Hedge Fund Market Neutral
Strategies: Distinguishing Financial
and Operational Risk Factors
Stephen J. Brown
NYU Stern
The Joint 14th Annual PBFEA
and 2006 Annual FeAT Conference
第十四屆亞太財務經濟及會計會議暨
2006台灣財務工程學會聯合研討會
Distinguishing operational and
financial risk
Historical perspective
Operational risk
Characterized by conflicts of interest
Financial risk
The myth of market neutrality
Robust measure of tail risk neutrality
Conclusion
The History of Hedge Funds
 The first hedge fund: Alfred Winslow Jones (1949)
Limited Partnership (exempt from ’40 Act)
Long-short strategy
20% of profit, no fixed fee
Used short positions and leverage
 “Hedge Fund” (Fortune magazine 1966)
 Tiger Fund (Institutional Investor 1986)
 George Soros $3.2Billion raid on the ERM (1992)
 CalPERS (2000)
Institutional concern about risk
Fiduciary guidelines imply concern
for risk
Financial risk
Operational risk
Institutional demand
Growing popularity of market neutral
styles
Explosive growth of funds of funds
Demand for “market neutral” funds of
Fract ion of Funds Surviving
Operational Risk
1
0 .8
0 .6
CTAs
0 .4
Hedge
Fund
0 .2
0
7
15
23
3 1 3 9 4 7 5 5 6 3 71 79
Durat ion (Mont hs)
87
Hedge fund failure is highly
predictable …
Source: Tremont TASS (Europe) Limited
Measuring operational risk
SEC registration requirement (Feb
2006)
2270 of TASS Funds that registered
Had better past performance
Had larger assets under management
15.8% had prior legal/regulatory
problems
Correlates of operational risk
“Problem” Funds
“Non-Problem”
Funds
N
Mea
n
Median
N
Mean
Media
n
Diff
Avg Return
356
0.89
0.80
1898
0.98
0.84
-0.09*
Std Dev
354
2.60
1.79
1897
2.74
2.08
-0.14
Sharpe Ratio
354
0.33
0.29
1897
0.39
0.30
-0.06*
AUM ($mm)
325
218.
2
58.74
1647
180.2
54.00
38.00
Age (Years)
358
5.65
4.50
1912
4.99
3.92
0.66**
Management Fee
(%)
358
1.37
1.25
1912
1.38
1.50
-0.01
Incentive Fee (%)
358
15.2
3
20.00
1912
17.52
20.00
-2.29**
External conflicts
Problem funds
Non problem
funds
With:
N
% Yes
N
% Yes
Broker/Dealer
359
73.8
1912
24.8
Investment Comp
359
50.4
1912
16.0
Investment Advisor
359
74.7
1912
41.3
Commodities
Broker
359
53.5
1912
20.3
Bank
359
40.4
1912
9.8
Insurance
359
39.8
1912
9.4
Sponsor of LLP
359
56.8
1912
22.2
Internal conflicts
Problem funds
With:
N
% Yes
Non problem
funds
N
% Yes
Trade securities with clients
359
30.1
1912
8.4
Allow trading on own account
359
85.2
1912
69.6
Recommend own securities
359
74.9
1912
50.8
In-house broker dealer
359
31.2
1912
2.3
Recommends own underwriting
service
359
69.4
1912
46.8
Recommends commission fee
items
359
22.6
1912
15.7
Recommends brokers
359
45.7
1912
38.4
Use broker provided external
research
359
81.3
1912
69.9
Towards a univariate index of
operational risk
TASS Variables
SEC Variables
Previous Returns
-0.27
In-house broker dealer
0.06
Previous Std. Dev.
-0.36
Associated with broker dealer
0.24
Fund Age
-0.10
Investment company association
0.25
Log of Assets
0.09
Investment advisor association
0.24
Reports Assets
0.07
Commodity trader association
0.44
Incentive Fee
-0.89
Associated with bank or thrift
0.39
Margin
-0.29
Associated with insurance co
0.42
Audited
-0.21
Associated with ltd. partner
syndicator
0.27
Personal Capital
-0.26
Trade securities with clients
0.06
Onshore
-0.11
Allow trading on own account
Open to Inv.
