The jump-start effect in return series of long/short equity hedge funds
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DOI: 10.1057/jdhf.2010.14
- Cite this article as:
- Viebig, J. & Poddig, T. J Deriv Hedge Funds (2010) 16: 191. doi:10.1057/jdhf.2010.14
Abstract
This study complements the literature on possible misreporting by hedge funds. Kernel density estimation shows that the likelihood of observing positive outliers in the first 3-month period is significantly larger than the likelihood of observing positive outliers in each of the following non-overlapping 3-month periods at the 99 per cent level of confidence. The outliers do not disappear if we control for systematic risks. The results presented in this article suggest that the average upward bias during the incubation period reported in previous studies may partly stem from hedge funds, actively jump-starting newly launched funds. Large positive outliers during the incubation period can result from both legal and illegal trading behavior.
Keywords
hedge funds risk factors regulationINTRODUCTION
Getmansky et al,^{1} Agarwal et al^{2} and Bollen and Pool^{3, 4} present empirical evidence suggesting that a widespread misreporting phenomenon may exist in the widely unregulated hedge fund industry. Agarwal et al^{2} argue that hedge fund investors face a principal-agent conflict like shareholders of corporate firms. Hedge funds only voluntarily submit return information to databases. Most hedge funds do not disclose their holdings to investors. As a result, hedge fund investors make investment decisions under incomplete and asymmetric information. High performance-linked incentive fees, typically 20 per cent of the annual fund performance, tend to align interests between investors and hedge fund managers. However, high incentive fees may not only motivate managers to act on behalf of their investors, but may also induce some managers to misreport returns in order to earn higher compensation.
Hedge fund managers have an interest to smooth returns in order to attract and retain investors. Return smoothing behavior leads to lower volatility and higher Sharpe ratios. Getmansky et al^{1} present empirical evidence that hedge fund returns are often highly serially correlated, and argue that the high positive autocorrelation is due primarily to illiquidity and smoothed returns. Agarwal et al^{2} observe that hedge fund returns during December are significantly higher than hedge fund returns during the rest of the year. They argue that hedge funds inflate December returns by underreporting returns earlier in the year and by borrowing from January returns in the next year. The latter can be achieved, for example, by placing large buy orders in illiquid securities at the end of December to artificially inflate prices. Bollen and Pool^{3} find that return series of hedge funds exhibit a high degree of serial correlation and argue that hedge funds are possibly engaged in fraudulent return smoothing behavior. They show empirically that high serial correlation is a leading indicator for fraud. Funds investigated for fraud by the SEC more likely exhibit higher positive serial correlation than other funds. Bollen and Pool^{4} observe that the number of small gains significantly exceeds the number of small losses reported by hedge funds. They argue that the discontinuity in the pooled distribution of hedge fund returns may result from hedge funds that avoid reporting losses in order to attract and retain investors.
Our findings complement the literature on possible misreporting by hedge funds. In this study, we analyze extreme realizations (outliers) in return series of long/short equity hedge funds. Analyzing the performance of 657 long/short equity hedge funds, we detect a significant amount of large positive outliers in the initial phase of their existence, an empirical measurable phenomenon for which we coined the term ‘jump-start effect’.
We develop the hypothesis, which we test in this article as follows. Our basic assumption is that a principal-agent conflict exists between investors and hedge fund firms and that the agency dilemma is most severe in the first few months after a new hedge fund is launched mainly for two reasons. First, hedge funds are often seeded with capital coming from the pockets of the funds’ managers, their friends and families, creating an incentive for the seed capital providers to allocate profitable trades to newly launched funds. Second, newly launched hedge funds compete with existing funds. Hedge funds that do not report outstanding returns in the first few months of their existence do not attract capital, do not break-even and therefore risk to go out of business sooner or later. We assume that most managers of newly launched hedge funds do not seek to smooth returns, but try to generate extraordinarily high returns to attract investors. We therefore focus on the analysis of outliers instead of mean returns and standard deviations of returns.
