ssrn-id1809570

Electronic copy available at: http://ssrn.com/abstract=1809570

Hedge Fund Biases After the Financial Crisis

Dieter G. Kaiser*

Florian Haberfelner†

First Draft: December 3, 2010

Latest Revision: April 13, 2011

ABSTRACT In this paper, we explore how hedge fund database biases developed during the 2007-2009 financial crisis. Our sample consists of 8,935 hedge funds from the Lipper TASS Hedge Fund Database for the January 2002-September 2010 time period. The theoretical foundation of this paper draws from Fung and Hsieh (2000), who argue that time series of funds of hedge funds should be less prone to some of the documented database biases. We use a sampling technique to create hedge fund portfolios, and we then compare them using fund of fund data. We find empirical evidence that fund of hedge fund data is less biased than single hedge fund data, and that the impact of the survivorship and backfilling biases has increased since the financial crisis. We also find that the attrition rate for hedge funds has nearly doubled since the financial crisis, and that an elevated attrition rate has a negative impact on the quality and representativeness of hedge fund data due to the liquidation bias. The liquidation bias increased strongly in the aftermath of the financial crisis. It also fluctuates over time, and it can account for an overestimate of performance of over 10% p.a. Given this increase and the volatile nature of hedge fund biases, we believe investors (for benchmarking) and academics (for empirical studies) should consider refraining from using single hedge fund index data.

JEL Classification: G2, G12, G31.

Keywords: Hedge Funds, Survivorship Bias, Backfilling Bias, Liquidation Bias, Self-Selection Bias,

Portfolio Opportunity Distributions, Sampling.

* Dr. Dieter G. Kaiser, Research Fellow, Centre for Practical Quantitative Finance, Frankfurt School of Finance and Management & Director Investment Management, Feri Institutional Advisors GmbH, Rathausplatz 8-10, 61348 Bad Homburg, Germany, Phone: +49 (6172) – 916-3712, Fax: +49 (6172) – 916-9000, Email: [email protected]. † Florian Haberfelner, CFA, Senior Investment Manager, Feri Institutional Advisors GmbH, Rathausplatz 8-10, 61348 Bad Homburg, Germany, Phone: +49 (6172) – 916-3709, Fax: +49 (6172) – 916-9000, Email: [email protected].

Electronic copy available at: http://ssrn.com/abstract=1809570

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1. Introduction

The financial crisis of 2007-2009 had a powerful impact on the hedge fund industry. In

an environment in which stocks and commodities lost between 35%-40% on average, many

hedge funds failed to generate their promised absolute returns. Ultimately, hedge funds as a

group lost about 20%, which was double the loss of 1998, their prior worst year on record.

In addition to the magnitude of losses, the consecutive number of negative monthly

performances was unparalleled in hedge fund performance datasets. In this environment, risk

aversion suddenly spiked among hedge fund investors, and they reacted fairly uniformly by

redeeming their shares in (funds of) hedge funds. Many funds did manage to make payouts

according to their standard redemption terms (in fact, many managers now feel they were

used as “ATM machines” during that period). However, a large number of funds decided to

start enforcing special redemption restrictions.1

While there can be good reasons for such restrictions, the temporary suspension or

postponement of redemption payments during extreme market turbulence took many investors

by surprise. Discontent among investors increased further when numerous hedge funds

decided not to lift those restrictions when markets calmed in early 2009. Many funds

restructured and implemented multi-year liquidation schedules during which they continued

to charge full management fees. This naturally spurred further redemptions across the industry.

It was not unusual in 2008 and 2009 to see even high-performing hedge funds that had been

closed to new investors for years losing 60% or more of their total assets under management.

Likewise, the number of hedge fund liquidations during those years exceeded the number of

new launches.

It is rather surprising, then, that the performance of hedge fund indices paints a fairly

rosy picture of the hedge fund industry coming out of the financial crisis. According to these

indices, high watermarks from before the financial crisis were easily reached in 2009, and in

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2010 the industry even outperformed equities and provided double-digit returns. In this paper,

we challenge those results, as we believe that actual hedge fund performance was

significantly worse. We argue that the unprecedented attrition rate of hedge funds during the

financial crisis caused an extraordinarily high level of liquidation bias, which decreased even

further the already low quality of empirical hedge fund data.

