This document summarizes a survey and analysis of commercial hedge fund strategy classifications conducted by AIMA. It finds that commercial databases provide inconsistent classifications, with around half of hedge funds relying on external sources. Analyzing over 4,500 funds across three major databases found only around 28% agreement on strategy classifications. The document proposes several methodology approaches to improve classification accuracy.

1. Hedge Fund Strategy Classification:
AIMA Survey and Analysis of
Commercial Classifications
Drago Indjic
Fauchier Partners
AIMA Research Day,
20 October 2003, Paris

2. Overview
• AIMA initiative (April 2003)
• AIMA Classification practice survey (June, published in Sept 2003)
• Analysis of commercial databases classification (Aug-Sep 2003)
• Classification methodology proposals
• Acknowledgements: Alexander Ineichen, Francois-Serge L’Habitant,
Lionel Martellini, Narayan Naik, Aasmund Heen
• Standard disclaimer

3. 1
Introduction
• Early 2003: An index family for every commercial data source: too many
indices but a lack of definitions
– Implications: legal, performance attribution etc.
• Ad-hoc committee under the auspices of AIMA called for “Expressions
of interest” in April 2003
– 72 members (Aug 2003)
•
‘Non-commercial’, coordinated long-term research effort leading to the
development of a set of definition “guidelines”

4. 2
Survey: Classification
Source and Limits
No Reply
No
classification
3%
Using
outside
(external)
classification
system
47%
Is s u e s
Using own
(internal)
classification
system
50%
Other issues
Verification difficult
Strategy classification too narrow
Strategy classification too broad
0
5
10
15
Frequency
AIMA HF Strategy Classification Survey: Sample of 36 out of 73 institutions, June 2003.
Source: AIMA Journal, Sep 2003
20

5. 3
Others
14%
Hedgefund.net
9%
“External” Classification
Sources – Commercial
Databases
CSFB/Tremont
27%
MSCI
23%
HFR
27%
Source: AIMA Journal, Sep 2003

6. Classification Source
by User Category
4
100%
75%
50%
25%
0%
Bank (1)
Fund of
funds (5)
Own classification system
Doesn't classify
HF
manager
(16)
Investor (3)
Service
Provider
(10)
Total (35)
External classification provider
Most HF managers and investors rely on commercial classifications.
Source: AIMA Journal, Sep 2003

7. AIMA Committee
Member’s Involvement
Ba
nk
HF
M
fu
nd
s
of
In
ve
st
or
Fu
nd
ce
m
Se
rv
i
An
on
y
pr
ov
id
er
s
18
16
14
12
10
8
6
4
2
0
ou
s
Number of instances
5
Passive
Active
Service providers may dominate “active” membership – commercial pressure.
Source: AIMA Journal, Sep 2003

8. 6
Survey Findings
• Fact: almost 50% of professionals rely on commercial sources
– Some reply on more than one source
• Demand for more specific, verifiable classifications
– True meaning of hedge fund indices, investment guidelines, RFP,
performance attribution …
• What classifications are commercially available?
– No “best” index - unequal risk of different indices for the same
strategy

9. 7
Expectation Management
• 100% classification accuracy is not feasible
– Limited by transparency (IAFE IRC recommendations – even
valuation is problematic) and consistency of manager’s behaviour
– Limited coverage of risk platforms and exchanges - is transparency
welcome? Are new funds investor-friendlier?
• Who should be providing classifications?
– Fund administrators (or risk measurers)?
– How often vendors re-classify strategies?
• Pricing accurate classifications?

10. 8
Strategy
Emerging Markets
Foreign Exchange
Global Emer.
Global Macro
Macro
Managed Futures
Market Timing
Sector
Short Selling
Total Directional
Arbitrage
Eq Market Neutral
Fixed Income
Arbitrage
Market Neutral
Merger Arbitrage
Relative Value Arb
Total Relative Valu
HFR
TASS
129
28
CISDM Strategy
108
99
53
108
89
163
47
137
16
446
89
153
141
20
399
130
147
121
25
298
90
393
67
73
523
367
Direct Count of Hedge
Fund Strategies
393
Note: Data as at September 1st, 2003
Arbitrage
Eq Market Neutral
Fixed Income
Arbitrage
Market Neutral
Merger Arbitrage
Relative Value Arb
Total Relative Value
Global Est.
Equity Hedge
Equity Non-Hedge
Global Intl
Long Only
Long/Short Equity
Total Security Selec
Securities
Event Driven
Total Multi Process
HFR
89
153
141
TASS
CISDM
130
147
90
393
67
73
523
367
393
325
551
85
46
16
636
65
231
296
836
836
387
104
104
153
153

