Hedge Fund Strategy Classification: AIMA Survey and Analysis of Commercial Classifications

Drago Indjic

Fauchier Partners

AIMA Research Day, 20 October 2003, Paris

• 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

Overview

• 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 “Expressionsof 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”

1 Introduction

Using outside

(external) classification

system47%

No classification

3%

Using own (internal)

classification system

50%

0 5 10 15 20

Strategy classification too broad

Strategy classification too narrow

Verification difficult

Other issues

No Reply

Issu

es

Frequency

AIMA HF Strategy Classification Survey: Sample of 36 out of 73 institutions, June 2003. Source: AIMA Journal, Sep 2003

2 Survey: Classification Source and Limits

HFR27%

MSCI23%

Hedgefund.net9%

Others14%

CSFB/Tremont27%

“External” Classification Sources – Commercial Databases

Source: AIMA Journal, Sep 2003

3

Classification Source by User Category

Most HF managers and investors rely on commercial classifications. Source: AIMA Journal, Sep 2003

0%

25%

50%

75%

100%

Bank (1) Fund offunds (5)

HFmanager

(16)

Investor (3) ServiceProvider

(10)

Total (35)

Own classification system External classification provider

Doesn't classify

4

AIMA Committee Member’s Involvement

Service providers may dominate “active” membership – commercial pressure. Source: AIMA Journal, Sep 2003

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Passive Active

5

• 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

6 Survey Findings

• 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?

7 Expectation Management

8 Direct Count of Hedge Fund Strategies

Strategy HFR TASS CISDM Strategy HFR TASS CISDM

Emerging Markets 129 108 Arbitrage 89 130

Foreign Exchange 28 Eq Market Neutral 153 147

Global Emer. 99 Fixed Income 141

Global Macro 108 53 Arbitrage 90

Macro 89 Market Neutral 393

Managed Futures 163 Merger Arbitrage 67

Market Timing 47 Relative Value Arb 73

Sector 137 121 Total Relative Value 523 367 393Short Selling 16 20 25 Global Est. 325

Total Directional 446 399 298 Equity Hedge 551

Arbitrage 89 130 Equity Non-Hedge 85

Eq Market Neutral 153 147 Global Intl 46

Fixed Income 141 Long Only 16

Arbitrage 90 Long/Short Equity 836

Market Neutral 393 Total Security Selec 636 836 387Merger Arbitrage 67 Securities 65

Relative Value Arb 73 Event Driven 231 104 153

Total Relative Valu 523 367 393 Total Multi Process 296 104 153

Note: Data as at September 1st, 2003

9 Direct Count of Hedge Fund Strategies (2)

Event driven and short selling are the only strategy descriptions common to all three data providers.Note: Data as at September 1st, 2003

Strategy HFR TASS CISDMHedgeHedge Fund 2479 2433 1676Index 102 210 22Median 20Other 82Unclassified 447Composite 101Total 2682 2725 2165Fund of Funds 564 524 445

5928 5974 4775

Classification methodologies – concern over purity

Classification Purity -Martellini (2003)

Index Provider N ° of Indices Classification Methodology

EACM 18 Classified by EACM

HFR 37 Manager self proclaimed style

CSFB 14Classified by the manager and then checked by the Index

Committee

Zurich 5 Classified by Zurich

Van Hedge 16 Classified by Van Hedge

Hennessee 24Classified by the ma nager and then checked by the Index

Committee

HF Net 37 Manager self proclaimed style

LJH 16 Classified by LJH

CISDM 19 Manager self proclaimed style

Altvest 14 Manager self proclaimed style

MSCI over 160Classified by the manager and then checked by the Index

Committee

S&P 10 Classified by S&P

Feri 16 Classified by Feri

Blue X 1 Classified by BlueX

MondoHedge 7Classified by the manager and then checked by the Index

Committee

EurekaHedge 3 Not reported

HFIntelligence9 InvestHedge + 12

EuroHedg e + 7 AsiaHedgeNot reported

Bernheim 1 Not reported

TalentHedge 3 Classified by TalentHedge

10

• 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

11 Commercial Strategy Classifications

• Fauchier Partners research project– 3 man-months (G. Thompson, A. Heen, A. Lahiri)

• Not taxonomical analysis of strategy descriptions but collectingevidence

• Pertrac data format - database cleaning, name matching and counting

• Not database market research: – Not a comparison of data vendors

12 Hedge fund database Classification analysis

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

Short Selling (H*,T*,C*)

Sector (H*,C*)

Relative Value Arb (H*)Market Timing (H*)

Merger Arbitrage (H*)Long/Short Equity (T*)Managed Futures (T*)

Market Neutral (C*)Long Only (C)Macro (H*)

Fixed Income Arbitrage (T*)Global Intl (C*)Global Macro (T*,C*)

Fixed Income (H*)Equity Non-Hedge (H*)Global Emer. (C*)

Eq Market Neutral (H*,T*)Equity Hedge (H*)Event Driven (H*,T*,C*)Foreign Exchange (H)

Convertible Arbitrage (H*,T*)Global Est. (C*)Distressed Securities (H*)Emerging Markets (H*,T*)

Relative ValueSecurity SelectionMulti ProcessDirectional

• Subject to discussion: convention based on compilation of several sources.