Our aim here is to provide an update on hedge fund performance biases after the

financial crisis, and to propose a method that can be used to estimate liquidation bias in hedge

fund return series. Our theoretical foundation draws from Fung and Hsieh (2000), who argue

that time series of funds of hedge funds should be less prone to some of the documented

database biases (e.g., survivorship bias, selection bias, backfilling bias). Following Surz

(1994), we use portfolio opportunity distributions (POD) to create thousands of single hedge

fund (SHF) portfolios. Using this sampling technique, we generate all the possible portfolios a

fund of hedge funds (FHF) could conceivably hold, and compare them to actual and gross of

fees FHF data (to proxy for "less biased" hedge fund return series). We argue that differences

in the return distribution of the median POD simulated from SHF and FHF data should be

explained largely by the liquidation bias.

Our dataset is comprised of 8,935 hedge funds from the Lipper TASS “live” and

“graveyard” databases. Our sample period covers January 2002 through September 2010. To

cleanse the dataset, we classify each fund ourselves, and then build our own style-specific

peer groups for SHFs (Equity Hedge, Event Driven, Relative Value, and Tactical Trading)

and FHFs (Diversified, Equity Hedge, Event Driven, Relative Value, and Tactical Trading).

The remainder of the paper is organized as follows. Section 2 provides an overview of

hedge fund database biases and the relevant literature. Section 3 describes the cleansing

process and our dataset. Section 4 gives our methodology, while Section 5 reports our results.

Section 6 concludes and summarizes our results.

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2. Biases

Hedge fund indices are generally constructed from commercial hedge fund databases

using either an asset- or equal-weighted approach. Irrespective of construction principle, to be

representative, a hedge fund index must be able to reproduce the "true" performance of the

hedge fund market or a specific hedge fund style or strategy.2 However, hedge funds report

performance to commercial databases on a voluntary basis. Hence, they can decide when to

begin or when to stop reporting. As a result, they and their databases are prone to several

biases, such as self-selection, backfilling, survivorship, and the liquidation bias. These can

over- or understate the "true" performance of the market (see Ackermann et al., 1999).

The self-selection bias, for example, stems from the voluntary nature of hedge fund

performance reporting. It is generally assumed that funds only begin reporting to databases

when their first months have been good (relative to their peer groups), so that they can market

their returns. Likewise, funds that have failed to generate absolute returns will most likely

never report to a database. Furthermore, high-performing funds that reach capacity at launch

may also never report to a database.

Fung and Hsieh (2009) find that 40% of the 100 biggest hedge funds do not report

performance to databases. They argue that these firms have reached their high levels of assets

under management because of good performance.3 For example, Asness et al. (2001) consider

the effect of the self-selection bias to be minimal. Ackermann et al. (1999) believe that the

overall effects of the survivorship bias and the self-selection bias will cancel each other out.

Aiken et al. (2010) construct a dataset of 1,193 hedge funds that have never reported to a

database and compare it to hedge funds that have reported performance. They conclude that,

between 2004 and 2008, the self-selection bias resulted in an approximately 2% p.a.

overestimate of average hedge fund performance.

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The backfilling bias (sometimes referred to as the incubation or the instant history bias)

occurs when hedge funds with track records decide to report their performance to a database,

and the database provider includes their "past" performance information. The overall effect of

this bias has been the subject of many papers. As Table 1 shows, empirical studies on the

backfilling bias suggest it leads to an overestimate of hedge fund industry performance. Those

empirical findings are very reasonable, as it is generally assumed that a good track record is

more likely to be backfilled than a poor one.

– Table 1 about here –

Note that the most common bias is the so-called survivorship bias. Survivorship bias

occurs when only live funds (i.e., those still reporting to the database) are used to calculate an

index time series at the end of an observation period, while funds that “died” (or became

defunct) during that period are excluded. Because the dead funds were likely to have

performed worse than those that survived, an index that excludes the track records of dead

funds is likely to be biased to the upside.4

Several empirical studies calculate survivorship bias in hedge fund index returns. Some

conclude that its impact is irrelevant, at only 0.16% p.a. (e.g., Ackermann et al., 1999); others

find an upward bias of as much as 8.35% p.a. (e.g., Malkiel and Saha, 2005).

Table 2 provides an overview of the main papers on survivorship bias in hedge fund

databases. The papers vary regarding database choice, time period under examination, and

calculation method used to quantify the survivorship bias. However, all the papers find that

hedge fund indices that use databases excluding “dead” funds overestimate average hedge

fund returns. We note that most index providers have begun to include historic track records

of dead funds, so this bias is becoming increasingly irrelevant.

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Another related bias, which is sometimes subsumed into survivorship bias, occurs when

a hedge fund stops reporting performance but continues to exist for some months afterward.

There are many reasons why this would occur, most likely because the fund was liquidated.

