Why Understanding Hedge Fund Beta Is Important

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Why understanding hedge fund beta is important?

Portable Alpha Asia 2006 Conference
25 – 27 April 2006, Conrad Hotel, Hong Kong

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This presentation and the analysis herein contains proprietary information and is not to be
copied, reproduced, used, or divulged to any person in whole or in part without proper
written authorization from an officer or director of Siemens AG. This information is the
fin4cast Q U A N T I T A T I V E I N V E S T M E N T S T R A T E G I E S

property of Siemens AG and is subject to completion and amendment.

The content of the presentation should not be interpreted as legal, tax, or investment
Portable Alpha Asia 2006

advice. This document has been prepared by Siemens for discussion purposes only, based
upon unaudited financial data. Siemens does not make any representation that the
strategy will or is likely to achieve performance comparable to that shown. This document
is not an offer to sell or a solicitation for the sale of a security nor shall there be any sale
of security in any jurisdiction where such offer, solicitation, or sale would be unlawful. An
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employees, members, or principals is not indicative of future results and is no guarantee
targeted performance will be achieved.

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Agenda


Differentiating between hedge fund beta and traditional investment beta

Are hedge funds just repacking beta and selling it as a pure alpha strategy?

Replicating the hedge fund returns synthetically

Why should investors pay for embedded beta?

Conclusion

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Differentiating between hedge fund beta and traditional
investment beta

Yt = α + β * X t + ε t

Traditional beta:

Market Return
Single Index Model Return Alpha = Skill = Residuals
Market Risk
Alpha Beta
Agility Agility
Hedge Market Return
Pure
„Agility“
Fund = Residuals
Alpha
Return Market Risk

Yt = α + δ * At + β * X t + ε t
Hedge Fund Beta:

Yt = α + β1 * X 1 t + β 2 * X 2 t + β 3 * X 3 t + β 4 * X 4 t + L + β k * X k t + ε t
APT

Volatility Commodity Currency Credit ... Factors
Risk Risk Risk Risk TBD*
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*See Appendix A.


An example
Traditional beta

Agility

Beta
Alpha

Risk Factors
Hedge fund beta
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Source: CSFB, Siemens/fin4cast

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We need to understand what drives the hedge fund return
Pure alpha requires a lot of maintenance. It comes from manager’s skills:

technology and know how

„Agility” is the ability to invest in ways not open to traditional investors:

Beta Agility: derivatives, short-selling, no restrictions on consentration limits

and credit ratings of investments

Alpha Agility: complex trading rules (Model Risk, Momentum Risk), leverage

Market beta is not a good reason to invest in hedge funds

Pure alpha is desirable, not reliable and not replicable

Agility is desirable, reliable and difficult to replicate

Market beta is reliable, easily replicable, but not desirable
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Agenda






Conclusion

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We need to translate the hedge funds returns into risk factors

Principal Component Analysis (PCA)

Conv_Arb
Multi_Strat FI_Arb
Event_D Equity_MN Global
Emerging Macro
Markets
CSFB/T HFI
Managed
Long Short
Futures
Equity

Dedicated Short

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Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast *See Appendix B for 6 principal components.

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Let us have a close look at the returns of CSFB/Tremont Hedge Fund Index
Model with Beta Agility,
Model with Beta and
Model with Beta Agility Beta T-Statistic Significance Beta T-Statistic Significance Beta T-Statistic Significance
Alpha Agility and Pure

Alpha Agility
Alpha
Commodity Risk 0.3303 3.2349 0.0020 Commodity Risk 0.2211 2.3716 0.0210 Pure Alpha 0.0738 0.4701 0.6420
Credit Risk -0.0355 -0.3157 0.7533 Credit Risk -0.0485 -0.4927 0.6241 Commodity Risk 0.2410 1.6106 0.1185

