“A Survey of Hedge Fund Research” Presentation …1 “A Brief Survey of Hedge Fund Research” The London School of Economics’ Financial Markets Group 14 February 2006 Ms. Hilary

1

“A Brief Survey of Hedge Fund Research” The London School of Economics’ Financial Markets Group

14 February 2006

Ms. Hilary Till (LSE, MSc in Statistics, 1987) * Premia Risk Consultancy, Inc.* E-mail: [email protected] *

Phone: 312-583-1137 * Chicago * Fax: 312-873-3914

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Presentation Outline

I. Return Sources

II. Properties of Returns

III. Performance Measurement

IV. Risk Management

V. Investor Preferences and Choices

VI. Conclusion

Cover of “In Search of Alpha: Investing in Hedge Funds” by Alexander Ineichen, UBS Global Equity Research, October 2002.

Based on Till and Gunzberg (2005).

3

I. Return Sources

A. Inefficiencies

Capacity of Hedge Fund Industry (With an “Alpha Advantage”)in Billions of Dollars

Allowable Inefficiency in Private, MutualFund and Institutional Fund Management

-0.5% -0.75% -1.0%Required Excess 10.0% 2,750 4,125 5,500

Return for 7.5% 3,667 5,500 7,333 Hedge Funds 5.0% 5,500 8,250 11,000

Similar Argument also in Ross (2004).

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I. Return Sources

B. Risk Premia

• Relative-Value Bond Funds

• Equity Risk Arbitrage

• Value vs. Growth Strategy

• Small Capitalization Stocks

• High-Yield Currency Investing

Rembrandt’s Storm on the Sea of Galilee, Isabella Stewart Gardner Museum, Boston, and Cover of Against the Gods: The Remarkable Story of Risk by Peter Bernstein, John Wiley & Sons, 1996.

Examples were drawn from Cochrane (1999a,b), Harvey and Siddique (2000), and Low (2000).

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I. Return Sources

C. Illiquidity• Benefits: Tick-by-Tick Evaluation of a Good Investment is

Painful

Probability of Making Money at Different Scales

Scale Probability

1 year 93%1 quarter 77%1 month 67%

1 day 54%1 hour 51.3%

1 minute 50.17%1 second 50.02%

Source: Taleb (2001), Table 3.1.

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I. Return Sources

C. Illiquidity (Continued)

• Costs: Default and Liquidation Risk

Source: Krishnan and Nelken (2003).

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I. Return SourcesD. Eventful Periods

• Managed Futures programs are now expected to benefit from event risk.

The Myth of Hedge Fund Market Neutrality: Good News for Managed Futures

Declines in the S&P 500 of GreaterThan 6% Since 1980

M a n aged H ed geS & P 500 F u tu res a F u n d s b

1 S ep -N ov 1 987 -30% 8 .5%2 A p r-Ju l 200 2 -20% 10 .6% -4 .4%3 Ju n -S ep 2 001 -17% 1 .9% -3 .8%4 Ju l-A u g 1998 -15% 5 .8% -9 .4%5 F eb -M ar 2001 -15% 4 .0% -3 .8%6 Ju n -O ct 1990 -15% 19 .4% -1 .9%7 S ep -N ov 2 000 -13% 2 .7% -6 .4%8 S ep 2002 -11% 1 .9% -1 .5%9 D ec 2002 to F eb 2003 -10% 12 .1% 0.5%

10 A u g-S ep 1981 -10% 0 .1%11 F eb -M ar 1980 -10% 10 .3%12 D ec 1981-M ar 1982 -10% 7 .9%13 S ep 1986 -8% -4 .2%14 D ec 1980-Jan 1981 -7% 9 .5%15 F eb -M ar 1994 -7% 0 .3% -2 .1%16 Jan -F eb 200 0 -7% 0 .9% 6.8%17 Jan 1990 -7% 3 .2% -2 .1%18 M ay-Ju ly 1982 -7% 1 .4%19 Ju l-S ep 1999 -6% -0 .5% 0.7%

A verage -12% 5% -2%

a: CISDM (Center for International Securities and Derivatives Markets) Trading Advisor Qualified Index.b: HFR (Hedge Fund Research) Fund Weighted Composite Index.

Based on Horwitz (2002), Slide 8.