This liquidation component is directly linked to the attrition rate, which indicates the

percentage of hedge funds that drop out of a database. According to Chan et al. (2005), the

attrition rate in hedge fund databases peaked after the 1998 LTCM crisis and after the end of

the new economy in 2001 (hence, after the (hedge fund) market crisis). Liang and Park (2010)

show that the real 3.1% failure rate is lower than the 8.7% annual attrition rate from 1995-

2004 based on the TASS database. Table 3 provides an overview of the literature on the

attrition rate in hedge fund databases.


Grecu et al. (2007) find that weak performance is overwhelmingly the main reason

hedge funds cease reporting performance to databases. This leads to the so-called liquidation

bias, which has not been studied in great detail thus far. Posthuma and Sluis (2004), for

example, measure the liquidation bias in hedge fund databases by assuming a significant (-

50%) or total (-100%) loss of investor capital in the liquidating month. However, given Fung

and Hsieh’s (2000) empirical findings, this approach seems arbitrary.5 Ackermann et al. (1999)

use data compiled from HFR (Hedge Fund Research, Inc.). They find that the average loss in

fund value beyond the information contained in the database is only 0.7%.6

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3. Dataset

The time series and descriptive variables for the empirical part of our paper come from

the Lipper TASS Hedge Fund Database. According to Füss et al. (2009), this remains the

most popular database for empirical hedge fund studies. Our first step was to merge the

November 2010 versions of Lipper’s "live" and "graveyard" databases.7 This yielded a unique

master database with performance information and descriptive variables for 16,191 funds.

In order to obtain a representative and clean dataset, we next used each hedge fund’s

strategy description from the TASS database to classify it into one of the four main hedge

fund styles: Equity Hedge, Event Driven, Relative Value, or Tactical Trading, or into a fifth

category, Multi-Strategy. This step minimized the classification biases in our peer groups,

because, similarly to Lipper TASS, the managers themselves chose their own styles/strategies.

And we found that any fund misclassifications were more likely to be due to simple input

error than to managers’ beliefs that a specific style has more investor demand.

Our second step was to clean the database rigorously. We included only the following

time series in our study:

- The USD share class, for hedge funds that report different currency classes.

- Offshore funds, for hedge funds that report both onshore and offshore fund

structures.

After the cleansing procedure, our hedge fund universe consisted of 6,088 SHFs (2,342

live and 3,746 graveyard), and 2,847 FHFs (1,261 live and 1,586 graveyard).

Our third step was to calculate gross performance figures for FHFs by adding back

reported management and performance fees to the net performances reported in the databases.

We use these gross performance figures for our calculations.

Table 4 shows the number of SHFs and FHFs in the respective peer groups of our

sample. We find that the highest number of SHFs can be classified as Equity Hedge (2,841),

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followed by Tactical Trading (1,409), Relative Value (1,150), and Event Driven (688). For

FHFs, we find that most follow the Multi-Strategy approach (1,702), followed by Equity

Hedge (554), Tactical Trading (271), Relative Value (251), and Event Driven (69). However,

the number of constituents within peer groups varies over time.


In order to control for the survivorship bias that is typical of the Lipper TASS database

(as noted by Aggarwal and Jorion, 2010), we use the January 2002-September 2010 sample

period. Aggarwal and Jorion (2010) note that when the TASS database was merged with the

Tremont database, 60% of the funds added between April 1999 and November 2001 were

likely to be survivors. They estimate that this bias averages more than 5% p.a.

The choice of our sample period is also in accordance with Fung and Hsieh (2000), who

argue that hedge fund data before 1994 is unreliable and should not be used for academic

research. Capocci and Hübner (2004) further recommend that empirical hedge fund studies

should include both bullish and bearish market phases for robustness; our dataset satisfies this

recommendation. Table 5 gives the descriptive statistics for our sample.


4. Methodology

We calculate survivorship bias by deducting the average return of the surviving ("live")

funds from the entire hedge fund universe ("live” and “dead") of our database. We perform

the survivorship bias calculations following Ibbotson et al. (2010), both with and without the

inclusion of backfilling effects. We construct this backfill bias-free comparison group by 1)

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excluding the track records preceding the reported date of inclusion into the database

(following Ibbotson et al., 2010), and 2) excluding the first twelve monthly observations from

funds missing their inclusion date (following Fung and Hsieh, 2000).

Next, as per Ibbotson et al. (2010), we calculate the backfill bias by 1) deducting the

average return of live funds with backfilling from the average return of live funds without it,

and 2) deducting the average return of live and dead funds with backfilling from the average

return of live and dead funds without it. We calculate the attrition rate as the number of

discontinued funds within one year divided by the total number of existing funds at the

beginning of that year. Because we include dead funds in our sample and we adjust for

backfilling, our sample is essentially free from the backfilling or survivorship biases.