Currency Risk -0.2056 -1.9300 0.0583 Currency Risk -0.2031 -2.1428 0.0363 Credit Risk 0.1043 0.6836 0.4998
Emerging Market Risk 0.4078 3.2941 0.0016 Emerging Market Risk 0.2320 2.0081 0.0492 Currency Risk -0.2993 -1.8470 0.0753
Interest Rate Risk (short) -0.1566 -1.6451 0.1051 Interest Rate Risk (short) -0.1346 -1.6093 0.1129 Emerging Market Risk 0.4532 2.0792 0.0469
Interest Rate Risk (long) 0.0284 0.2352 0.8148 Interest Rate Risk (long) 0.0962 0.8881 0.3781 Interest Rate Risk (short) -0.0161 -0.1019 0.9196
Liquidity Risk 0.2178 2.1539 0.0352 Liquidity Risk 0.1451 1.5458 0.1275 Interest Rate Risk (long) 0.0406 0.1930 0.8484
Yield Curve Risk -0.1633 -1.6347 0.1073 Yield Curve Risk -0.0926 -1.0405 0.3024 Liquidity Risk 0.2482 1.6690 0.1063
Market Risk -0.2591 -1.8066 0.0758 Market Risk -0.1859 -1.4425 0.1545 Yield Curve Risk -0.1555 -0.9538 0.3483
Style Risk 0.1531 1.5825 0.1187 Style Risk 0.1102 1.2597 0.2128 Market Risk -0.0960 -0.4366 0.6658
Volatility Risk -0.2400 -1.6176 0.1109 Volatility Risk -0.2800 -2.1415 0.0364 Style Risk 0.1668 1.0569 0.2996
Model Risk -0.0022 -0.0175 0.9861 Volatility Risk -0.1787 -0.8711 0.3911
Momentum Risk 0.4136 3.4698 0.0010 Model Risk -0.1621 -0.9293 0.3607
Momentum Risk -0.0733 -0.4289 0.6713
R-squared 0.5041 R-squared 0.6316 R-squared 0.54277
Adjusted R-squared 0.4146 Adjusted R-squared 0.5505 Adjusted R-squared 0.33048
S.E. of regression 0.0077 S.E. of regression 0.0067 S.E. of regression 0.00611
Correlation 0.7100 Correlation 0.7947 Correlation 0.73673
Durbin-Watson stat 1.6143 Durbin-Watson stat 1.8638 Durbin-Watson stat 1.70051
F-statistic 5.6365 F-statistic 7.7817 F-statistic 2.55677
Prob(F-statistic) 0.0000 Prob(F-statistic) 0.0000 Prob(F-statistic) 0.01822
Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast

Beta agility and alpha agility seem to be significant sources of hedge fund return,
but is there any pure alpha?
Pure alpha tends to be less a matter of pure skill, but more a matter of flexibility
It is difficult to separate the alpha agility (flexibility) from the pure alpha (skill)
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Agenda





Why should investors pay for embedded beta

Conclusion

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It was necessary to understand where the hedge fund returns

came from in the past

It is more important to understand what will drive the hedge fund

returns in the future

Building Autoregressive Models with exogenous Variables (ARX)

to predict the future returns of:

CSFB/Tremont Hedge Fund Index

CSFB/Tremont Equity Long/Short Index (see Appendix D)

CSFB/Tremont Emerging Market Index (see Appendix E)

CSFB/Tremont Global Macro Index (see Appendix F)
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Forecasting the returns of the CSFB/Tremont Hedge Fund Index

Credit Risk

Interest Rate Risk (US long)
Emerging Market Risk

Commodity Risk

Interest Risk (EU long)

Market Risk

Momentum Risk

Pure alpha

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Source: Siemens/fin4cast

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What sources of return are more likely to drive the performance of
CSFB/Tremont Hedge Fund Index in the future?

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How the model did and what it expects for April 2006?


+0.73%
Forecast for April 2006

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Agenda






Conclusion

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Embedded beta could be divided into market beta and beta agility.

Investors should be prepared to pay for pure alpha, alpha agility and beta

agility, but not for market beta.