8


A. Short-Options-Like ReturnsHFR Event Driven Returns vs. Traditional Portfolio Returns

-0.10

-0.05

0.00

0.05

0.10

-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06

LPP Pictet Index monthly returns

HFR

Even

t driv

en m

onth

ly re

turn

s

LOESS Fit (degree = 3, span = 1.0000)

LPP Pictet Index:a benchmark index for Swissinstitutional investors, which includes Swiss equities, global equities, and global bonds.

LOESS Fit (Regression):a type of regression used to fit non-linear relationships. Here, the researchers fit therelationship between hedge fund returns and market returns. Market returns, in turn,are represented by the LPP Pictet Index.

HFR: Hedge Fund Research, Inc.Event Driven (Strategy): Also known as “corporate life cycle investing.”

Source: Favre and Galeano (2002), Exhibit 8.

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A. Short-Options-Like Returns (Continued)

Returns of an Options-Based Index Strategy that Maximizes the Sharpe Ratio vs. an Index

Source: Goetzmann et al. (2002), Figure 4.

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B. Long-Options-Like Returns• Call option

Payoff ProfileInvestors expect long-options-like profiles from CTA’s and global macro hedge fund managers.

Histogram of Monthly Returns of the Barclay CTA Index

0

10

20

30

40

50

60

-15%

-10% -4% -1% 1% 4% 10%

15%

Monthly Returns

Freq

uenc

y

Source: Lungarella (2002), Figure 1.

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B. Long-Options-Like Returns (Continued)

• Straddle

Global Macro Style versus the Dollar

-4

-2

0

2

4

6

1 2 3 4 5

Quintile s of Dollar Re turn

Perc

ent p

er M

onth

Global Macro US Dollar

Source: Fung and Hsieh (1997), Figure 5.

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A. Sharpe Ratio• Required Assumptions

1. Historical Results Have Some Predictive Ability;

2. The Mean and Standard Deviation Are Sufficient Statistics;

3. The Investment’s Return Are Not Serially Correlated; and

Source: Sharpe (1994).

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A. Sharpe Ratio (Continued)

• Required Assumptions (Continued)

4. The Candidate Investments Have Similar Correlations with the Investor’s Other Assets.

5. Conclusion: Sharpe himself states that the use of historical Sharpe ratios as the basis for making predictions …

“is subject to serious question.”Source: Lux, (2002).

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B. Alternative Metrics• Asset-Based Style Factors

Hedge Fund Styles That Can be Modeled with Asset-Based Style Factors

Market Timing or Directional Strategies

High beta to standard asset classes

Long/Short or Relative Value Strategies

Low beta to standard asset classes

Trend Following Reversal

Equity Fixed-Income

Event-Driven

• Stocks • Bonds • Currencies • Commodities

Convergence on: • Capitalization Spread • Value/Growth Spread Trend Following: 1 and/or 2 above

Convergence on: • Credit Spread • Mortgage

Spread Trend Following: Credit Spread

Excerpted from Fung and Hsieh (2003), Exhibit 5.5.

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B. Alternative Metrics (Continued)

• Asset-Based Style Factors

HFR Event Driven Index

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

Jul-00

Aug-00

Sep-00

Oct-00

Nov-00

Dec-00

Jan-01

Feb-01

Mar-01

Apr-01

May-01

Jun-01

Jul-01

Aug-01

Sep-01

Oct-01

Nov-01

Dec-01

Month

Ret

urn EDRP

ED

Equity Arbitrage Strategies

EDRP: Event Driven Replicating Portfolio

ED: HFR Event Driven Index

Source: Agarwal and Naik (2004).