We next estimate the progression of the residual biases, i.e., the self-selection and

liquidation biases. We use the portfolio opportunity distribution approach to compare the

performance datasets of SHFs and FHFs. More precisely, we need to compare what FHFs

could have invested in (randomly sampled SHF portfolios) with their empirical gross returns.

We randomly select portfolios of twenty-five SHFs from our database, and construct equally

weighted portfolios assuming monthly rebalancing. The sample is periodically reselected in

order to simulate active fund management.

This results in 25,000 potential multimanager portfolios. We compare the median

performance of this sample with that of the FHF data. We repeat this step for each hedge fund

style by using only the SHF and FHF data for each respective peer group.

We believe this comparison provides insights into the degree of liquidation and the self-

selection bias. According to Fung and Hsieh (2000), FHF data is less prone to database biases

than SHF data. The underlying assumption is that the liquidation bias is mitigated in FHF

datasets because liquidating funds must continue reporting to their FHF investors even if they

cease reporting to databases.

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We make a similar argument for the self-selection bias: Because FHFs source new

managers through their networks, they also invest in SHFs that have never reported to

databases. We believe that, given the high attrition rate over the course of the financial crisis,

liquidation will clearly be the dominant bias.

5. Empirical Results

Table 6 reports the results for the survivorship and backfilling biases in our database.

Over our entire sample period, we find a 2.75% p.a. survivorship bias and a 0.66% p.a.

backfilling bias for SHFs; the values are substantially lower for FHFs, with 0.92% and 0.00%

p.a., respectively. We also find that the impact of the survivorship and backfilling biases has

increased for SHFs and FHFs since the financial crisis. We illustrate this by calculating the

biases not only for our sample period, but also for two subperiods, January 2002-December

2006, and January 2007-September 2010 (the selection of these subperiods follows Fung et al.,

2008).

Moreover, by comparing the results from both subperiods, we find that the effects of the

survivorship and self-selection biases increased more dramatically for FHFs during the second

subperiod. However, FHF data is markedly less biased than SHF data on an absolute level

(over the entire sample period and over the two subperiods). And, note that we find no impact

of backfilling bias in the FHF data. This may be attributable to the fact that the surviving

FHFs have more similar risk and return patterns than did their dead counterparts.


Table 7 reports our results for the attrition rate. Over our sample period, we calculate an

average attrition rate of 13.57% p.a. for SHFs and 10.67% for FHFs. For the total universe,

the attrition rate nearly doubles, from 9.7% p.a. during the 2002-2006 period, to 19.2% p.a.

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after the financial crisis. Hence, we also observe a tremendous increase in the attrition rate of

SHFs and FHFs in our sample since the crisis. This pattern holds for all hedge fund styles,

with Tactical Trading and Event Driven exhibiting the lowest rates, and Equity Hedge and

Relative Value exhibiting the highest.


The fact that the attrition rate reached unprecedented levels in the aftermath of the

financial crisis indicates that the liquidation bias would also have spiked over that period.

Table 8 confirms this for our entire universe as well as for each of the styles. The liquidation

bias for the universe over our sample period is 2.3% p.a., while it is 1.2% p.a. for the January

2002-December 2006 subperiod, and increases to 3.7% p.a. for the January 2007-September

2010 subperiod.

The maximum liquidation bias for the rolling twelve-month observation periods is

substantially larger, at 11.1%. This shows that the liquidation bias increased throughout the

financial crisis, and has been volatile over time. It also confirms our view that hedge fund

industry performance after the financial crisis has likely been overstated by hedge fund

indices that build upon commercial hedge fund databases (where the index providers only

control for survivorship and backfilling, and not for the liquidation bias).


When we assess the results on a style level, we see that the outcomes vary significantly.

For Equity Hedge, the attrition rate during the crisis period rose to 19.0% from 9.7% during

the 2002-2006 period. As with all of the styles, this caused an increase in the liquidation bias

we measured, from 2.0% to 3.1% p.a. The maximum rolling twelve-month bias experienced a

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dramatic spike to 18.9%, the highest level among the four styles for any twelve-month

observation period.

For Event Driven, the increase in the liquidation bias was less substantial. The attrition

rate rose more, from 8.0% (over 2002-2006) to 21.4% (2007-2009), but the liquidation bias

only increased from 0.7% to 1.6% p.a. Over our entire observation period, we note that the

liquidation bias in Event Driven was rather low, with 1.1% p.a. However, for any rolling

twelve-month period, it peaked at 7.7% p.a.