Market beta is easy to replicate. Investors should buy it cheaper form the

traditional managers.

Although beta agility is replicable, few hedge fund managers are in a position to

replicate it.

Investors should be prepared to pay for alpha agility, which is difficult to

replicate.

Investors should pay for pure alpha, which comes from innovation, technology

and know how and is not replicable.
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Agenda






Conclusion

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Summary: Why understanding hedge fund beta is important?
Traditional beta (single factor model) vs. hedge fund beta (multi factor model):

agility explains most of hedge fund returns

Pure alpha is desirable, not reliable and not replicable

Hedge Funds sell beta agility and alpha agility:

Both seem to be significant sources of hedge fund returns

Pure alpha tends to be less a matter of pure skill, but more a matter of flexibility

Deep understanding of past hedge funds performance is not enough. We need

a better grip on what will drive future hedge fund returns.

alpha agility: momentum and trading model

beta agility: emerging market factor, commodity factor, stock market factor

Investors should pay for pure alpha and be prepared to pay for agility
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Appendix A: Considered (Hidden) Risk Factors of Hedge Funds
Market Risk: S&P 500

Commodity Risk: Dow Jones AIG Commodity Index

Credit Risk: Spread (Yield M.L. US Corp BBB Bonds – Yield M.L. US Corp. AAA Bonds)

Emerging Market Risk: JPMorgan EMBI+ Composite

FX Risk: NYBOT US Dollar Index

Style Risk: Spread (S&P 500 – Russell 2000)

Interest Rate Risk: US Treasury Bill 90 day (short), Euro Bund (long EU), US T-Note 10y (long US)

Volatility Risk: VIX Index (implicit volatility of S&P 100 options)

Yield Curv Risk: Spred (US 30 year Treasury Bond – US Tresury Bill 90 day)

Liquidity Risk: NYSE Traded Volume

Model Risk: fin4cast Global Macro Diversified Futures Index I

Momentum Risk: Autoregressive Term
Source: Reuters, Thomson Financial, Siemens/fin4cast
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Appendix B: Principal Component Analyses

Source: CSFB, Reuters, Thomson Financial, Siemens/fin4cast
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Appendix C: Descriptive Statistics and Correlation Matrix
Equity Fixed Long
Convertible CSFB Hedge Dedicated Emerging Event Global Managed Multi
Descriptive Statistics Market Income Short
Arbitrage Fund Index Short Market Driven Macro Futures Strategy

Neutral Arbitrage Equity
Mean 0.78% 0.61% -0.16% 1.52% 0.48% 0.75% 0.45% 0.72% 0.31% 0.78% 0.70%
Median 0.85% 0.61% -0.48% 1.98% 0.43% 0.82% 0.56% 0.50% 0.39% 0.45% 0.71%

Maximum 3.40% 2.72% 10.89% 7.34% 2.39% 2.71% 2.97% 4.36% 10.31% 9.20% 4.86%
Minimum -2.36% -1.09% -13.56% -5.06% -0.49% -1.76% -2.21% -1.77% -7.99% -8.71% -1.54%
Std. Dev. 0.011 0.008 0.045 0.026 0.005 0.009 0.010 0.012 0.029 0.038 0.010
Skewness -0.14 0.24 -0.10 -0.31 1.05 -0.25 -0.41 0.47 0.40 -0.20 0.79
Kurtosis 3.07 2.91 3.44 2.75 4.63 3.45 3.42 3.10 5.81 2.77 5.72
Autocorrelation 1st Order 0.55 0.10 0.13 0.07 0.18 0.26 0.19 0.20 0.00 0.10 0.24
Jarque-Bera 0.2354 0.7093 0.6950 1.3361 21.2498 1.3784 25.7536 2.6895 25.5599 0.6553 29.7101
Probability 0.8889 0.7014 0.7065 0.5127 0.0000 0.5020 0.2759 0.2606 0.0000 0.7206 0.0000
Observations 72 72 72 72 72 72 72 72 72 72 72