0.04
Recommend own securities
0.32
Recommends own underwriting
service
0.24
Recommends commission fee items
0.28
Accepts Managed Accts
-0.13
-0.12
Recommends brokers
-0.35
Use broker provided external
research
-0.69
Percent of risk
Financial Risk
10 0 %
90 %
80%
70 %
60%
50 %
40 %
30 %
20 %
10 %
0%
Equit ies
S&P50 0 risk
0
10
90
80
70
60
50
40
30
20
10
0
Size of port folio
Source: Elton and Gruber 1995. Risk is measured relative to the standard deviation of the
average stock
Percent of risk
Financial Risk
10 0 %
90 %
80%
70 %
60%
50 %
40 %
30 %
20 %
10 %
0%
Equit ies
Hedge Funds
S&P50 0 risk
Hedge Fund risk
0
10
90
80
70
60
50
40
30
20
10
0
Size of port folio
Caught by the tail
“S&P500 returns at Treasury Bill risk”
Most new funds claim to be “market
neutral”
Zero correlation with benchmark
Zero correlation is not a strategy
Zero correlation is an outcome of a
strategy
These strategies fail in liquidity crises
Risk is considerably understated
New concept: “tail risk neutrality”
A market neutral strategy
Data
TASS hedge funds – both dead and
alive
US funds with at least 10 returns,
average of 40 max of 120.
Not a lot of data per fund, but plenty
when the universe is combined –
nearly 50,000 fund-month
observations.
Fund Returns
An example of ‘market neutrality’
0.8
1.5%
0.6
1.1%
0.4
0.8%
0.2
0.4%
0.2
Assuming MVN
0.4 0.6 0.8
Market Returns
Beta = .28, rho = .24
Fund Returns
Market neutrality in the ‘real world’
0.8
2.5%
0.6
1.9%
0.4
1.3%
0.2
0.6%
0.2
0.4 0.6 0.8
Using TASS data S&P500 Returns Beta = .28, rho = .24
Fund Returns
Market neutrality in the ‘real world’
0.8
2.5%
0.6
1.9%
0.4
1.3%
0.2
0.6%
0.2
0.4 0.6 0.8
S&P500 Returns
Beta = .28, rho = .24
Fund Returns
Long Short Equity Funds
0.8
2.9%
0.6
2.2%
0.4
1.3%
0.2
0.6%
0.2
0.4 0.6 0.8
S&P500 Returns
Beta = .50, rho = .37
Fund Returns
Event driven style
0.8
3.1%
0.6
2.3%
0.4
1.5%
0.2
0.8%
0.2
0.4 0.6 0.8
S&P500 Returns
Beta = .20, rho = .23
Fund Returns
Dedicated Short Sellers
0.8
4.5%
0.6
3.4%
0.4
2.3%
0.2
1.1%
0.2
0.4 0.6 0.8
S&P500 Returns
Beta = -.91, rho = -.61
Fund Returns
Fixed income arbitrage
0.8
1.5%
0.6
1.1%
0.4
0.8%
0.2
0.4%
0.2
0.4 0.6 0.8
S&P500 Returns
Beta = 0.01, rho = 0.02
Funds of Hedge Funds
Fund of Funds
Hedge Fund 1
Hedge Fund 2
Hedge Fund 3
Funds of Hedge Funds
Fund of Funds
Hedge Fund 1
Provides
Hedge Fund 2
Hedge Fund 3
Funds of Hedge Funds
Fund of Funds
Hedge Fund 1
Hedge Fund 2
Hedge Fund 3
Provides
Diversification – lower value at risk
Funds of Hedge Funds
Fund of Funds
Hedge Fund 1
Hedge Fund 2
Hedge Fund 3
Provides
Diversification – lower value at risk
Smaller unit size of investment
Funds of Hedge Funds
Fund of Funds
Hedge Fund 1
Hedge Fund 2
Hedge Fund 3
Provides
Diversification – lower value at risk
Smaller unit size of investment
Professional management / Due diligence
Funds of Hedge Funds
Fund of Funds
Hedge Fund 1
Hedge Fund 2
Hedge Fund 3
Provides
Diversification – lower value at risk
Smaller unit size of investment
Professional management / Due diligence
Access to otherwise closed funds
Institutions love FoF