The fact that the attrition rate for Event Driven was low compared to the other styles

may be attributable to its performance as a group. Before the crisis, Event Driven at 11.0% p.a.

was the best-performing hedge fund style; after the financial crisis, at 1.4% p.a. since 2007, it

was second only to Tactical Trading.

For Relative Value, we observe the highest attrition rate for all the styles before, during,

and after the financial crisis. It increased from 11.8% over 2002-2006 to 25.0% p.a. during

2007-2009. The liquidation bias over the 2002-2010 period was 1.8% p.a., but its maximum

value over any twelve-month rolling period was 12.0%.

Interestingly, we observed a negative liquidation bias for Relative Value over the 2002-

2006 period. We argue that this indicates the self-selection bias is dominating the liquidation

effect during that time. And, as the FHF performance data in Table 8 shows, Relative Value

had the lowest performance during the financial crisis. We believe that the high attrition rate

and the high liquidation bias in this style signal that its highly leveraged and illiquid strategies

fell out of favor with hedge fund investors after the crisis. The high attrition rate could thus be

a sign that investor interest has now shifted significantly toward more liquid hedge fund styles.

Finally, for Tactical Trading, the attrition rate rose over the crisis period to 16.2%, from

10.0% in the 2002-2006 period. This is the lowest increase we observe among the styles.

Furthermore, in 2010, the attrition rate for this style had already reverted back to its pre-crisis

level. The liquidation bias for this style only reached 2.2% p.a. for the period since 2007.

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As with Relative Value, the liquidation bias before the crisis is negative, indicating that

the self-selection bias is dominating the liquidation effect. The maximum rolling twelve-

month liquidation bias for Tactical Trading shows a spike of 11.0%, a level slightly below the

value for our total universe. The low attrition rate for this style during the financial crisis may

be attributable to its positive performance during that time. In fact, in 2008, it was the only

style with positive performance. Since that time, demand for this style (as well as for the

substrategies Global Macro, Systematic Trend-Following and Commodities) has increased

substantially because its long-term risk and performance measures outperformed those of the

other styles.

6. Conclusion

This paper explores how hedge fund database biases developed during the 2007-2009

financial crisis. For our analysis, we first built a universe of 8,935 hedge funds from the

Lipper TASS Hedge Fund Database for the January 2002-September 2010 time period. We

find empirical evidence that fund of hedge fund data is less biased than single hedge fund data,

and that the impact of the survivorship and backfilling biases has increased since the financial

crisis. Furthermore, we observe a near doubling of the attrition rate for hedge funds since the

financial crisis.

We show that, because of the liquidation bias, an elevated attrition rate has a negative

impact on the quality and representativeness of hedge fund data. Additionally, the liquidation

bias increased strongly in the aftermath of the financial crisis, and it fluctuates over time,

accounting for as much as a 10% p.a. return differential. We found that, among the different

hedge fund styles, Event Driven and Tactical Trading were the least affected by the

liquidation bias, while Equity Hedge and Relative Value were affected the most.

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Our results strongly suggest that (gross of fees) fund of hedge fund data provides a

better picture of performance than single hedge fund data. However, our FHF results are

biased by the effects of the liquidation and self-selection biases.

If we examine the FHF attrition rate (see Table 7), we note two primary observations:

First, it spiked later than the SHF attrition rate (2009 versus 2008). This confirms our

assumption about the insights to be gained by comparing SHF and FHF data. Second, the

magnitude of the spike in FHF data was significant and unprecedented. Hence, we can assume

that the liquidation bias is dominating the self-selection bias during the aftermath of the

financial crisis, which is similar to our observations about SHFs. As a result, we suspect that

FHF performance itself is biased upward, which implies that our estimates of the liquidation

bias are biased downward, and the actual impact may be even larger. We encourage further

research on this topic, and particularly recommend including investable hedge fund indices in

the analysis.