Equity Fixed Long
Convertible CSFB Hedge Dedicated Emerging Event Global Managed Multi
Correlation Matrix Market Income Short
Arbitrage Fund Index Short Market Driven Macro Futures Strategy
Neutral Arbitrage Equity
Convertible Arbitrage 1.00
0.41 1.00
CSFB Hedge Fund Index
-0.22 -0.45 1.00
Dedicated Short
0.20 0.51 -0.60 1.00
Emerging Market
0.31 -0.05 -0.10 0.00 1.00
Equity Market Neutral
0.48 0.59 -0.59 0.61 0.02 1.00
Event Driven
0.39 0.25 -0.03 0.12 0.13 0.35 1.00
Fixed Income Arbitrage
0.27 0.39 0.17 -0.06 -0.04 0.09 0.17 1.00
Global Macro
-0.02 0.68 -0.64 0.52 -0.18 0.37 0.00 -0.07 1.00
Long Short Equity
0.05 0.47 0.14 -0.14 -0.08 -0.05 -0.21 0.38 -0.03 1.00
Managed Futures
0.60 0.61 -0.52 0.54 0.15 0.64 0.42 0.06 0.40 -0.08 1.00
Multi Strategy
Period: 31 January 2000 - 31 January 2006
Source: CSFB/Tremont, Siemens/fin4cast
21

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Appendix D: Forecasting Model for CSFB/Tremont Long Short Equity Index

Credit Risk

Yield Curve Risk


Commodity Risk


Market Risk

Momentum Risk

Pure alpha

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Appendix D: What sources of return are more likely to drive the performance of
CSFB/Tremont Equity Long/Short Index in the future?

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Appendix D: How the model did and what it expects for April 2006?


+0.61%

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Appendix E: Forecasting Model for CSFB/Tremont Emerging Market Index

Credit Risk

Yield Curve Risk


Commodity Risk


Market Risk

Momentum Risk

Pure alpha

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Appendix D: What sources of return are more likely to drive the performance of
CSFB/Tremont Emerging Market Index in the future?


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-0.63%

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Appendix D: Forecasting Model for CSFB/Tremont Global Macro Index

Credit Risk

Yield Curve Risk


Commodity Risk


Market Risk

Momentum Risk

Pure alpha

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Appendix D: ARX Model for CSFB/Tremont Global Macro Index
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-0.02%

30

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Biographies
Dr. Miroslav Mitev
Siemens AG Österreich
Siemens IT Solutions and Services

PSE/fin4cast
Phone: +43 (0) 51707 46253
Fax: +43 (0) 51707 56465
Mobile: +43 (0) 676 9050903
Email: miroslav.mitev@siemens.com

Dr Miroslav Mitev is a managing director and head of quantitative research and strategy development at
Siemens/fin4cast. Dr Mitev is responsible for the development of innovative, systematic long-short
investment strategies for institutional investors world wide based on Siemens/fin4cast technology. After
joining Siemens in 2001 Dr Mitev successfully formed a qualified team of 25 professionals which is
continuously developing the Siemens/fin4cast Technology and building mathematical forecasting models
for a variety of financial instruments like currency futures, commodity futures, stock index futures, bond
futures, single stocks and hedge fund indices. Dr Mitev is in charge of the Siemens/fin4cast’s research
cooperation with various universities and is actively involved in the scientific management of numerous
master thesis and dissertations. Dr Mitev is a regular speaker at international conventions on liability driven
investing, asset management, hedge funds, portable alpha, advanced quantitative studies, algo-trading and
system research. Dr Mitev’s research is published on a regular basis in international journals and presented
on international scientific conferences. Prior to joining Siemens Dr Mitev was at CA IB, the Investment Bank
of Bank Austria Group, where he was in charge of the quantitative research of the securities research
division. Dr Mitev received a Master of Economics and Business Administration with main focus on
Investment Banking and Capital Markets. Dr Mitev also received a PhD in Economics with main focus on
Finance and Econometrics.
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