As hedge fund firms have strong incentives to report extraordinarily high returns during the incubation period, we hypothesize that some hedge fund managers deliberately jump-start newly launched funds. If hedge fund managers jump-start newly launched hedge funds during the incubation period, positive outliers occur more often in initial periods than in later periods. This hypothesis is testable. We use explorative data analysis (EDA) and multi-factor models to define unconditional and conditional outliers and apply kernel density estimation to examine whether the concentration of positive outliers during the incubation period is significant at the 99 per cent level of confidence.
One possible explanation for extraordinarily high returns, or positive outliers, during the incubation period is that factor returns are higher during the incubation period than in later periods. We conduct a detailed investigation of the possibility that risk factors explain the jump-start effect. Among others, Fung and Hsieh^{5, 6, 7, 8, 9} and Mitchell and Pulvino^{10} show that hedge funds exhibit significant exposures to systematic risks. Fung and Hsieh^{9} apply a 3-factor model proposed by Fama and French^{11, 12, 13, 14} to explain the variation in returns of long/short equity hedge fund indices. We apply the Fama and French model to a much larger sample of individual long/short equity funds than Fung and Hsieh.^{9} In addition, we apply a 5-factor model similar to the model proposed by Kuenzi and Shi^{15} and run rolling window regressions to neutralize the systematic risk of individual long/short equity funds. In this study, we concentrate on long/short equity hedge funds, as previous studies suggest that risk characteristics of hedge funds are style-specific.^{5, 6, 7, 8, 9, 10} Applying risk factor models, we show that the jump-start bias does not disappear if we control for systematic risks of long/short equity hedge funds.
It has been well-documented that hedge fund returns are severely upward biased in the first year of existence by a practice called backfilling. Hedge funds typically start reporting returns to databases after they have established a track record for several months. Data vendors usually backfill historical returns when they include a new hedge fund to their database. Unlike mutual funds, hedge funds only voluntarily report return information to investors and data vendors. It is reasonable to assume that hedge funds with good track records are more likely to report return information than do bad performing competitors. This creates the possibility of a backfilling bias. Previous researchers argue that the practice of backfilling may result in an upward bias for hedge funds in the first year of existence and recommend excluding the first 12 months of returns when analyzing hedge fund return series.^{16, 17} The backfilling bias in hedge fund databases has first been recognized by Park^{18} in his doctoral dissertation. Fung and Hsieh^{16} find that annual returns of hedge funds in the first year of existence are on average 1.4 per cent higher than hedge fund returns in subsequent years. Edwards and Caglayan^{17} estimate an instant history bias of around 1.2 per cent per year for hedge funds. Malkiel and Saha^{19} analyze the TASS database, and show that the difference between backfilled and contemporaneously reported returns was highly significant in the 1995–2003 period. They find that backfilled returns exceed not backfilled returns on average by more than 5 per cent per annum.
The results presented in this article suggest that the average upward bias during the incubation period reported in previous studies can partly be explained by a large, unusual concentration of positive outliers during the incubation period. The size and the concentration of the outliers during the first 3-month period are highly unusual. Large positive outliers during the incubation period can result from both legal and illegal trading behavior. It is possible that some hedge funds allocate profitable trades to newly launched hedge funds to purposefully jump-start these funds.
The remainder of this article is structured as follows. The data used in this article are described in the next section. The subsequent section discusses the concentration of unconditional outliers in return series of long/short equity funds during the incubation period. Kernel density estimation is used to determine whether the results are significant. In the penultimate section, we ask whether the high concentration of positive outliers disappears when controlling for systematic risks. The final section concludes.
DATA DESCRIPTION
In this article, we analyze the likelihood of observing positive outliers in return series of long/short equity hedge funds. Unfortunately, no universally accepted definition of the term ‘long/short equity hedge fund’ exists today. Data vendors and academics use different standards to classify hedge fund strategies. We obtained our data directly from MSCI.