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Getmansky, M. Lo, A.W. and Mei, S.X. (2004), "Sifting Through the Wreckage: Lessons from Recent Hedge Fund Liquidations", Journal of Investment Management, Vol. 2 No. 4, pp. 6-38. Grecu, A., Malkiel, B.G. and Saha, A. (2007), "Why Do Hedge Funds Stop Reporting Their Performance?", Journal of Portfolio Management, Vol. 34 No. 1, pp. 119-126. Gregoriou, G.N. and Rouah, F. (2002), "Large versus Small Hedge Funds: Does Size Affect Performance?", Journal of Alternative Investments, Vol. 5 No. 3, pp. 75-77. Hartmann, D. and Kaiser, D.G. (2011), "Hedging Equity Market Risk in Hedge Fund Investing: A New Approach", Journal of Portfolio Management, forthcoming. Ibbotson, R.G., Chen, P. and Zhu, K.X. (2010), "The ABCs of Hedge Funds: Alphas, Betas, and Costs", Financial Analysts Journal, Vol. 67 No. 1, pp. 15-25. Jenkins, I. (2003), "State of the Hedge Fund Industry", AIMA Journal, No. 56, pp. 7-10. Kaiser, D.G. (2008), "The Lifecycle of Hedge Funds", Journal of Derivatives and Hedge Funds, Vol. 14 No. 2, pp. 127-149. Liang, B. (1999), "On the Performance of Hedge Funds", Financial Analysts Journal, Vol. 55 No. 4, pp. 72-85. Liang, B. (2000), "Hedge Funds: The Living and the Dead", Journal of Financial and Quantitative Analysis, Vol. 35 No. 3, pp. 309-326. Liang, B. (2001), "Hedge Fund Performance: 1990-1999", Financial Analysts Journal, Vol. 57 No. 1, pp. 11-18. Liang, B. (2002), "Hedge Funds, Fund of Funds and Commodity Trading Advisors", Working Paper, Case Western Reserve University, Cleveland. Liang, B. (2004), "Alternative Investments: CTAs, Hedge Funds and Fund of Funds", Journal of Investment Management, Vol. 2 No. 4, pp. 76-93. Liang, B. and Park, H. (2010), "Predicting Hedge Fund Failure: A Comparison of Risk Measures", Journal of Financial and Quantitative Analysis, Vol. 45 No. 1, pp. 199-222. Malkiel, B. and Saha, A. (2005), "Hedge Funds: Risk and Return", Financial Analysts Journal, Vol. 61 No. 6, pp. 80-88. Moerth, P. (2005), "Konstruktion von Hedgefonds-Portfolios", in Peetz, D. (Ed.), Praktiker-Handbuch Alternatives Investmentmanagement, Schäffer-Poeschel, Stuttgart, pp. 291-308. Posthuma, N. and Sluis, P.J.v.d. (2004), "A Critical Examination of Historical Hedge Fund Returns", in Schachter, B. (Ed.), Intelligent Hedge Fund Investing, Risk Books, London, pp. 365-386.

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Rouah, F. (2006), "Competing Risks in Hedge Fund Lifetimes", Working Paper, McGill University, Montreal. Schneeweis, T., Kazemi, H. and Martin, G. (2001), "Understanding Hedge Fund Performance: Research Results and Results of Thumb for the Institutional Investor", Working Paper, University of Massachusetts, Massachusetts. Schneeweis, T., Spurgin, R. and McCarthy, D. (1996), "Survivor Bias in Commodity Trading Advisors Performance", Journal of Futures Markets, Vol. 16 No. 2, pp. 757-772. Surz, R.J. (1994), "Portfolio Opportunity Distributions: An Innovation in Performance Evaluation", Journal of Investing, Vol. 3 No. 2, pp. 30-35. Xu, X.E., Liu, J. and Loviscek, A. (2010), "Hedge Fund Attrition, Survivorship Bias, and Performance: Perspectives from the Global Financial Crisis", Working Paper, Seton Hall University, South Orange, February.

19

Table 1: Literature Overview -- Backfilling Bias This table provides an overview of the academic studies on the backfilling bias cited herein. All backfilling bias figures are reported in an annualized format. MAR stands for Managed Accounts Reports; today, this database is known as the Center for International Securities and Derivatives Markets (CISDM) database. HFR stands for Hedge Fund Research, Inc., TASS is the Lipper TASS database, and Barclay refers to the BarclayHedge database.

Authors Database Time Period Number of Funds Deleted Months Backfilling Bias

Ackermann et al. (1999) MAR/HFR 1988-1995 547 24 0.05

Capocci et al. (2005) MAR 1994-2002 2,796 12 1.32

Capocci (2006) MAR/Barclay 1994-2002 3,963 12 0.17





Edwards/Caglayan (2001) MAR 1990-1998 1,665 12 1.17

Fung/Hsieh (2000) TASS 1994-1998 1,722 12 1.40

Ibbotson et al. (2010) TASS 1995-2010 6,219 individual 4.02

Malkiel/Saha (2005) TASS 1994-2003 2,065 12 5.74

Posthuma/Sluis (2004) TASS 1996-2002 3,580 34 4.35

20

Table 2: Literature Overview -- Survivorship Bias This table provides an overview of the academic studies on the survivorship bias cited herein. All survivorship bias figures are reported in an annualized format. MAR stands for Managed Accounts Reports (today, this database is known as the Center for International Securities and Derivatives Markets (CISDM) database), ZCM stands for Zurich Capital Markets (a database that was merged into the CISDM database in 2001), HFR stands for Hedge Fund Research, Inc., TASS is the Lipper TASS database, Barclay refers to the BarclayHedge database, FRM stands for Financial Risk Management, and Offshore Directory refers to the U.S. Offshore Funds Directory published by Antoine Bernheim.

