Temp_store

LO 71.4: Evaluate the role of investors in shaping the hedge fund industry.
Historical data on hedge fund performance was difficult to obtain prior to the early 1990s. In early 1994, dramatic losses triggered by a Federal Reserve change in interest rate policy had a large impact on hedge fund performance reporting. This prompted the development of hedge fund databases so that participants could better obtain and analyze hedge fund performance.
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Assets under management have increased 10 times from 1997 to 2010 as the number of funds has quadrupled. There are some hedge funds that do not participate in commercial databases, which impacts aggregate hedge fund performance. Thus, there is selection bias, also known as self-reporting bias, contained in hedge fund databases.
There is evidence that suggests that selection bias in large hedge fund databases is actually small. The average return of funds-of-hedge funds (FOHF), comprised of managers who theoretically invest across all hedge funds, not just funds reported to commercial databases, is highly correlated to the average return of hedge funds in commercial databases.
However, there are still concerns about possible measurement errors and various biases in reported hedge fund returns. The consensus is that hedge fund index returns became increasingly reliable beginning in 1996. Prior to 1996, looking at the period from 1987 to 1996, 27 large hedge funds substantially outperformed the S&P 500 index. The outperformance is high, which is more than enough to account for any measurement biases.
The collapse of Long-Term Capital Management (LTCM) in 1998 was a watershed event in the hedge fund industry. It was a reminder that higher returns are accompanied by higher risk. The LTCM collapse had a much greater effect on hedge fund performance compared to equity performance.
The time period of 2000 to 2001 brought the dot-com bubble collapse. During this period, the hedge fund industry experienced a 20% net asset inflow and there was a major shift in the hedge fund industry structure. Hedge funds outperformed the S&P 500 with half of the S&P 500 standard deviation. As a result, institutional investors poured money into hedge funds.
>From 1999 to 2007, hedge funds assets under management went from $197 billion to $1.39 trillion. Investors in hedge funds thus shifted from exclusively private wealth to institutions, including foundations, endowments, pension funds, and insurance companies. Evidence suggests that these institutional investors were rewarded from 2002 to 2010 with high returns, due in large part to bearing credit and emerging market risks.
Al p h a -Be t a Se pa r a t io n

LO 71.3: Explain the evolution of the hedge fund industry and describe landmark events that precipitated major changes in the development of the industry.

LO 71.2: Explain biases that are commonly found in databases of hedge funds.

LO 71.1: Describe the characteristics of hedge funds and the hedge fund industry, and compare hedge funds with mutual funds.
There are important distinctions between hedge funds and mutual funds. Hedge funds are private, much less regulated investment vehicles, not available to the general public. On the other hand, mutual funds are more structured and regulated. Hedge funds are highly leveraged, and managers obtain profits from both long and short positions. Hedge fund managers tend to take large bets based on perceived relative price discrepancies of assets.
Privacy is a hallmark of hedge funds. There is little transparency in the hedge fund industry because managers do not want their methods copied. A hedge fund charges a fixed management fee plus a healthy share of new profits from the fund, generally around 10- 20%.
Ev o l u t io n o f t h e H e d g e Fu n d In d u s t r y