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IV. Risk Management

A. Incorporating Extreme EventsSample Portfolio with a Maximum Investment in Hedge Funds of 10%

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

0,90%

1,00 2,00 3,00 4,00 5,00 6,00Normale und modifizierte VaR (in %)

His

toris

che

mon

atlic

he R

endi

ten

Effizienzliniemit Berücksichtigung von S + K

Effizienzlinieohne Berücksichtigung von S + K

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

0,90%

1,00 2,00 3,00 4,00 5,00 6,00Normal and modified VaR (in %)

His

toric

mon

thly

ret

urns

Efficient frontierwith considerationof S + K

Efficient frontierwithout considerationof S + K

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

0,90%


His

toris

che

mon

atlic

he R

endi

ten

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

0,90%


His

toris

che

mon

atlic

he R

endi

ten

Effizienzliniemit Berücksichtigung von S + K

Effizienzlinieohne Berücksichtigung von S + K

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

0,90%


His

toric

mon

thly

ret

urns

0,30%

0,40%

0,50%

0,60%

0,70%

0,80%

0,90%


His

toric

mon

thly

ret

urns

Efficient frontierwith considerationof S + K

Efficient frontierwithout considerationof S + K

(S refers to skewness, and K refers to kurtosis).

Source: Signer and Favre (2002), Exhibit 6.

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IV. Risk Management

B. Event Risk: Individual Managers

A Derivatives Portfolio’s Exposure to Severe Events

Event Maximum Loss October 1987 stock market crash -4.11% Gulf War in 1990 -4.12% Fall 1998 bond market debacle -6.42% Aftermath of 9/11/01 attacks -3.95%

Worst-Case Event Maximum Loss Fall 1998 bond market debacle -6.42%

Value-at-Risk based on recent volatility and correlations 3.67%

Source: Risk Report from Premia Capital Management, LLCas cited in Till and Eagleeye (2003).

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IV. Risk Management

C. Event Risk: Fund-of-Funds

Source: Johnson et al. (2002).

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IV. Risk Management

D. Transparency and the Limitations to Quantitative Techniques

• Bismarck’s Advice

From experience, it seems that hedge fund investors apply Baron von Bismarck's advice on sausages and legislation to their investments:

“Anyone who likes legislation or sausage should watch neither one being made.”

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IV. Risk Management

D. Transparency and the Limitations to Quantitative Techniques (Continued)

• Inferring ExposuresHedge-Fund Style Radars

“The figure shows the hedge fund radars obtained for a convertible arbitrage fund (left) and a fund of hedge funds (right). The sensitivities (i.e., style-beta coefficients) are estimated using three years of historical data.”

Source: Lhabitant (2001).

0.00

0.05

0.10

0.15

0.20

0.25Dedicated Short Bias

Fixed Income Arbitrage

Managed Futures

Event Driven

EmergingLong Short Equity

Global Macro

Market Neutral

Convertible Arbitrage

0.000.050.100.150.200.250.300.350.400.45Global Macro

Fixed Income Arbitrage

Market Neutral

Managed Futures

Event DrivenEmerging

Dedicated Short Bias

Long Short Equity

Convertible Arbitrage

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IV. Risk Management


• Inferring Exposures (Continued)

This graph illustrates Premia Capital’s rolling exposures in energies, metals, U.S. fixed income, livestock, and agriculture during the first eight months of 2004. More technically, the graph shows the conventional benchmarks that were most effective in jointly explaining Premia’s daily return variance using an advanced returns-based-analysis technique.

Proportional Marginal Variance Decomposition

The benchmarks are the Goldman Sachs (GS) Commodity sector excess return (ER) indices and a Bloomberg U.S. fixed-income index. The graph’s y-axis is the fraction of R-squared that can be attributed to a benchmark exposure. This is also known as the benchmark’s variance component. The middle chart shows each benchmark’s contribution to R-squared over the whole history.

Based on Feldman (2005), Slide 8, PRISM Analytics, http://www.prismanalytics.com.

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IV. Risk Management


• Cautionary ExampleSimulated Short Volatility Investment Strategy

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76

Time (months)

Inve

stm

ent V

alue

Short Volatility Investment Investment at T-bill 6%

Source: Anson (2002), Exhibit 1. (This chart wascreated by Professor J. Clay Singleton of Rollins College using the algorithm in Anson’s article.)

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A. Types of Products

• Risk and Loss Aversion

• In a Situation of Surplus or Not

Sources: Chen et al. (2002) and Siegmann and Lucas (2002).

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V. Investor Preferences and ChoicesB. How to Incorporate Hedge Funds in an Investor’s Overall

PortfolioSix Possible Conceptual Frameworks for Hedge Funds, Part I

POTENTIAL IMPLICATIONS FOR INSTITUTIONALHOW HEDGE FUNDS SHOULD BE CHARACTERIZED IMPLICATIONS FOR MANAGER SELECTION ASSET ALLOCATION

1. Equity Proxies Want managers who capture the Replace traditionalpremium of asset class but also curtail downside risk equity managers with

hedge fund managers.