Authors Database Number of Funds Time Period

Survivorship Bias

Ackermann et al. (1999) MAR/HFR 547 1988-1995 0.16

Ammann/Moerth (2005) TASS 4,014 1994-2005 3.54

Amin/Kat (2003) TASS 1,721 1994-2001 1.89

Anjilvel et al. (2000) FRM 1,130 1990-2000 2.20

Baquero et al. (2004) TASS 1,797 1994-2000 2.10

Barès et al. (2002) FRM 2,308 1996-1999 1.30

Bianchi/Drew (2006) TASS 3,012 1994-2001 3.01

Brown et al. (1999) Offshore Directory 395 1989-1995 0.75

Capocci et al. (2005) MAR 2,796 1994-2002 1.51

Darst (2000) MAR 2,202 1995-2000 1.15

Das (2003) ZCM 2,467 1989-2000 2.16

Edwards/Caglayan (2001) MAR 1,665 1990-1998 1.85

Edwards/Liew (1998) MAR 1,456 1989-1996 1.91

Fung/Hsieh (1997) TASS 901 1989-1995 3.42

Fung/Hsieh (2000) TASS 1,722 1994-1998 3.00

Fung/Hsieh (2006) TASS 2,431 1994-2004 2.40

Fung/Hsieh (2006) HFR 2,939 1994-2004 1.80

Fung/Hsieh (2006) CISDM 1,315 1994-2004 2.40

Ibbotson et al. (2010) TASS 6,219 1995-2009 5.13

Liang (2000) TASS 1,627 1994-1998 2.24

Liang (2000) HFR 1,162 1994-1997 0.60

Liang (2001) TASS 1,921 1994-1999 2.43

Liang (2001) TASS 2,016 1990-1999 1.69

Liang (2002) CISDM 2,357 1985-2001 1.52

Liang (2004) ZCM 2,357 1994-2001 2.32

Malkiel/Saha (2005) TASS 2,700 1996-2003 8.35

Moerth (2005) TASS 2,328 1994-2003 2.96

Rouah (2006) HFR 3,595 1994-2003 3.35

Schneeweis et al. (2001) TASS 1,722 1998-2000 2.17

Schneeweis et al. (1996) TASS 56 1988-1991 1.20

Xu et al. (2010) CISDM 2,570 1994-2009 3.12

21

Table 3: Literature Overview -- Attrition Rate This table provides an overview of the academic studies on the attrition rate cited herein. All attrition rate figures are reported in an annualized format. HFR stands for Hedge Fund Research, Inc., TASS is the Lipper TASS database, FRM stands for Financial Risk Management, MSCI is the Morgan Stanley Capital International, EuroHedge refers to the EuroHedge database of Hedge Fund Intelligence, CISDM refers to the Center for International Securities and Derivatives Markets, and Offshore Directory refers to the U.S. Offshore Funds Directory, published by Antoine Bernheim.

Authors Database Time Period of Reporting

Attrition Rate (%)

min mean max

Amin/Kat (2003) TASS 1994-2001 2.2 - 12.3

Baba/Goko (2006) HFR 1994-2005 2.9 7.3 9.1 Baquero et al. (2004) TASS 1994-2000 - 8.6 - Barès et al. (2002) FRM 1992-2000 - 5.0 - Barry (2003) TASS 1994-2000 8.0 - 10.0 Bianchi/Drew (2006) TASS 1994-2001 2.3 8.7 12.4 Brown et al. (1999) Offshore Directory 1987-1996 - 14.0 - Brown et al. (1999) Offshore Directory 1989-1995 - 20.0 - Brown et al. (2001) TASS 1994-1998 - 15.0 - Chan et al. (2005) TASS 1994-1999 - 7.5 - Chan et al. (2005) TASS 1994-2003 3.0 8.8 11.4 Christory (2009) HFR 2008-2009 25.0* 28.0 48.0* Fung/Hsieh (1997) TASS 1989-1995 - 19.0 - Getmansky et al. (2004) TASS 1993-2004 1.1* 8.8 30.7* Getmansky (2005) TASS 1994-2002 2.6 7.1 9.3 Jenkins (2003) EuroHedge 2001-2002 8.0 9.0 10.0 Liang (1999) HFR 1993-1997 - 2.2 - Liang (2000) TASS 1994-1998 4.7 8.3 13.4 Liang (2000) HFR 1994-1997 1.4 2.7 6.2 Liang (2001) TASS 1994-1999 4.1 - 13.0 Liang/Park (2010) TASS 1995-2004 3.4 8.7 12.5 Malkiel/Saha (2005) TASS 1994-2003 12.4** 17.5** 26.9** Rouah (2006) HFR 1994-2003 2.1 9.5 16.2 Xu et al. (2010) CISDM 1994-2009 2.1 12.1 31.0

* Strategy-specific extrema. ** Only funds reporting contemporaneously were considered.