Unlike other data vendors, MSCI uses multiple characteristics to classify hedge funds. According to MSCI, the primary characteristics of hedge funds are the ‘investment process’, the ‘asset class’ and the ‘geography’ of the funds. MSCI subdivides the process group ‘Security Selection’ into four different processes: ‘Long Bias’, ‘No Bias’, ‘Short Bias’ and ‘Variable Bias’. Other data vendors such as TASS differentiate among ‘Long/Short Equity Hedge’, ‘Equity Market Neutral’ and ‘Dedicated Short Bias’ funds. We define ‘Long/Short Equity Hedge Funds’ as those funds that belong to the MSCI asset class ‘Equity’ and the MSCI process ‘Long Bias’. As previous research shows that the sources of systematic risk differ among hedge fund strategies, we concentrate on one single hedge fund strategy: long/short equity. Measured by both the number of funds and assets under management, long/short equity represents the largest segment in the hedge fund universe.
The MSCI hedge fund database contains both surviving funds and defunct hedge funds, which stopped reporting data to MSCI. At the end of January 2006, the MSCI hedge fund database contains 466 surviving and 191 defunct long/short equity hedge funds.
Gregoriou^{20} calculates the median survival lifetime of hedge funds of 5.5 years. The median survival lifetime of our universe of long/short equity hedge funds is 54 months. Although EDA, which is mostly concerned with data visualization, can be applied to explore short time series, regression analysis and statistical tests require a sufficient amount of data. When applying EDA, we exclude only 11 return series with less than three data points from our analysis, which reduces our original universe from 657 to 646 funds (‘Sample A’). When performing regression analysis or statistical tests, we exclude an additional 181 return series with less than 36 monthly return observations and 26 return series starting before May 1992 from our universe.^{21} This reduces our original universe further from 646 to 450 long/short equity hedge funds (‘Sample B’).
UNCONDITIONAL OUTLIERS
Empirical evidence suggests that the likelihood of outliers in return series of hedge funds is substantially higher than the normal distribution suggests. Brooks and Kat,^{22} Kat and Lu,^{23} Kat,^{24} Lamm,^{25} Brulhart and Klein^{26} and Eling^{27} find that hedge funds exhibit a significant degree of negative skewness and positive excess kurtosis.
Statistical tests confirm that hedge fund returns are not normally distributed. The JB test discussed in Jarque and Bera^{28} measures departure from normality. Eling,^{27} for example, calculates JB statistics for all strategy indices provided by CSFB Tremont over the January 1994–December 2004 period, and concludes that the return distribution of the CSFB Tremont Long/Short Equity index differs from normality at the 1 per cent level of significance. Unlike Eling,^{27} we perform JB tests for 450 individual long/short equity funds instead of hedge funds indices. We test the null hypothesis that the return series come from a normal distribution.
Jarque–Bera test results and descriptive statistics
Level of significance | Null hypothesis rejected: number of funds | Total number of funds | Null hypothesis rejected: in % |
---|---|---|---|
(a) Jarque–Bera test results | |||
0.01 | 177 | 450 | 39.3 |
0.05 | 247 | 450 | 54.9 |
0.10 | 275 | 450 | 61.1 |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
---|---|---|---|---|---|---|---|
First quartile | Second quartile (median) | Third quartile | Fourth quartile (max) | Mean | Interquartile range | Standard deviation | |
(b) Descriptive statistics | |||||||
Mean | 0.0052 | 0.0091 | 0.0136 | 0.0753 | 0.0187 | 0.0084 | 0.0081 |
Standard deviation | 0.0295 | 0.0418 | 0.0602 | 0.1695 | 0.0573 | 0.0307 | 0.0276 |
Skewness | −0.2159 | 0.1886 | 0.7143 | 4.4433 | 0.3107 | 0.9302 | 0.9339 |
Kurtosis | 3.2490 | 4.1250 | 5.8095 | 31.7591 | 5.3968 | 2.5605 | 3.8315 |
Likelihoods of observing positive and negative unconditional outliers
Non-overlapping 3-month period | Likelihood of observing a positive unconditional outlier (A) (%) | Likelihood of observing a negative unconditional outlier (B) (%) | Ratio (A)/(B) |
---|---|---|---|
1 | 6.35 | 4.39 | 1.4 |
2 | 4.88 | 4.98 | 1.0 |