2. Unconventional Betas/Non-Standard Could decide to only use style-pure managers Include unconventional betasPerformance Characteristics once factor exposures are defined; in plan's long-term asset allocation

modeling.Use investable style tracker funds instead of managers; and/or

Opens up possibility forBe careful to not pay high "alpha" fees for what is tactical style selection.

actually a type of "beta."Decide which hedge fund styles

are appropriate, given an institution'slevel of risk and loss aversion.

3. Alpha Generators/Exploiting Inefficiencies Emphasis on managers whose performance cannot be Expectation is that returnlinked to major risk factors patterns will be unrelated to asset

classes in the core portfolio.Manager selection is a bottom-up exercise.

Cannot use hedge fund style and indexdata in asset allocation modeling.

For every investor thatbenefits from exploiting

an inefficiency, there mustbe an investor supplying the

inefficiency:Strategies are therefore

inherently capacity constrained.

Source: Till (2004).

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B. How to Incorporate Hedge Funds in an Investor’s Overall Portfolio (Continued)

Six Possible Conceptual Frameworks for Hedge Funds, Part I (Continued)

POTENTIAL IMPLICATIONS FOR INSTITUTIONALHOW HEDGE FUNDS SHOULD BE CHARACTERIZED IMPLICATIONS FOR MANAGER SELECTION ASSET ALLOCATION

4. Traditional Factor Exposures with Additional Manager selection would be part of a top-down approach. A holistic framework in which all Returns from Market Segmentation and Liquidity Premia investments are represented in

terms of a common set of factors

5. Total Return Provision Emphasis on fund-of-funds or multi-strategy managers Diversify idiosyncraticThrough a Fund-of-Funds operational risk of individual

"Style Drift" is acceptable on the part of both managers hedge funds.and the fund-of-funds.

Additional advantage in modeling is as follows:Within a fund-of-funds portfolio, rebalancing is not a viable of the hedge fund data that is available,

option. fund-of-fund data have the least biases.

Optimal fund-of-fund construction is a responsibility of the fund-of-fund manager, not

the plan sponsor.

6. Unstable Factor Exposures Hedge Funds can't be integrated into an institutional framework. Don't use hedge funds


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B. How to Incorporate Hedge Funds in an Investor’s Overall Portfolio (Continued)

Six Possible Conceptual Frameworks for Hedge Funds, Part IIHOW HEDGE FUNDS SHOULD BE CHARACTERIZED BENCHMARK

1. Equity Proxies Want correlation withS&P but with

truncated downside.

Equity mutual funds

2. Unconventional Betas/Non-Standard Benchmark is either a linear functionPerformance Characteristics of basic factor exposures, or

asset-based style factors, orhedge fund styles.

3. Alpha Generators/Exploiting Inefficiencies A total-return benchmark

4. Traditional Factor Exposures with Additional Derived from the factors assumed toReturns from Market Segmentation and Liquidity Premia drive each hedge fund strategy's returns.

5. Total Return Provision Balanced 60/40 Portfolio:Through a Fund-of-Funds But note that this bogey has been

difficult to outperform.

6. Unstable Factor Exposures Not applicable


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VI. Conclusion

• We cannot all be exploiters of inefficiencies, providers of insurance, and suppliers of liquidity.

• Therefore, one will need to accept that most investors’ long-term performance will be due to an appropriately designed and executed asset allocation policy.

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References

• Agarwal, Vikas and Narayan Naik, “Risks and Portfolio Decisions involving Hedge Funds,” Review of Financial Studies, Spring 2004, pp. 63-98.

• Anson, Mark, “Symmetrical Performance Measures and Asymmetrical Trading Strategies: A Cautionary Example,” Journal of Alternative Investments, Summer 2002, pp. 81-85.

• Chen, Peng, Feldman, Barry, and Chandra Goda, “Portfolios with Hedge Funds and Other Alternative Investments: Introduction to a Work in Progress,” Ibbotson Associates, Working Paper, July 2002.

