[PPT] - A Brief Survey of Hedge Fund Research The London School of Economics PowerPoint Presentation

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“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: info@premiacap.com * 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).

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I. Return Sources
A. Inefficiencies

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

Allowable Inefficiency in Private, Mutual Fund 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 Sources
D. 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 Greater Than 6% Since 1980

M anaged H edge S& P 500 Futures a Funds b 1 Sep-N ov 1987

30%

8.5% 2 A pr-Jul 2002

20%

10.6%

4.4%

3 Jun-Sep 2001

17%

1.9%

3.8%

4 Jul-A ug 1998

15%

5.8%

9.4%

5 Feb-M ar 2001

15%

4.0%

3.8%

6 Jun-O ct 1990

15%

19.4%

1.9%

7 Sep-N ov 2000

13%

2.7%

6.4%

8 Sep 2002

11%

1.9%

1.5%

9 D ec 2002 to Feb 2003

10%

12.1% 0.5% 10 A ug-Sep 1981

10%

0.1% 11 Feb-M ar 1980

10%

10.3% 12 D ec 1981-M ar 1982

10%

7.9% 13 Sep 1986

8%
4.2%

14 D ec 1980-Jan 1981

7%

9.5% 15 Feb-M ar 1994

7%

0.3%

2.1%

16 Jan-Feb 2000

7%

0.9% 6.8% 17 Jan 1990

7%

3.2%

2.1%

18 M ay-July 1982

7%

1.4% 19 Jul-Sep 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.

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II. Properties of Returns
A. Short-Options-Like Returns

HFR 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 E vent driven monthly returns LOESS Fit (degree = 3, span = 1.0000)

LPP Pictet Index: a benchmark index for Swiss institutional 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 the

relationship 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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II. Properties of Returns
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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II. Properties of Returns
B. Long-Options-Like Returns
Call option

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

Histogram of Monthly Returns of the Barclay CTA Index 10 20 30 40 50 60

1

5 %

1

%

4

%

1

% 1 % 4 % 1 % 1 5 % Monthly Returns Frequency

Source: Lungarella (2002), Figure 1.

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II. Properties of Returns
B. Long-Options-Like Returns (Continued)
Straddle

Global Macro Style versus the Dollar

4
2

2 4 6 1 2 3 4 5 Quintiles of Dollar Return Percent per Month Global Macro US Dollar Source: Fung and Hsieh (1997), Figure 5.

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III. Performance Measurement
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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III. Performance Measurement
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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III. Performance Measurement
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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III. Performance Measurement
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 M ay- 01 Jun- 01 Jul- 01 Aug- 01 Sep- 01 Oct- 01 Nov- 01 Dec- 01 Month Return 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 Events

Sample 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 6,00 Normale und modifizierte VaR (in %) Historische monatliche Renditen Effizienzlinie mit Berücksichtigung von S + K Effizienzlinie

hne 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 6,00 Normal and modified VaR (in %) Historic monthly returns Efficient frontier with consideration

f S + K

Efficient frontier without consideration

f 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 6,00 Normale und modifizierte VaR (in %) Historische monatliche Renditen 0,30% 0,40% 0,50% 0,60% 0,70% 0,80% 0,90% 1,00 2,00 3,00 4,00 5,00 6,00 Normale und modifizierte VaR (in %) Historische monatliche Renditen Effizienzlinie mit Berücksichtigung von S + K Effizienzlinie

hne 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 6,00 Normal and modified VaR (in %) Historic monthly returns 0,30% 0,40% 0,50% 0,60% 0,70% 0,80% 0,90% 1,00 2,00 3,00 4,00 5,00 6,00 Normal and modified VaR (in %) Historic monthly returns Efficient frontier with consideration

f S + K

Efficient frontier without consideration

f 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, LLC as 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 Exposures

Hedge-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 0.25 Dedicated Short Bias Fixed Income Arbitrage Managed Futures Event Driven Emerging Long Short Equity Global Macro Market Neutral Convertible Arbitrage 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Global Macro Fixed Income Arbitrage Market Neutral Managed Futures Event Driven Emerging Dedicated Short Bias Long Short Equity Convertible Arbitrage

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IV. Risk Management
D. Transparency and the Limitations to Quantitative

Techniques (Continued)

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
D. Transparency and the Limitations to Quantitative

Techniques (Continued)

Cautionary Example

Simulated Short Volatility Investment Strategy

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) Investment Value Short Volatility Investment Investment at T-bill 6% Source: Anson (2002), Exhibit 1. (This chart was created by Professor J. Clay Singleton of Rollins College using the algorithm in Anson’s article.)

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

Portfolio

Six Possible Conceptual Frameworks for Hedge Funds, Part I

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

1. Equity Proxies

Want managers who capture the Replace traditional premium 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 betas Performance Characteristics

nce 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 for Be 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's level of risk and loss aversion.

3. Alpha Generators/Exploiting Inefficiencies

Emphasis on managers whose performance cannot be Expectation is that return linked 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 index data in asset allocation modeling. For every investor that benefits from exploiting an inefficiency, there must be an investor supplying the inefficiency: Strategies are therefore inherently capacity constrained.

Source: Till (2004).

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V. Investor Preferences and Choices
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 INSTITUTIONAL HOW 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 idiosyncratic Through a Fund-of-Funds

perational 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

f the hedge fund data that is available,
ption.

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

Source: Till (2004).

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V. Investor Preferences and Choices

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

Six Possible Conceptual Frameworks for Hedge Funds, Part II

HOW HEDGE FUNDS SHOULD BE CHARACTERIZED BENCHMARK

1. Equity Proxies

Want correlation with S&P but with truncated downside. Equity mutual funds

2. Unconventional Betas/Non-Standard

Benchmark is either a linear function Performance Characteristics

f basic factor exposures, or

asset-based style factors, or hedge fund styles.

3. Alpha Generators/Exploiting Inefficiencies

A total-return benchmark

4. Traditional Factor Exposures with Additional

Derived from the factors assumed to Returns 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 Source: Till (2004).

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VI. Conclusion
We cannot all be exploiters of inefficiencies, providers
f 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
f 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

f 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 RiskInvest

2002 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., farren@premiacap.com