3 | 4.69 | 5.95 | 0.8 |
4 | 4.30 | 5.91 | 0.7 |
5 | 4.19 | 5.40 | 0.8 |
6 | 3.11 | 4.41 | 0.7 |
7 | 1.86 | 5.64 | 0.3 |
8 | 2.38 | 4.35 | 0.5 |
9 | 3.75 | 4.30 | 0.9 |
10 | 3.25 | 5.55 | 0.6 |
11 | 2.66 | 4.99 | 0.5 |
12 | 3.31 | 5.04 | 0.7 |
13 | 3.24 | 5.12 | 0.6 |
14 | 2.52 | 4.58 | 0.6 |
15 | 2.17 | 4.34 | 0.5 |
16 | 2.38 | 4.42 | 0.5 |
17 | 2.41 | 4.64 | 0.5 |
18 | 2.06 | 4.72 | 0.4 |
19 | 0.95 | 5.05 | 0.2 |
20 | 1.36 | 4.07 | 0.3 |
The likelihood of observing an outlier during the initial 3-month period exceeds the median likelihood of all non-overlapping 3-month periods by the factor of 3.09. The high outlier likelihood during the initial 3-month period is clearly not representative for the entire lifetime of long/short equity funds.
Given the estimated cumulative probability density function L_{1} shown in Figure 2, the probability p of observing an outlier likelihood equal to or greater than 6.35 per cent in the first 3-month period is less than 1 per cent. We conclude that the observed outlier likelihood l_{1}=6.35 per cent is significantly larger than L_{1} at the 99 per cent level of confidence. It is worth noting that l_{1} is also significantly larger than L_{1} at the 99.9 per cent level of confidence.
In order to validate our results, we introduce an alternative outlier definition and conduct a second test to check whether we can confirm our finding that the number of outliers in the first 3 months of existence is significantly larger than the number of outliers in the following months. Our alternative outlier definition is widely used by statisticians: We assume that an outlier is present if a return observation lies outside of a ϕ per cent confidence interval of an estimated density function. For each fund included in Sample B, we separate the respective return series in two parts. The first part ζ_{1} contains the first three monthly returns, ζ_{1}=[r_{1}, r_{2}, r_{3}], the second part ζ_{2} includes the remaining returns ζ_{2}=[r_{4}, …, r_{n}].
Applying kernel density estimation with a Gaussian kernel and an optimal bandwidth, we estimate the unknown density function f_{n}(r_{i}∣ζ_{2}). We then count how many returns included in ζ_{1} are larger than the ϕ=99 per cent (97.5 per cent, 95 per cent) quantile q(f_{n},ϕ) of the estimated distribution f_{n}(r_{i}∣ζ_{2}). We find that the number of returns included in ζ_{1}, which are larger than q(f_{n}, ϕ) is 36 (74, 116), which is much larger than the expected number of 12.75 (31.88, 63.75) given f_{n}(r_{i}∣ζ_{2}). Again, we conclude that the jump-start effect in return series of long/short equity funds is significant at the 99 per cent (97.5 per cent, 95 per cent) level of significance. We examine whether our conclusion holds if empirical densities instead of kernel density estimates are applied. Analyzing empirical densities, we count 51 (85, 140) observed outliers at the 99 per cent (97.5 per cent, 95 per cent) level of confidence, far exceeding the expected number of 12.75 (31.88, 63.75). The conclusion that the jump-start effect is significant holds at even higher levels of confidence if empirical densities are applied instead of kernel density estimates, as kernel density estimation tends to overestimate the density at the tails of the distribution.
In addition, we calculate the differences between subsequent returns to remove the systematic return component and possible first-order autocorrelation in the return data. Applying return differences instead of returns also reduces the backfilling bias reported in previous papers. We will discuss the impact of the backfilling bias on the jump-start effect in the last section in more detail. Applying return differences, however, also reduces the jump-start effect if two outliers follow each other in adjacent periods. The number of observed outliers 42 (65, 91) is higher than the number of expected outliers 12.6 (31.5, 63) at the 99 per cent (97.5 per cent, 95 per cent) level of confidence if the difference method is applied. We conclude that the jump-start effect is significant if unconditional outliers are calculated on the basis of both returns and return differences. In the next section, we examine whether the jump-start effect disappears if we analyze conditional outliers instead of unconditional outliers.