11. 9
Strategy
Hedge Fund
Index
Median
Other
Unclassified
Composite
Total
Fund of Funds
HFR
Direct Count of Hedge
Fund Strategies (2)
TASS
CISDMHedge
2479
2433
1676
102
210
22
20
82
447
101
2682
2725
2165
564
524
445
5928
5974
4775
Event driven and short selling are the only strategy descriptions common to
all three data providers.
Note: Data as at September 1st, 2003

12. 10
Classification Purity Martellini (2003)
Classification methodologies – concern over purity
Index Provider
N ° of Indices
Classification
Methodology
EACM
18
Classified by
EACM
HFR
37
CSFB
14
Zurich
5
Classified by the manager and then checked by the Index
Committee
Classified by
Zurich
Van Hedge
16
Classified by
Hennessee
24
Manager self proclaimed style
Classified by the ma
Van Hedge
nager and then checked by the Index
Committee
HF Net
37
LJH
16
CISDM
19
Manager self proclaimed style
Altvest
14
MSCI
over 160
Manager self proclaimed style
Classified by the manager and then checked by
Committee
S&P
10
Classified by
S&P
Feri
16
Classified by
Feri
Blue X
1
MondoHedge
7
EurekaHedge
3
HFIntelligence
9 InvestHedge + 12
EuroHedg e + 7 AsiaHedge
Bernheim
1
TalentHedge
3
Manager self proclaimed style
Classified by
Classified by
LJH
BlueX
Classified by the manager and then checked by the Index
Committee
Not reported
Not reported
Not reported
Classified by
TalentHedge
the Index

13. 11
Commercial Strategy
Classifications
• How are funds are classified by commercial databases?
– Get a “baseline” classification estimates using HFR, Tass and
CISDM hedge fund databases
– How consistent are the classifications of the same fund?
– Related study: Meriot Jones (Pertrac), Apr 2003, unpublished
• “Noisy” database fund identifiers and strategy classification fields

14. 12
Hedge fund database
Classification analysis
• Fauchier Partners research project
– 3 man-months (G. Thompson, A. Heen, A. Lahiri)
• Not taxonomical analysis of strategy descriptions but collecting
evidence
• Pertrac data format - database cleaning, name matching and counting
• Not database market research:
– Not a comparison of data vendors

15. 13
Approach
• “Top-Down” Strategy Classification Approach
– Map the “narrow” vendor strategies to “broad” strategies (by
convention)
– “Count” classifications and “vote”
– Estimate overall consistency of the broad strategy classifications
and identify conflicts
• Identify “unique” funds in different databases
– Problem: No ISIN, no sector classification
– LP/Ltd, USD/EUR share classes etc causes funds to be identified as
the same when they are not

16. 14
“Top-Down” Strategy
Grouping: A Strategy
Mapping Convention (1)
Directional
Multi Process
Security Selection
Relative Value
Emerging Markets (H*,T*)
Distressed Securities (H*)
Global Est. (C*)
Convertible Arbitrage (H*,T*)
Foreign Exchange (H)
Event Driven (H*,T*,C*)
Equity Hedge (H*)
Eq Market Neutral (H*,T*)
Global Emer. (C*)
Equity Non-Hedge (H*)
Fixed Income (H*)
Global Macro (T*,C*)
Global Intl (C*)
Fixed Income Arbitrage (T*)
Macro (H*)
Long Only (C)
Market Neutral (C*)
Managed Futures (T*)
Long/Short Equity (T*)
Merger Arbitrage (H*)
Market Timing (H*)
Relative Value Arb (H*)
Sector (H*,C*)
Short Selling (H*,T*,C*)
Notes: H = HFR98, T = Tass, C = CISDMHedge. * = index exists. FOF excluded
•
•
Subject to discussion: convention based on compilation of several sources.
Note: Altvest classifies non-exclusively (“tick all that apply”)