Spectacular growth of Funds of Funds
2000:
2003:
2005:
15% of all Hedge funds were FoF
18% of all Hedge funds were FoF
27% of all Hedge funds were FoF
Institutional attraction of Funds of Funds
Risk management
 Due diligence

Fund Returns
Funds of Funds
0.8
2.9%
0.6
2.2%
0.4
1.3%
0.2
0.6%
0.2
0.4 0.6 0.8
S&P500 Returns
Beta = .14, rho = .22
Fund Returns
Relationship to LIBOR
0.8
1.0%
0.6
0.8%
0.4
0.5%
0.2
0.3%
0.2
0.4 0.6 0.8
LIBOR return
Beta = 0.0, rho = 0.0
Fund Returns
Fixed income arbitrage
0.8
2.0%
0.6
1.5%
0.4
1.0%
0.2
0.5%
0.2
0.4 0.6 0.8
LIBOR return
Beta = -.02, rho = -.05
Simple measures of tail risk
exposure
Frequency of falling into lower decile for
both fund and benchmark
0.045
0.04
0.035
Probability
0.03
0.025
MVN
MVt (3 df)
0.02
0.015
0.01
0.005
-0.5
-0.3
0
-0.1
rho
0.1
0.3
0.5
Independence an
unrealistic
benchmark
Consider
MV Normal with the
same sample
correlation
MV Student with 3
df
Simple measures of tail risk
exposure
Frequency of falling into lower decile for
both fund and benchmark
0.045
0.04
0.035
Probability
0.03
0.025
0.0188
MVN
MVt (3 df)
0.02
0.015
0.01
0.005
-0.5
-0.3
0
-0.1
rho
0.1
0.3
0.24
0.5
Independence an
unrealistic
benchmark
Consider
MV Normal with the
same sample
correlation
MV Student with 3
df
Fund Returns
An example of ‘market neutrality’
0.8
1.5%
0.6
1.1%
0.4
0.8%
0.2
0.4%
0.2
Assuming MVN
0.4 0.6 0.8
Market Returns
Beta = .28, rho = .24
Fund Returns
An example of ‘market neutrality’
0.8
1.5%
0.6
1.1%
LW
WW
0.4
0.8%
0.2
0.4%
LL
WL
0.2
0.4
0.6
0.8
LL should be 1.88% of
Market Returns
sample assuming MVN
Beta = .28, rho = .24
Comparison with S&P500
Benchmark
Correlation
with
benchmark
Binomial Crash
p-value (ind)
p-value (N)
p-value (t)
All Funds
0.28**
0
0
0
Funds of Funds
0.14**
0
0
0
Convertible
Arbitrage
0.09**
0
0.033
0.840
Dedicated Short
Bias
-0.91**
0.997
0.112
0.838
Emerging Markets
0.66**
0
0.031
0.394
Equity Market
Neutral
0.02
0.001
0.006
0.893
Event Driven
0.20**
0
0
0
Fixed Income
Arbitrage
0.01
0.395
0.480
0.995
Global Macro
0.08
0.004
0.034
0.752
Comparison with LIBOR
Benchmark
Correlation
with
benchmark
Binomial Crash
p-value (ind)
p-value (N)
p-value (t)
All Funds
0.00
1
1
1
Funds of Funds
0.01
1
1
1
Convertible
Arbitrage
0.00
0
0
0.074
Dedicated Short
Bias
0.07
0.006
0.031
0.432
Emerging Markets
-0.17**
0.995
0.823
1
Equity Market
Neutral
0.07**
0.148
0.567
1
Event Driven
-0.04**
1
1
1
Fixed Income
Arbitrage
-0.05
0
0
0.007
Global Macro
-0.03
0.849
0.756
0.999
Logit Specification
Boyson, Stahel and Stulz [2006] suggest
running logit regressions of whether a
fund index crashes in a month upon the
market return and a dummy for market
crashes. A positive coefficient on the
dummy indicates additional dependence
during crashes.
Lacks power when run on a single index.
We run the regressions on the cross-
Conclusions
Operational risk
Important role for due diligence
Characterized by internal and external
conflicts of interest
Financial risk
Undiversifiable crash risk lurks in
hedge fund returns, despite their
seemingly light dependence in normal
times.