CONDITIONAL OUTLIERS
In this section, we apply multi-factor models to neutralize systematic risks of long/short equity hedge funds, analyze conditional outliers instead of unconditional outliers and ask whether the likelihood of observing positive conditional outliers in the first 3-month period is significantly larger than the likelihood of observing positive conditional outliers in each of the subsequent non-overlapping 3-month periods after adjusting for systematic risks. Previous research suggests that systematic risks explain some of the variation in hedge fund returns. Fung and Hsieh^{5} apply an asset class model proposed by Sharpe^{31} to analyze 3327 mutual funds and 409 hedge funds and find that the returns of hedge funds have lower correlations to standard asset classes than mutual funds. The explanatory power of the model increases when style factors are introduced capturing the strategy-specific risk of hedge funds.
Previous researchers have identified strategy-specific style factors for different hedge funds strategies. Fung and Hsieh,^{6} for example, demonstrate that trend-following strategies generate high returns when markets increase or decrease substantially, and suggest that their returns can be replicated by lookback straddles. Mitchell and Pulvino^{10} find that merger arbitrage returns are generally uncorrelated with the overall equity market but positively correlated with equity market returns when the market declines severely.
We apply the Fama and French model to neutralize the systematic risk of individual long/short equity hedge funds instead of equity hedge fund indices. To derive the first explanatory variable, F_{1}, the 1-month US LIBOR is subtracted from the monthly returns on the S&P500 index. The second factor, F_{2}, is calculated by subtracting the monthly returns of the MSCI US Large Cap 300 index from the MSCI US Small Cap 1750 index. The third explanatory variable, F_{3}, is estimated as the difference between the monthly returns on the MSCI US Value index and the returns on the MSCI US Growth index.^{32}
Summary: Regression statistics
β_{p,1}(E(r_{m})−r_{f}) | β_{p,2}(SMB) | β_{p,3}(HML) | R^{2}(model) | F-statistic (model) | |
---|---|---|---|---|---|
1st Quartile | 0.20 | 0.19 | −0.25 | 0.22 | 6.46 |
2nd Quartile (MEDIAN) | 0.37 | 0.34 | −0.01 | 0.38 | 12.45 |
3rd Quartile | 0.61 | 0.56 | 0.18 | 0.53 | 26.57 |
4th Quartile (MAX) | 2.35 | 2.20 | 1.33 | 0.87 | 181.29 |
Mean | 0.45 | 0.41 | −0.03 | 0.39 | 21.53 |
Standard deviation | 0.36 | 0.34 | 0.38 | 0.20 | 26.43 |
Statistically significant (95% Confidence) | |||||
No. of funds | 324 | 293 | 191 | — | — |
In% | 72 | 65 | 42 | — | — |
Positive and negative conditional outlier likelihoods
Non-overlapping 3-month period | Likelihood to observe a positive conditional outlier (A) (%) | Likelihood to observe a negative conditional outlier (B) (%) | Ratio (A)/(B) |
---|---|---|---|
1 | 9.04 | 3.11 | 2.9 |
2 | 6.89 | 2.59 | 2.7 |
3 | 6.52 | 3.11 | 2.1 |
4 | 5.19 | 2.44 | 2.1 |
5 | 4.52 | 2.59 | 1.7 |
6 | 4.37 | 2.67 | 1.6 |
7 | 3.04 | 2.44 | 1.2 |
8 | 3.85 | 2.96 | 1.3 |
9 | 4.74 | 1.63 | 2.9 |
10 | 2.96 | 2.00 | 1.5 |
11 | 3.33 | 2.74 | 1.2 |
12 | 4.30 | 2.22 | 1.9 |
13 | 3.07 | 2.46 | 1.2 |
14 | 2.86 | 2.61 | 1.1 |
15 | 2.59 | 1.90 | 1.4 |
16 | 2.57 | 2.39 | 1.1 |
17 | 1.91 | 2.22 | 0.9 |
18 | 2.03 | 2.99 | 0.7 |
19 | 1.15 | 2.87 | 0.4 |
20 | 1.37 | 1.99 | 0.7 |
Median coefficients of determination (rolling window regressions)
Window length | 18 months | 24 months | 30 months |
---|---|---|---|
Fama/French 3-factor model | |||
1st Quartile | 0.35 | 0.31 | 0.27 |
2nd Quartile (MEDIAN) | 0.50 | 0.47 | 0.44 |
3rd Quartile | 0.65 | 0.63 | 0.62 |
4th Quartile (MAX) | 0.89 | 0.90 | 0.90 |
Mean | 0.50 | 0.47 | 0.44 |
Standard deviation | 0.19 | 0.21 | 0.22 |