17. 15
“Top-Down” Strategy
Grouping: A Strategy
Mapping Convention (2)
Directional
Event Driven
Security Selection
Relative Value
Emerging Markets (H*,T*)
Distressed Securities (H*)
Global Est. (C*)
Convertible Arbitrage (H*,T*)
Foreign Exchange (H)
Event Driven (H*,T*,C*)
Equity Hedge (H*)
Eq Market Neutral (H*,T*)
Global Emer. (C*)
Merger Arbitrage (H*)
Equity Non-Hedge (H*)
Fixed Income Arbitrage (T*)
Global Macro (T*,C*)
Global Intl (C*)
Market Neutral (C*)
Macro (H*)
Long/Short Equity (T*)
Relative Value Arb (H*)
Managed Futures (T*)
Market Timing (H*)
Sector (H*,C*)
Short Selling (H*,T*,C*)
Fixed Income (H*)
Long Only (C)
Notes: H = HFR98, T = Tass, C = CISDMHedge. * = index exists. FOF excluded
•
Following to Naik and Ineichen; large multi-strategy funds should be in separate
group (Inechien)

18. 16
Fund Matching
Heuristics
• Descriptive + numerical criteria : match of fund name (substrings)
and fund return (±%tollerance) on two specific dates
ID
MatchID
Name
Source
Return
Return
Previous
1422
2042 Pioneer Global Macro (PGM) USD
ALTVEST
-1.28%
-0.32%
1414
2042 Pioneer Global Macro (USD)
Tass
-1.28%
-0.32%
4600
2042 Pioneer Global Macro PGM (USD)
HFR98
-1.28%
-0.32%
• Runtime: merged database cleaning for 15,000 funds takes ~1 hour
on PC

19. Automatic HF Universe
Count
17
Tass Tremont
(54%)
28%
12%
4%
10%
25%
HFR
(52%)
5%
16%
CISDMHedge
(35 %)
Total of 6363 funds in 3 major databases (table for >3 databases available), after filtering
duplicate records 4589 “unique” funds (28% less). Includes dead and alive funds for
classification analysis purpose.
Source: Fauchier (August 2003)

20. 18
Strategy Classification
“Matching”
• Following to identification of an unique fund present in 1 or more
databases:
• Cases of classification multiplicity:
– Only 1: trivial, fund present in only one database, no 2nd opinion
on its classification
– 2: fund present in two databases
– 3: fund present three databases
– >3: fund present in three databases
• Algorithm: count modified Pertrac “des” database field descriptors
where “narrow” vendor classification are replaced by “broad”
classifications

21. Case of Two Available
Classifications
19
Fund
Broad
Strategy
Broad Strategy
YYY
Relative Value
YYY
Fund
Directional
XXX
Relative
XXX
Relative
2 “name”
matches
Directional
Multi-Process
Relative Value
Security Selection
Fund of funds
#Agreement
312
132
236
476
468
Nonagreement
156
94
240
272
32
468
226
476
748
500
%
Directional
Multi-Process
Relative Value
Security Selection
Fund of funds
1 strategy
19%
8%
15%
29%
29%
2 strategies
20%
12%
30%
34%
4%
Out of 794 funds classified into different broad strategies there are 156 instances where one of
the “broad” strategies is “Directional”. “Non-agreements” counts instances, while
“agreement” counts instances of unique fund pairs (thus equals 2 x the number of funds).

22. Case of Three Available
Classifications
20
Broad
Strategy
Fund
Broad
Strategy
Fund
Broad
Strategy
Fund
ZZZ
Relative Val.
YYY
Relative Val.
XXX
Relative Val.
ZZZ
Directional
YYY
Sec. Select
XXX
Relative Val.
ZZZ
Sec. Select
YYY
Relative Val.
XXX
Relative Val.
3 “name” Matches
Directional
Multi-Process
Relative Value
Security Selection
Fund of Funds
# Agreement
166
106
253
331
259
2 to 1
198
116
318
438
46
46
6
22
46
27
410
228
593
815
332
Security Selection
Fund of Funds
Non-agreement
Percentages
Directional
Multi-Process
Relative Value
1 strategy
15%
10%
23%
30%
23%
2 strategies
18%
10%
28%
39%
4%
3 strategies
31%
4%
15%
31%
18%
Note: some funds are classified in 3 different “broad” strategies.