LO 70.9: Describe and apply performance attribution procedures, including the asset allocation decision, sector and security selection decision, and the aggregate contribution.
William Sharpe introduced the concept of style analysis. From January 1985 to December 1989 he analyzed the returns on Fidelitys Magellan Fund for style and selection bets. His study concluded that 97.3% of the funds returns were explained by style bets (asset allocation), and 2.7% were due to selection bets (individual security selection and market timing). The importance of long-run asset allocation has been well established empirically. These results suggest that the returns to market timing and security selection are minimal at best and at worst insufficient to cover the associated operating expenses and trading costs.
The steps for Sharpes style analysis are as follows: 1. Run a regression of portfolio returns against an exhaustive and mutually exclusive set of
asset class indices:
Rp – bpiRB1 + bp2RB2 + …+ bpix^Bn + ep
where: Rp = return on the managed portfolio Rb- = return on passive benchmark asset class n bp. = sensitivity or exposure of Portfolio P return to passive asset class n return e = random error term
In Sharpes style analysis, the slopes are constrained to be non-negative and to sum to 100%. In that manner, the slopes can be interpreted to be effective allocations of the portfolio across the asset classes.
2. Conduct a performance attribution (return attributable to asset allocation and to
selection): The percent of the performance attributable to asset allocation = R2 (the coefficient
of determination).
The percent of the performance attributable to selection = 1 R2.
The asset allocation attribution equals the difference in returns attributable to active asset allocation decisions of the portfolio manager:
[biRgi + b2RB2 + …+ bnRgJ – Rg
Notice if the slopes (estimated allocations) for the managed portfolio equal those within the benchmark (passive asset allocation), then the asset allocation attribution will be zero.
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The selection attribution equals the difference in returns attributable to superior individual security selection (correct selection of mispriced securities) and sector allocation (correct over and underweighting of sectors within asset classes):
Rp
[bjRgj + b2RB2 + …+ bnRBn] Notice if the manager has no superior selection ability, then portfolio returns earned within each asset class will equal the benchmark asset class returns: RPj = Rgj, and the selection attribution will equal zero. Also, notice that the sum of the two attribution components (asset allocation plus selection) equals the total excess return performance: Rp Rg.
3. Uncover the investment style of the portfolio manager: the regression slopes are used to infer the investment style of the manager. For example, assume the following results are derived:
Rp = 0.75Rl c g + 0.15Rl c v + 0.05Rs c g + 0.05RSCV
where: Rl c g = return on the large cap growth index Rpcy = return on the large cap value index Rsc g = return on the small cap growth index RSCv = return on the small cap value index
The regression results indicate that the manager is pursuing primarily a large cap growth investment style.
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Ke y Co n c e pt s
LO 70.1 The dollar-weighted rate of return is defined as the internal rate of return (IRR) on a portfolio, taking into account all cash inflows and outflows. The beginning value of the account is an inflow as are all deposits into the account. All withdrawals from the account are outflows, as is the ending value.
Time-weighted rate of return measures compound growth. It is the rate at which $ 1 compounds over a specified time horizon. Time-weighting is the process of averaging a set of values over time.
LO 70.2 The Sharpe ratio uses standard deviation (total risk) as the relevant measure of risk. It shows the amount of excess return (over the risk-free rate) earned per unit of total risk.
The Treynor measure is very similar to the Sharpe ratio except that it uses beta (systematic risk) as the measure of risk. It shows the excess return (over the risk-free rate) earned per unit of systematic risk.
Jensens alpha is the difference between the actual return and the return required to compensate for systematic risk. To calculate the measure, we subtract the return calculated by the capital asset pricing model (CAPM) from the account return.
The information ratio is the ratio of the surplus return (in a particular period) to its standard deviation. It indicates the amount of risk undertaken to achieve a certain level of return above the benchmark.
LO 70.3 The M2 measure compares the return earned on the managed portfolio against the market return, after adjusting for differences in standard deviations between the two portfolios.
LO 70.4 A positive alpha produces an indication of superior performance; a negative alpha produces an indication of inferior performance; and zero alpha produces an indication of normal performance matching the benchmark.
LO 70.3 Hedge fund performance evaluation is complicated because: Hedge fund risk is not constant over time (nonlinear risk). Hedge fund holdings are often illiquid (data smoothing). Hedge fund sensitivity with traditional markets increases in times of a market crisis and
decreases in times of market strength.
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LO 70.6 Changes in volatility will likely bias the Sharpe ratio, and produce incorrect conclusions when comparing portfolio performance to a benchmark or index.
LO 70.7
Extending basic return regression models offers a tool to assess superior market timing skills of a portfolio manager. A market timer will include high (low) beta stocks in her portfolio if she expects an up (down) market. If her forecasts are accurate, her portfolio will outperform the benchmark portfolio.
LO 70.8 William Sharpe introduced the concept of style analysis. From January 1985 to December 1989 he analyzed the returns on Fidelitys Magellan Fund for style and selection bets. His study concluded that 97.3% of the funds returns were explained by style bets (asset allocation), and 2.7% were due to selection bets (individual security selection and market timing).
LO 70.9 The importance of long-run asset allocation has been well established empirically. Historical results suggest that the returns to market timing and security selection are minimal at best and at worst insufficient to cover the associated operating expenses and trading costs.
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Topic 70 Cross Reference to GARP Assigned Reading – Bodie, Kane, and Marcus, Chapter 24
Co n c e pt Ch e c k e r s
Use the following data to answer Questions 1 and 2. Assume you purchase a share of stock for $50 at time t = 0 and another share at $65 at time t = 1, and at the end of year 1 and year 2, the stock paid a $2.00 dividend. Also at the end of year 2, you sold both shares for $70 each.
1.
2.
3.
The do liar-weighted rate of return on the investment is: A. 10.77%. B. 15.45%. C. 15.79%. D. 18.02%.
The time-weigh ted rate of return on the investment is: A. 18.04%. B. 18.27%. C. 20.13%. D. 21.83%. The following information is available for funds ABC, RST, JKL, and XYZ:
F und
A nnual Rate o f Return
ABC RST JKL XYZ
15% 18% 25% 11%
Beta 1.25 1.00 1.20 1.36
Volatility
20% 25% 15% 9%
The average risk-free rate was 5%. Rank the funds from best to worst according to their Treynor measure. A. JKL, RST, ABC, XYZ. B. JKL, RST, XYZ, ABC. C. RST, JKL, ABC, XYZ. D. XYZ, ABC, RST, JKL.
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Use the following information to answer Question 4. The following data has been collected to appraise funds A, B, C, and D:
Return Beta Standard deviation
F und A 8.25% 0.91 3.24%
F und B 7.21% 0.84 3.88%
F und C 9.44% 1.02 3.66%
F und D 10.12%
1.34 3.28%
M arket Index
8.60% 1.00 3.55%
The risk-free rate of return for the relevant period was 4%.
4.
5.
Calculate and rank the funds from best to worst using Jensens alpha. A. B, D, A, C. B. A, C, D, B. C. C,A, D, B. D. C, D, A, B.
Sharpes style analysis, used to evaluate an active portfolio managers performance, measures performance relative to: A. a passive benchmark of the same style. B. broad-based market indices. C. D. an average of similar actively managed investment funds.
the performance of an equity index fund.
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Topic 70 Cross Reference to GARP Assigned Reading – Bodie, Kane, and Marcus, Chapter 24
Co n c e pt Ch e c k e r An s w e r s 1. D One way to do this problem is to set up the cash flows so that the PV of inflows = PV of
outflows and then to plug in each of the multiple choices.
50 + 65 / (1 + IRR) = 2 / (1 + IRR) + 144 / (1 + IRR)2 -> IRR = 18.02%
Alternatively, on your financial calculator, solve for IRR: -50 – + IRR 6 5 -2 ———- b 1 + IRR
2(70 + 2) (l + IRR)2
Calculating Dollar- Weighted Return With the TI Business Analyst II Plus
Key Strokes [CF] [2nd] [CLR WORK] 50 [+/-] [ENTER] [4] 63 [+/-] [ENTER] [4-] [4-] 144 [ENTER] [IRR] [CPT] Explanation Clear CF Memory Registers Initial cash inflow Period 1 cash inflow Period 2 cash outflow Calculate IRR
Display CFO = 0.00000 CFO = -50.00000 C01 =-63.00000 C02 = 144.00000 IRR = 18.02210
2. D HPR, = (65 + 2) / 50 – 1= 34%, HPR, = (140 + 4) / 130 – 1 = 10.77%
time-weighted return = [(1.34)(1.1077)]0’5 1 = 21.83%.
3. A Treynor measures:
^ n T ARr = —————= 0.08 = 8 a b c
0.15-0.05
_

