is in a situation of surplus.
For those in surplus, the optimal strategies have long option
profiles (with particular strike prices.) For those who do
not have a surplus, the optimal strategies are income-producing,
short option-like payoffs (again with particular strike prices.)
Siegmann further notes that the optimal strategy also depends
on the available options (or achievable dynamic strategies).
This will determine whether the long call or the straddle
pay-off is optimal in the case of a positive surplus. And
similarly for negative surplus and the short put and short
straddle pay-off. The author also notes that it is a matter
of ongoing research to interpret the properties of dynamic
strategies in terms of specific option strategies.
Income-Producing Short Option-Like Payoffs
Kao  states that:
Institutional investors often use hedge funds
as part of absolute return strategies in pursuing capital
preservation while seeking high single to low double
This kind of return stream has been achieved by arbitrage
Equity Arbitrage Strategies
Agarwal and Naik  found that the payoffs of a number
of arbitrage strategies resemble that from writing a put option
on the market index. In their study, they apply stepwise regressions
on a number of equity hedge fund strategies. They regress
the strategies against a number of style factors and include
options on market indices, too.
For example, the authors find that the following risk factors
are significant in explaining the returns of the Hedge Fund
Research Event Arbitrage index: a short out-of-the-money put
on the S&P 500 along with an equity market capitalization
factor and an equity value-vs.-growth factor.
Long Option Strategies
Anecdotally, the very wealthy clients of European fund-of-funds
do prefer strategies that have a lot of optionality, including
Commodity Trading Advisors (CTAs) and Global Macro.
These funds have at times gravitated to managers who are in
the midst of large drawdowns, believing that with such a large
dispersion of results, there is an increased chance of a large
If Siegmann and Lucas model is correct, though, for
everyone else, the appropriate hedge fund strategies are income-producing,
arbitrage strategies, which are implicitly short options as
discussed by Agarwal and Naik.
The researchers, Fung and Hsieh, have linked the returns
of both the CTA and Global Macro hedge fund style to long
option-like profiles, as discussed below.
Commodity Trading Advisors
Fung and Hsieh propose searching for rule-based strategies
that can be implemented systematically and passively, which
mirror a dynamic trading strategys returns.
They use this approach in modeling the returns of trend-following
Commodity Trading Advisors (CTAs.) In this case, they
find high explanatory power in modeling the return profile
of CTAs as equivalent to look-back straddles on currencies,
commodities, and fixed income. In this way, they are able
to capture the non-linear, option-like return of profile of
CTAs better than buy-and-hold benchmarks.
Fung and Hsieh  suggest focusing on extreme events
to detect non-linear correlations between hedge fund strategies
and risk factors. They provide an example of how a particular
global macro fund behaves like a straddle on the U.S. dollar.
(A straddle is the combination of being long a call option
and long a put option.)
Whether the traditional investment industry and hedge fund
industry converge is a matter of debate. At present these
two industries respond to very different expectations on how
sources of investment returns should be generated, which result
in differing investment processes and risk management. It
is very plausible, though, that the traditional investment
industry will have to incorporate the total-return industrys
emphasis on downside risk protection in the post-bubble market
environment. And within the total-return industry itself,
varying investor preferences will continue to drive the creation
of different types of absolute-return investment products,
which have widely divergent return-to-risk profiles.
This article first appeared in the June 2003 Quantitative
Agarwal, Vikas and Naryan Naik, Risks and Portfolio
Decisions involving Hedge Funds, Review of Financial
Studies, 2003 (forthcoming).
Brinson, Gary, L. Randolph Hood, and Gilbert Beebower, Determinants
of Portfolio Performance, Financial Analysts Journal,
Brinson, Gary, Brian Singer, and Gilbert Beebower, Determinants
of Portfolio Performance II: An Update, Financial Analysts
Journal, May/June 1991.
Fung, William and David Hsieh, Empirical Characteristics
of Dynamic Trading Strategies: The Case of Hedge Funds,
The Review of Financial Studies, Summer 1997.
Fung, William and David Hsieh, The Risk in Hedge Fund
Strategies: Theory and Evidence from Trend Followers,
The Review of Financial Studies, Summer 2001.
Galbraith, Steve and Mary Viviano, Tail or Dog: The
Ponds Getting Crowded, Morgan Stanley, Strategy
and Economics, US Strategy, 30 April 2002.
Goetzmann, William, Jonathan Ingersoll, Matthew Spiegel,
and Ivo Welch, Sharpening Sharpe Ratios, Yale
School of Management, Working Paper, February 2002.
Ineichen, Alexander, Asymmetric Returns and Sector
Specialists, Journal of Alternative Investments, Spring
2003, pp. 31-40.
Johnson, Damien, Nick Macleod, and Chris Thomas, Modeling
the Return Structure of a Fund of Hedge Funds, AIMA
Newsletter, April 2002.
Kao, Duen-Li, Risk Analysis of Hedge Funds versus
Long-Only Portfolios, General Motors Asset Management
Working Paper, 10/01.
Kritzman, Mark, Hidden Risks of Hedge Funds, and Asset
Allocation versus Security Selection, Presentation to
Kritzman, Mark and Sebastien Page, The Hierarchy of
Investment Choice: A Normative Interpretation, Revere
Street Working Paper Series, 8/30/02.
Kritzman, Mark and Don Rich, The Mismeasurement of
Risk, Financial Analysts Journal, May/June 2002, pp.
Kuenzi, David, Strategy Benchmarks: From the Investment
Managers Perspective, Journal of Portfolio Management,
Winter 2003, pp. 46-56.
Mr. Buffett on the Stock Market, Fortune, November
Siegmann, Arjen, and Andre Lucas, Explaining Hedge
Fund Investment Styles By Loss Aversion: A Rational Alternative,
Tinbergen Institute Discussion Paper, May 2002.
Signer, Andreas and Laurent Favre, The Difficulties
of Measuring the Benefits of Hedge Funds, The Journal
of Alternative Investments, Summer 2002, pp. 31-41.
The Harvard Management Company, Harvard Business
School, 9-292-094, Rev.4/8/92.