23. 21
Further Database
Classification Research
• Estimate size of universe and attrition rates
– quarterly trend analysis of strategy growth
• Marginal utility of additional databases – how many?
• What is behind inconsistencies?
– Identify classification trouble spots
– Estimate misclassification rate and bias
– Induce vendor’s classification rule
• Verify HF index compositions

24. 22
Part 2: Methodology
Requirements
• Threshold transparency level (non-transparent funds cannot be
classified)
• 1: performance estimates (NAV)
• 2: consolidated exposure (sensitivities)
• 3: position level (daily copy of portfolio statement)
• 4: trade level (intra-daily - ideal)
• Accuracy, precision, confidence …
• Econometrics: data (history) requirements, “drift” detection
discriminate styles within strategy, adapt to evolving strategies

25. 23
Current Classification
Methodology Proposals
• Initiate discussion
• Several proposals made by ad-hoc committee:
– Statistical: clustering, PCA
– Structural: risk factors, syntactical
• Further proposals are welcome
– Explanation facility

26. 24
F. –S. L’Habitant (2003)
• Cluster Analysis: the best way to classify hedge funds without bias
– Suggested algorithm: partition around metroids (PAM)
• Center of each style = first principal component of all indices publicly
available for a style (e.g. EDHEC indices)
• Leverage effects should be normalized

27. 25
Related Research
• Brown and Goetzmann (2001) style analysis using clustering
– Does not distinguish between (equally correlated) share classes
with varying leverage
• Gyger and Gibson (2001)
– “Hard” vs “Soft” (fuzzy/probabilistic) classification, robust
distance measures
– Normalise leverage by average strategy variance (or by “gross”
balance sheet exposure?)
• Produces peer-relative measure (“tracking error”)

28. 26
Naik (2003)
• “Asset based style” factor analysis, Fung and Hsieh (2001)
– Linear and nonlinear (option) payoffs
• Standardise taxonomy of strategies
– Managers should self-declare %risk exposure to strategies
• Mutual fund industry – re-classification lessons
– Some 700 managers asking to be reclassified by Morningstar
exhibited better performance under new benchmarks (Goetzmann)

29. 27
Martellini (2003)
• Two problems: right categories + classification method
• Using a manager’s self-proclaimed style is not a good option because of
style biases and style drifts.
– William Sharpe’s insight: “If it acts like a duck, I’ll consider it’s a
duck”
• Perform a rolling-window regression analysis of the fund performance
on a set of indices, and look for patterns
– One should use pure indices perfectly representative of a given pure
strategy
• Many index providers exist but none is entirely reliable
– EDHEC Indices: Portfolio of indices derived using PCA

30. 28
Indjic (2003)
• Verification and validation problem
Issuer
X
X
X
Y
Z
Type
Equity
CB
CDS
Equity
Equity
Sector
A
A
A
B
B
Position
Short
Long
Long
Long
Short
• What does managers’ portfolio holdings say about strategy?
– Strategy reasoning system

31. 29
AIMA Conference
Feedback
• Why not classifying strategies on the basis of VaR?
• Can discretionary traders be ever classified using systematic factors?

32. 30
Future: Methodology
• Guidelines/ endorsement (for investors, FoF, performance attribution)
– Standard definitions
– “Blind classification” competition
– Are you prepared to “override” your classifications?
• Classification “clearing house” / web server
– Consensus building
– Data fusion (statistics, factor analysis)

33. 31
Future: Committee
• Open Forum
– Public dissemination – classification workshop in 2004?
– Consensus is slowly moving: how to facilitate the process?
• Format for constructive dialogue with vendors
– Publish names of inconsistently classified funds and resolve
conflicts?
– Implication for index “products” and benchmarking
• For-profit or not?
– “Open” academic “standard”
– Independency guarantee vs (charitable) funding