L
2
3
0.18-0.05 _ Tr st = ————- – = 0.13 = 13 R S T
1.00
0.25-0.05
1.20
0.11-0.05
1.36
= 0.1667 = 16.7
= 0.0441 = 4.4
The following table summarizes the results:
Fund ABC RST JKL XYZ
Treynor Measure
Rank
8.00% 13.00% 16.67% 4.41%
3 2 1 4
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4. C CAPM Returns:
Ra = 4 + 0.91(8.6-4) = 8.19% RB = 4 + 0.84(8.6 – 4) = 7.86% Rc = 4 + 1.02(8.6-4) = 8.69% Rd = 4+ 1.34(8.6-4) = 10.16%
F und A
F und B
F und C
F und D
Alpha
8.25% -8.19%
= +0.06
Ranking
2
7.21% -7.86%
9.44% – 8.69%
= -0.65%
4
= +0.75%
1
10.12% – 10.16%
= -0.04%
3
5. A Sharpes style analysis measures performance relative to a passive benchmark of the same
style.
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The following is a review of the Risk Management and Investment Management principles designed to address the learning objectives set forth by GARP. This topic is also covered in:
H e d g e Fu n d s
Ex a m Fo c u s
Topic 71
The topic examines two decades of hedge fund performance. Significant events that shaped the hedge fund industry are discussed, including the growth of institutional investments. Different hedge fund strategies are explained, along with the continuing growth of assets under management. Performance is analyzed to see if the rewards justify the risks, and performance is compared with the broad equity markets. The performance of top fund managers is also compared to the performance across the hedge fund industry.
C h a r a c t e r is t ic s o f H e d g e Fu n d s