• Note: Altvest classifies non-exclusively (“tick all that apply”)

Notes: H = HFR98, T = Tass, C = CISDMHedge. * = index exists. FOF excluded

“Top-Down” Strategy Grouping: A Strategy Mapping Convention (1)

14

“Top-Down” Strategy Grouping: A Strategy Mapping Convention (2)

Long Only (C)

Fixed Income (H*)

Short Selling (H*,T*,C*)

Sector (H*,C*)

Market Timing (H*)

Managed Futures (T*)

Relative Value Arb (H*)Long/Short Equity (T*)Macro (H*)

Market Neutral (C*)Global Intl (C*)Global Macro (T*,C*)

Fixed Income Arbitrage (T*)Equity Non-Hedge (H*)Merger Arbitrage (H*)Global Emer. (C*)

Eq Market Neutral (H*,T*)Equity Hedge (H*)Event Driven (H*,T*,C*)Foreign Exchange (H)

Convertible Arbitrage (H*,T*)Global Est. (C*)Distressed Securities (H*)Emerging Markets (H*,T*)

Relative ValueSecurity SelectionEvent DrivenDirectional

• Following to Naik and Ineichen; large multi-strategy funds should be in separate group (Inechien)

Notes: H = HFR98, T = Tass, C = CISDMHedge. * = index exists. FOF excluded

15

Fund Matching Heuristics

• Descriptive + numerical criteria : match of fund name (substrings) and fund return (±%tollerance) on two specific dates

• Runtime: merged database cleaning for 15,000 funds takes ~1 houron PC

-0.32%-1.28%HFR98Pioneer Global Macro PGM (USD)20424600

-0.32%-1.28%TassPioneer Global Macro (USD)20421414

-0.32%-1.28%ALTVESTPioneer Global Macro (PGM) USD20421422

Return PreviousReturn SourceNameMatchIDID

16

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)

17 Automatic HF Universe Count

Tass Tremont (54%)

CISDMHedge(35 %)

HFR (52%)

25% 5% 16%

4%10%

12%

28%

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

18

4%34%30%12%20%2 strategies

29%29%15%8%19%1 strategy

Fund of fundsSecurity SelectionRelative ValueMulti-ProcessDirectional%

500748476226468

3227224094156Non-agreement

468476236132312#Agreement

Fund of fundsSecurity SelectionRelative ValueMulti-ProcessDirectional

2 “name” matches

Case of Two Available Classifications

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).

RelativeXXX

RelativeXXX

Broad StrategyFund

DirectionalYYY

Relative ValueYYY

Broad StrategyFund

19

18%31%15%4%31%3 strategies

4%39%28%10%18%2 strategies

23%30%23%10%15%1 strategy

Fund of FundsSecurity SelectionRelative ValueMulti-ProcessDirectionalPercentages

332815593228410

274622646Non-agreement

464383181161982 to 1

259331253106166# Agreement

Fund of FundsSecurity SelectionRelative ValueMulti-ProcessDirectional

3 “name” Matches

Case of Three Available Classifications

Relative Val.XXX

Relative Val.XXX

Relative Val.XXX

Broad StrategyFund

DirectionalZZZ

Sec. SelectZZZ

Relative Val.ZZZ

Broad StrategyFund

Sec. SelectYYY

Relative Val.YYY

Relative Val.YYY

Broad StrategyFund

Note: some funds are classified in 3 different “broad” strategies.

20

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

21

• 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

22 Part 2: Methodology Requirements

• Initiate discussion

• Several proposals made by ad-hoc committee:

– Statistical: clustering, PCA

– Structural: risk factors, syntactical

• Further proposals are welcome

– Explanation facility

23 Current Classification Methodology Proposals

• 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

24 F. –S. L’Habitant (2003)

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”)

25

• “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)

26 Naik (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

27 Martellini (2003)

• Verification and validation problem

• What does managers’ portfolio holdings say about strategy?

– Strategy reasoning system

28 Indjic (2003)

Issuer Type Sector PositionX Equity A ShortX CB A LongX CDS A LongY Equity B LongZ Equity B Short

• Why not classifying strategies on the basis of VaR?

• Can discretionary traders be ever classified using systematic factors?

29 AIMA Conference Feedback

• 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)

30 Future: Methodology

• 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

31 Future: Committee