5-factor model | |||
1st Quartile | 0.47 | 0.35 | 0.35 |
2nd Quartile (MEDIAN) | 0.61 | 0.53 | 0.53 |
3rd Quartile | 0.73 | 0.67 | 0.67 |
4th Quartile (MAX) | 0.93 | 0.91 | 0.91 |
Mean | 0.60 | 0.50 | 0.50 |
Standard deviation | 0.17 | 0.21 | 0.21 |
To conclude our analysis, we apply kernel density estimation to construct probability density estimates. Applying the methodology discussed in the previous section, we find that the probability of observing a positive conditional likelihood equal to or greater than the positive conditional likelihood in the first non-overlapping 3-month period is less than 1 per cent for all six regression models. We conclude that the jump-start effect is significant at the 99 per cent level of confidence.
To assess the robustness of these findings, we apply the alternative outlier definition discussed in the previous section assuming that a conditional outlier is present if a residual return observation lies outside of an ϕ per cent confidence interval of an estimated density function. Unlike in the previous section, we now separate the time series of residual returns instead of absolute returns in two parts. We apply the two static factor models and the six rolling window factor models discussed above to determine residual returns. The first part ζ_{1} includes the first three monthly residuals, ζ_{1}=[res_{1}, res_{2}, res_{3}], the second part ζ_{2}=[res_{4}, …, res_{n}] contains the remaining residuals. Kernel density estimation is then used to estimate the unknown density function f_{n}(res_{i}∣ζ_{2}).
Observed and expected positive conditional outliers
Model | Confidence level (%) | Observed number of PCO | Expected number of PCO | Observed number of PCO – Difference method | Expected number of PCO – Difference method |
---|---|---|---|---|---|
Fama and French model (Static)^{**} | 99.0 | 52^{**} | 12.75 | 33^{**} | 12.6 |
97.5 | 84^{**} | 31.88 | 54^{**} | 31.5 | |
95.0 | 128^{**} | 63.75 | 93^{**} | 63.0 | |
5-factor model (Static)^{**} | 99.0 | 58^{**} | 12.75 | 30^{**} | 12.6 |
97.5 | 85^{**} | 31.88 | 51^{**} | 31.5 | |
95.0 | 141^{**} | 63.75 | 92^{**} | 63.0 | |
Fama and French model (rolling window, 18 months)^{**} | 99.0 | 32^{**} | 8.67 | 22^{**} | 8.43 |
97.5 | 46^{**} | 21.68 | 34^{**} | 21.08 | |
95.0 | 72^{**} | 43.35 | 56^{**} | 42.15 | |
Fama and French model (rolling window, 24 months)^{**} | 99.0 | 20^{**} | 7.44 | 13^{**} | 7.08 |
97.5 | 32^{**} | 18.60 | 24^{**} | 17.70 | |
95.0 | 49^{**} | 37.20 | 45^{**} | 35.40 | |
Fama and French model (rolling window, 36 months)^{**} | 99.0 | 15^{**} | 5.40 | 7^{**} | 5.22 |
97.5 | 19^{**} | 13.50 | 15^{**} | 13.05 | |
95.0 | 32^{**} | 27.00 | 25 | 26.10 | |
5-factor model (rolling window, 18 months)^{**} | 99.0 | 26^{**} | 8.67 | 21^{**} | 8.43 |
97.5 | 43^{**} | 21.68 | 40^{**} | 21.08 | |
95.0 | 79^{**} | 43.35 | 64^{**} | 42.15 | |
5-factor model (rolling window, 24 months)^{**} | 99.0 | 17^{**} | 7.44 | 9^{**} | 7.08 |
97.5 | 36^{**} | 18.60 | 23^{**} | 17.70 | |
95.0 | 59^{**} | 37.20 | 48^{**} | 35.40 | |
5-factor model (rolling window, 36 months)^{**} | 99.0 | 12^{**} | 5.40 | 8^{**} | 5.22 |
97.5 | 19^{**} | 13.50 | 15^{**} | 13.05 | |
95.0 | 41^{**} | 27.00 | 24 | 26.10 |
In addition, we calculate the differences between subsequent residual returns to remove any remaining systematic return component and possible first-order autocorrelation in the return data. Applying residual return differences instead of residual returns also reduces the backfilling bias reported in previous papers. Table 6 shows that the jump-start effect does not disappear if conditional outliers are calculated on the basis of return differences instead of returns. Applying the difference method, we find that the number of positive conditional outliers in the first 3-month period is larger than the estimated number of positive conditional outliers for all models at almost all confidence levels.