LO 70.8: Describe style analysis.

LO 70.7: Describe techniques to measure the market timing ability of fund managers with a regression and with a call option model, and compute return due to market timing.
Measuring Market Timing with Regression
Extending basic return regression models offers a tool to assess superior market timing skills of a portfolio manager. A market timer will include high (low) beta stocks in her portfolio if she expects an up (down) market. If her forecasts are accurate, her portfolio will outperform the benchmark portfolio. Using a market timing regression model, we can empirically test whether there is evidence of superior market timing skills exhibited by the portfolio manager. The regression equation used for this test is as follows:
Rp – RF = a + Pp(Rm – RF) + Mp(RM – RF)D + ep
In this equation, D is a dummy variable that is assigned a value of 0 for down markets (i.e., when Rm Rp). Mp is the difference between the up market and down market betas and will be positive for a successful market timer. In a bear market, beta is simply equal to pp. In a bull market, beta is equal to pp + Mp. Empirical evidence of mutual fund return data suggests that Mp is actual negative for most funds. Thus, researchers have concluded that fund managers exhibit little, if any, ability to correctly time the market.
Measuring Market Timing with a Call Option Model
Consider an investor who has 100% perfect market forecasting ability and holds a portfolio allocated either 100% to Treasury bills or 100% to the S&P 500 equity market index, depending on the forecast performance of the S&P 500 versus the Treasury bill return. The investors portfolio will be:
100% invested in the S&P 500 if E(RM) > 100% invested in Treasury bills if E(RM) r f R F
If the market rises by less than the risk-free rate, the call option has no value, but the risk- free asset will still return Rp. Therefore, the down-market scenario return for the calls plus bills portfolio is:
Rp if Rm < Rp
In summary, the returns to the calls plus bills portfolio are identical to the 100% perfect foresight returns. Therefore, the value or appropriate fee for perfect foresight should equal the price of the call option on the market index.
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St y l e An a l y s is