• Cochrane, John, “New Facts in Finance,” Economic Perspectives, Federal Reserve Board of Chicago, Third Quarter, 1999, pp. 36-58.

• Cochrane, John, “Portfolio Advice for a Multifactor World,” Economic Perspectives, Federal Reserve Board of Chicago, Third Quarter, 1999, pp. 59-78.

• Favre, Laurent and Jose-Antonio Galeano, “An Analysis of Hedge Fund Performance Using Loess Fit Regression,” Journal of Alternative Investments, Spring 2002, pp. 8-24.

• Feldman, Barry, “Returns-Based Analysis Using PMVD,” Prism Analytics, Presentation at 4th Hedge Fund Analytics Conference, Financial Research Associates, New York, 24 February 2005.

• Fung, William and David Hsieh, “Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds,” Review of Financial Studies, Summer 1997, pp. 275-302.

Degas, Edgar, “The Cotton Exchange at New Orleans,” 1873, Musée Municipal, Pau, France.

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References (Continued)

• Fung, William and David Hsieh, “The Risk in Hedge Fund Strategies: Alternative Alphas and Alternative Betas,” The New Generation of Risk Management for Hedge Funds and Private Equity (Edited by Lars Jaeger) Euromoney Books (London), 2003.

• Goetzmann, William, Ingersoll, Jonathan, Spiegel, Matthew, and Ivo Welch, “Sharpening Sharpe Ratios,” Yale School of Management, Working Paper, February 2002.

• Harvey, Campbell and Akhtar Siddique, “Conditional Skewness in Asset Pricing Tests,” Journal of Finance, June 2000, pp. 1263-1296.

• Horwitz, Richard, “Constructing a ‘Risk-Efficient’ Portfolio of Hedge Funds,” Kenmar Global Investment, Presentation at RiskInvest2002 Conference, Boston, 11 December 2002 (with data updated through February 2003).

• Johnson, Damien, Macleod, Nick, and Chris Thomas, “Modeling the Return Structure of a Fund of Hedge Funds,” AIMA Newsletter, April 2002.

• Krishnan, Hari and Izzy Nelken, “A Liquidity Haircut for Hedge Funds,” Risk Magazine, April 2003, pp. S18-S21.

• Lhabitant, Francois-Serge, “Hedge Fund Investing: A Quantitative Look Inside the Black Box,” Union Bancaire Privee, Working Paper, 2001.

• Low, Cheekiat, “Asymmetric Returns and Semidimensional Risks: Security Valuation with a New Volatility Metric,” Working Paper, National University of Singapore and Yale University, August 2000.

• Lungarella, Gildo, Harcourt AG, “Managed Futures: A Real Alternative,” swissHEDGE, 4th Quarter 2002.

• Lux, Hal, “Risk Gets Riskier,” Institutional Investor magazine, October 2002, pp. 28-36.

• Ross, Steve, “Market Efficiency and Behavioral Finance,” Presentation at MIT Fama Conference, May 2004.

• Sharpe, William, “The Sharpe Ratio,” Journal of Portfolio Management, Fall 1994, pp. 49-58.

• 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,” Journal of Alternative Investments, Summer 2002, pp. 31-41.

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References (Continued)

• Taleb, Nassim, Fooled By Randomness, Texere (New York), 2001.

• Till, Hilary and Joseph Eagleeye, “A Review of the Differences Between Traditional Investment Programs and Absolute-Return Strategies,” Quantitative Finance, June 2003, pp. C42-C48, http://www.premiacap.com/publications/QF_0603.pdf.

• Till, Hilary, “The Role of Hedge Funds in Institutional Portfolios: Part II,” PRMIA (Professional Risk Managers’ International Association) Members’ Update, October 2004, pp. 1-5.

• Till, Hilary and Jodie Gunzberg, “Survey of Recent Hedge Fund Articles, Journal of Wealth Management, Winter 2005, pp. 81-98.

Presentation Prepared By Katherine Farren, Premia Risk Consultancy, Inc., [email protected]

http://www.premiacap.com/publications/QF_0603.pdf

http://www.premiacap.com/publications/QF_0603.pdf

Documents

“A Survey of Hedge Fund Research” Presentation …1 “A Brief Survey of Hedge Fund Research” The London School of Economics’ Financial Markets Group 14 February 2006 Ms. Hilary