“ The logic of the idea             Illuminating the World of                                                             ...
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Per trac infographic-illuminating-the-world-of-hedge-funds

  1. 1. “ The logic of the idea Illuminating the World of was very clear. It was a hedge against the vagaries of the market. You can buy more HEDGE FUNDS good stocks without taking as much risk as someone who merely buys. ” presented by Alfred Winslow Jones The Institutional Investor Journal, August 1968 BRIEF HISTORY of HEDGE FUNDS of Regulation of U.S. securities Fortune magazine Alternative Investment SEC adopted rules requiring industry begins with the publishes article on Alfred Management Association (AIMA) registered advisors with Securities Act of 1933. Winslow Jones’ hedge fund - formed to represent all practitioners $150 million or more in AUM Regulation D exempts number of hedge funds in the alternative investment to report comprehensive companies selling to grows in the following years. management industry. fund information. “accredited investors” from registering. European Federation of founded. Investment Funds and Companies (FEFSI) Regulation of U.S. funds begins established – later Financial Services with passage of the Investment renamed European Authority (FSA) Company Act of 1940. Fund and Asset established to regulate Management the UK financial Association (EFAMA). services industry. 1933 1934 1940 1949 1966 1969 1974 1990 1991 1996 2001 2002 2011 2012 Securities Exchange Act of Managed Funds Committee of JOBS Act passed. 1934 establishes the Rothschild Family Association (MFA) European Securities Allows hedge funds to Securities and Exchange introduces the world’s is formed to advocate Regulators (CESR) market themselves to Commission (SEC). first fund of on behalf of hedge established – later a broader audience. hedge funds. funds and managed replaced by the futures firms. European Securities Alfred Winslow Jones Commodity Futures and Markets Authority forms first hedge fund Trading Commission (ESMA) in 2011. (coining them “hedged (CFTC) established. funds”) and eventually Commodity Trading Advisors Hedge Fund “UCITS III” introduces the 20% (CTAs), or commodity pools, Association (HFA) Directives released performance fee. are generally required to founded to advocate on in Europe allowing register with the CFTC. behalf of smaller and UCITS to employ emerging hedge funds. alternative strategies. Total reported Assets Under Management (AUM) of hedge $ 2.317 trillion funds and funds of hedge funds at the end of first half 2012. If broken down into individual U.S. dollar bills and placed side by side, they would circle the equator 9,017 times or go to the moon and back 470 times. 29% Long/Short Equity 15% CTA/Managed Futures The Most Popular 12% 10% Emerging Markets Global Macro HEDGE FUND HEDGE FUND 6% 6% Fixed Income STRATEGIES Event Driven STRATEGIES 6% 4% Relative Value Multi Strategy 12% Other North America Municipalities Europe 6,178 Funds of University 4,092 Hedge Funds Endowments Hedge Funds are on Every South CONTINENT * CONTINENT America Asia 1,267 637 Australia Africa 165 Hedge Fund Investors are 113 DIVERSE DIVERSE Sovereign Family Offices Wealth Funds *Except for Antarctica Foundations & Charitable Individual Organizations Investors NUMBER OF HEDGE FUNDS AND FUNDS OF HEDGE FUNDS REPORTING A GIVEN DOMICILE Fire, Police, Teacher & Other Pensions size of the assets WEALTH is Relatively Concentrated $ under management in billions of USD based on management company location USA UK Switzerland Luxembourg Brazil France Netherlands Germany Sweden Bermuda $1124 $483 $127 $89 $88 $66 $49 $34 $34 $27 3 out of 4 FUNDS Where is All the Money in CTAs & MANAGED are denominated in the US Dollar or Euro FUTURES FUNDS PERCENT OF TOTAL AUM BY FUND SIZE 1.1% < $25M 56% 21% 7% 4% 4% 3% 5% 1.1% $25-50M 1.8% 78.1% $51-100M 4.7% $101-250M >$1B 5.0% $251-500M US Euro Brazilian Chinese UK Swiss Other Dollar Real Yuan Pound Franc 8.2% $501M-1B GOOD THINGS COME IN SMALL PACKAGES GOOD THINGS COME IN SMALL PACKAGES Smaller Funds Tend to Outperform Larger Funds… …But Larger Funds are Generally Less Volatile CUMULATIVE PERFORMANCE BY SIZE OF FUND ANNUALIZED VOLATILITY BY SIZE OF FUND January 1996 to December 2011 January 1996 to December 2011 STANDARD 6.92% 700% 5.94% DEVIATION 558% 5.95% 500% 7.39% SEMI 356% 6.05% DEVIATION 5.96% 300% 307% 4.75% LOSS 4.07% DEVIATION 4.00% 100% DOWNSIDE 4.12% DEVIATION 3.63% 0% (5%) 3.67% DEC DEC DEC DEC DEC DEC DEC DEC 1996 1998 2000 2002 2004 2006 2008 2010 0% 1% 2% 3% 4% 5% 6% 7% 8% SMALL MID-SIZE LARGE < $100M AUM $100 - 500M AUM > $500M AUM STANDARD DEVIATION SEMI DEVIATION LOSS DEVIATION DOWNSIDE DEVIATION (5%) measures the volatility of returns measures the volatility of returns measures the volatility of returns measures the volatility of returns below from the mean below the mean from the mean only during periods a Minimum Acceptable Return (MAR), of a loss offered here at 5% If Youve Made it This Far... Its Time for Some More Advanced Analysis ITS ALL GREEK TO ME ITS ALL GREEK TO ME Some Common Investment Statistics SYMBOL DEFINITION WHAT IT ATTEMPTS TO ANSWER THE FORMULA FOR YOUR INNER MATH GEEK α Where M R = The mean return of the benchmark