LO 70.6: Explain how changes in portfolio risk levels can affect the use of the Sharpe ratio to measure performance.
The Sharpe ratio is useful when evaluating the portfolio performance of a passive investment strategy, where risk and return characteristics are relatively constant over time. However, the application of the Sharpe ratio is challenged when assessing the performance of active investment strategies, where risk and return characteristics are more dynamic. Changes in volatility will likely bias the Sharpe ratio, and produce incorrect conclusions when comparing portfolio performance to a benchmark or index.
Take for example a low-risk portfolio with an alpha return of 1 % and a standard deviation of 3%. The manager implements this strategy for one-year, producing quarterly returns of
2%, 4%, 2%, and 4%. The Sharpe ratio for this portfolio is calculated as: 1% / 3% = 0.3333. If the market index has a Sharpe ratio of 0.3, we would conclude that this portfolio has superior risk-adj usted performance. In the following year, the portfolio manager decides to switch to a high-risk strategy. The alpha return and risk correspondingly increase to 5% and 15%, respectively. For the second year, quarterly returns were 10%, 20%, 10%, and 20%. The Sharpe ratio in this case is still 0.3333 (= 5% / 15%), which still indicates superior performance compared to the market index. However, if the Sharpe ratio is evaluated over the two-year time frame, considering both the low-risk and high-risk strategies, the measure will drop to 0.2727 since average excess return over both years was 3% with volatility of 11%. The lower Sharpe ratio now suggests underperformance relative to the market index.
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Topic 70 Cross Reference to GARP Assigned Reading – Bodie, Kane, and Marcus, Chapter 24
In this example, the Sharpe ratio was biased downward due to the perceived increase in risk in portfolio returns. In isolation, both the low-risk and high-risk strategies produced higher Sharpe ratios than the market index. However, when analyzed together, the Sharpe ratio suggests that the portfolio excess returns are inferior to the market. Therefore, it is important to consider changes in portfolio composition when using performance measures, as dynamic risk levels can lead to incorrect ranking conclusions.
M e a s u r in g M a r k e t Tim in g Ab il it y

LO 70.5: Explain the difficulties in measuring the performance of hedge funds.
Long-short hedge funds are often used to complement an investors well-diversified portfolio. For example, the investor might allocate funds to a passively managed index fund and an actively managed long-short hedge fund. The hedge fund is designed to provide positive alpha with zero beta to the investors overall composite portfolio. The hedge fund creates portable alpha in the sense that the alpha does not depend on the performance of the broad market and can be ported to any existing portfolio. Because the long-short fund is market-neutral, the alpha may be generated outside the investors desired asset class mix.
Unfortunately, hedge fund performance evaluation is complicated because:
Fledge fund risk is not constant over time (nonlinear risk). Hedge fund holdings are often illiquid (data smoothing). Hedge fund sensitivity with traditional markets increases in times of a market crisis and
decreases in times of market strength.
The latter problem necessitates the use of estimated prices for hedge fund holdings. The values of the hedge funds, therefore, are not transactions-based. The estimation process unduly smoothes the hedge fund values, inducing serial correlation into any statistical examination of the data.
P e r f o r m a n c e Ev a l u a t io n W it h D y n a m ic Ris k Le v e l s

LO 70.4: Determine the statistical significance of a performance measure using standard error and the t-statistic.
Alpha (a) plays a critical role in determining portfolio performance. A positive alpha produces an indication of superior performance; a negative alpha produces an indication of inferior performance; and zero alpha produces an indication of normal performance matching the benchmark. The performance indicated by alpha, however, could be a result of luck and not skill. In order to assess a managers ability to generate alpha, we conduct a r-test under the following hypotheses:
Null (i70): True alpha is zero.
Alternative ( N f : True alpha is not zero.
_ a 0 ~~ ct/V n
where: i = alpha estimate O o = alpha estimate volatility N = sample number of observations standard error of alpha estimate = a / Vn
In order to compute the r-statistic, we will need to know the alpha estimate, the sample number of observations, and the alpha estimate of volatility. From the volatility and sample size estimates, we can compute the standard error of the alpha estimate, which is shown in the denominator of the r-statistic calculation.
At a 95% confidence level (5% significance level) we reject the null hypothesis if we estimate a r-value of 2 or larger. That is, the probability of observing such a large estimated alpha by chance is only 5%, assuming returns are normally distributed.
Professors Note: Using a t-value o f 2 is a general test o f statistical significance. >From the F R M Part I curriculum, we know that the actual t-value with a 95% confidence level given a large sample size is 1.96.
If we assume an excess (alpha) return of 0.09% and a standard error of the alpha of 0.093%, the r-statistic would be equal to 0.97 (t = 0.09% / 0.093%); therefore, we fail to reject H Q and conclude that there is no evidence of superior (or inferior) performance.
Professors Note: Using statistical inference when evaluating performance is extremely challenging in practice. By the tim e you are reasonably confident that a managers returns are in fa ct due to skill, the manager may have moved elsewhere.
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M e a s u r in g H e d g e Fu n d P e r f o r m a n c e