Our main conclusion that the jump-start effect in return series of long/short equity funds is substantial is robust in at least three respects: First, the jump-start effect does not disappear if we adjust for well-known sources of risk. Second, the jump-start effect is significant for different outlier definitions. Third, the jump-start effect also does not disappear if outliers are calculated on the basis of return differences instead of returns.
CONCLUDING REMARKS
Time series of long/short equity funds are significantly distorted by large positive outliers during the initial 3-month period. The likelihood of observing positive unconditional outliers during the first 3-month period is significantly larger than the likelihood of observing positive outliers during a later non-overlapping 3-month period at the 99 per cent level of confidence. Even after adjusting for well-known sources of systematic risks of long/short equity strategies, the likelihood of observing positive conditional outliers in the first 3-month period is significantly larger than the likelihood of observing positive conditional outliers in any of the following non-overlapping 3-month periods at the 99 per cent level of confidence. In this study, we concentrate on long/short equity hedge funds, as the risks of hedge funds are strategy-specific. Future research is required to analyze whether the jump-start bias can be observed in return series of other hedge fund strategies.
During the incubation period, newly established hedge funds often trade on money coming from the pockets of the funds’ managers, their friends and relatives. Insiders providing seed capital can benefit at the expense of outside investors by allocating successful trades to recently launched funds and loss making trades to larger more established funds. High performance-linked incentive fees may not only motivate managers to act on behalf of their investors, but may also induce some managers to misreport returns in order to earn higher compensation. While we do not claim that anyone is acting illegally, we point out that illegal behavior could possibly be an explanation for the large number of positive outliers in return series of long/short equity hedge funds during the incubation period.
Hedge funds typically start reporting returns after they have established a track record for several months. Previous researchers have argued that the average upward bias in hedge fund returns during the first 12-month period results from data vendors backfilling historical return data to their databases. This study suggests that contemporaneous and backfilled returns included in hedge fund databases exhibit a large number of extreme outliers in the first 3-month period, which cannot be explained by common sources of hedge fund risks. The practice of backfilling is an important but not necessarily the only reason for the average upward bias in hedge fund returns during the first 12 months. The practice of backfilling does not explain how hedge funds generate extreme positive returns during the first 3-month period, which often deviate several standard deviations from the mean.
Without more transparency, investors, regulators and academics cannot analyze whether the statistically significant concentration of positive outliers during the first 3-month period stems from legal or illegal trading behavior. Taken together, the discontinuity in the pooled distribution of hedge funds, the December spike, the return smoothing effect (reported in previous studies), and the jump-start effect (documented in this study) indicate that a widespread misreporting phenomenon may exist in the widely unregulated hedge fund industry.