Measures the fund’s value relative to a How much extra did you earn from a fund Where M RD = The mean return of the fund benchmark. that you wouldn’t have otherwise earned from investing in the broad market? Alpha Alpha = M RD - Beta x M R Where R I = The return of the benchmark for period I Where RD I = The return of the fund for period I β Measures the fund’s sensitivity to How likely is your fund to track the Where M R = The mean return of the benchmark movements of the market as a whole benchmark? Where M RD = The mean return of the fund Where N = Number of periods N N Beta 2 Beta = ( ∑ ( R I - M R )(RD1 - MRD) ) ÷ ( ∑ ( R I - M R ) I-1 I-1 Where r is the threshold return, and Uses actual return distribution and How do your fund’s good returns stack up F is cumulative density function of returns. Ω divides expected returns into gains and losses to provide a relative measure of to its bad returns, and how likely is it that your fund will make more than a given b ∫(1-F(x))dx r Omega the likelihood of the fund achieving a percentage? Ω(r) = r given return. ∫F(x)dx a Where R I = Return for period I Where M R = Mean of return set R Where N = Number of periods σ Measures the degree of variation of the How much should you expect your fund’s N fund’s returns around the fund’s mean returns to vary from the norm? (average) return for a specified period. MR = ( ∑ R I ) ÷ N I=1 N Sigma 2 1/2 (Standard Deviation) Standard Deviation = ( ∑ ( R I - M R ) ÷ (N - 1) ) I-1 1/2 Annualized Standard Deviation = Monthly Standard Deviation x ( 12 ) UNDERSTANDING the MOMENTS of a DISTRIBUTION UNDERSTANDING MOMENTS DISTRIBUTION y μ = Mean 1st MEAN The average of returns. In other words, its the average. Moment x μ y σ = Standard Deviation VARIANCE 2nd Measures the distance of returns from the mean. In other words, variance shows how frequently possible Moment returns occur. x -3σ -2σ -1σ μ 1σ 2σ 3σ POSITIVE SKEW NO SKEW NEGATIVE SKEW (returns more likely to be to the (returns equally likely to (returns more likely to be left of the mean) be to the left and to the right to the right of the mean) of the mean) SKEWNESS Characterizes the degree of asymmetry of a distribution y Mode y y Mode 3rd around its mean. In other words, by using skewness investors should be able to better predict whether a Median Median Moment return is more likely to occur to the left or to the right μ of the mean. μ Median μ Mode x x x LEPTOKURTIC MESOKURTIC PLATYKURTIC (high peak) (normal distribution) (flat topped) KURTOSIS y y y 4th Characterizes the relative peakedness or flatness of a distribution. In other words, kurtosis helps investors Moment better predict the likelihood of a given return (or loss). x x x IMAGINING THE Keeping a Portfolio Protected UNIMAGINABLE TRADITIONAL RISK STATISTICS Traditional risk statistics assume that events to the left and right of the mean are equally likely to occur. Extreme For example, traditional risk Extreme Negative Event approaches assume that the Positive Event (Loss) Occurs Here likelihood of an extreme event (Gain) Occurs Here (negative or positive) is very slim. These extreme events are modeled by the tails of the normal distribution. 0.135% 0.135% chance -3σ -2σ -1σ μ 1σ 2σ 3σ chance Value at Risk (VaR) is the maximum loss a fund can expect within a specified holding period using a specified confidence level. It is calculated using Traditional Risk Statistics based on a Normal Distribution, with No Skew, and Mesokurtic Kurtosis. FAT-TAIL RISK STATISTICS The traditional VaR model has come under criticism because, in the real world, events cant be easily modeled on a simple, symmetrical curve (normal distribution). Newer, Fat-Tail VaR models better capture the fact that highly improbable and damaging events do occur, and can occur more frequently than traditional risk statistics assume. Nassim Nicholas Taleb popularized the use of the term “black swan” for these highly improbable events. Notice that the tail is “fatter” to In this example, An asymmetrical curve, based on real-world the left of 95% VaR. This indicates both the Fat-Tail and returns, can more accurately predict the chance that the probability of sustaining 95% normal distributions have that an extreme negative event (loss) or an extreme the same mean and VaR 95% losses has increased. VaR numbers, but the Fat-Tail positive event (gain) may occur. distribution is Leptokurtic and has a left skew. As a result, the Fat-Tail distribution’s Expected Tail Loss (ETL), which is the average of returns that exceed the VaR, more accurately captures a higher downside risk than traditional ETL. Traditional ETL The overstated probability that an extremely positive event (gain) occurs according to the traditional risk approach. Fat-Tail ETL "One single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans. All you need is one single (and, I am told, quite ugly) black bird.” Nassim Nicholas Taleb The Black Swan: The Impact of the Highly Improbable Images are for illustration purposes and are not to scalewww.pertrac.com Office locations and telephone numbers:sales@pertrac.com New York: +1 212.661.6050pertrac.com/blog London: +44 (0)20. 7651.0800 Hong Kong: +1 852.2526.9780 facebook.com/pertrac Tokyo: +81.3.5785.2041 © 2012 PerTrac Reno: +1 775.851.2282 @PerTrac Memphis: +1 901.888.7300