22

Table 4: Number of Funds of Funds and Single Hedge Funds in the Peer Groups This table reports the number of funds within the respective hedge fund styles categorized as single hedge funds and as funds of funds. Funds that trade several strategies or allocate to several strategies were included in the "Diversified" peer group. The number of funds in each respective peer group represents the funds that entered our empirical analysis (after the cleansing and classification process from the original Lipper TASS data).

Single Hedge Funds (SHF) Funds of Hedge Funds (FHF)

live dead total live dead total

Diversified - - 256 754 948 1,702

Equity Hedge 1,095 1,746 2,841 253 301 554

Event Driven 253 435 688 34 35 69

Relative Value 391 759 1,150 75 176 251

Tactical Trading 603 806 1,409 145 126 271

Total 2,342 3,746 6,088 1,261 1,586 2,847

23

Table 5: Descriptive Statistics Our data come from the Lipper TASS database. There are 8,935 hedge funds, including 3,603 survived funds and 5,332 dissolved funds as of September 2010. There are 2,847 funds of funds and 6,088 single hedge funds. 1,246 of 2,844 funds of funds are live; 1,586 (or 55.71%) have been dissolved. In contrast, 2,342 of 6,088 single hedge funds are live, while 3,746 (or 61.53%) have been dissolved. Assets and minimum investments are reported in millions of dollars, age is reported in months, notice period and lock-up period are in days, and management and performance fees are in percent p.a. We apply a t-test to test the null hypothesis that the means of the two populations are equal.

Single Hedge Funds (SHF) Funds of Hedge Funds (FHF)

No. Max Min Mean Std. Dev. No. Max Min Mean Std.

Dev. tFHF-HF

Management Fee 5,974 20.00 0.00 1.53 0.67 2,829 10.00 0.00 1.35 0.61 12.01***

Performance Fee 5,974 50.00 0.00 18.12 6.14 2,829 50.00 0.00 7.80 7.09 66.57***

Age 6,088 226.00 1.00 70.06 51.46 2,847 226.00 1.00 71.13 48.91 -0.95

Assets 2,027 20,198.00 0.96 186.43 756.34 1,125 5,808.23 0.10 125.66 385.94 2.98***

Notice Period 5,085 365.00 1.00 37.31 29.32 2,066 195.00 1.00 47.31 28.52 -13.33***

Minimum Investment 5,081 65.00 0.00 0.97 2.48 2,249 50.00 0.01 0.71 2.85 3.69***

Open for Investmentsa 5,994 1.00 0.00 0.94 0.24 2,842 1.00 0.00 0.98 0.14 -10.26***

High Watermarka 5,993 1.00 0.00 0.69 0.46 2,840 1.00 0.00 0.50 0.50 17.06***

Lockupa 6,088 1.00 0.00 0.25 0.43 2,846 1.00 0.00 0.12 0.33 15.56***

Lockup Period 1,532 180.00 1.00 12.57 8.70 349 72.00 1.00 12.13 7.26 1.00

a = dummy variable, 1 = yes, 0 = no.

***Significant at the 1% level.

24

Table 6: Survivorship and Backfill Biases

Our data come from the Lipper TASS database. There are 8,935 hedge funds, including 3,603 survived funds and 5,332 dissolved funds as of September 2010. There are 2,847 funds of funds and 6,088 single hedge funds. 1,246 of 2,844 funds of funds are live; 1,586 (or 55.71%) dissolved. In contrast, 2,342 of 6,088 single hedge funds are live, while 3,746 (or 61.53%) have been dissolved. All returns and standard deviations are reported in an annualized format. Subperiod selection follows Fung et al. (2008). We apply a t-test to test the null hypothesis that the means of the two populations are equal.

Panel A: Single Hedge Funds

Performance with backfilling without backfilling

live funds live + dead funds live funds live + dead funds

Time Period Return Stdev Return Stdev Return Stdev Return Stdev

01/2002-09/2010 11.21% 6.33% 8.47% 5.75% 10.52% 6.51% 7.80% 5.94%

01/2002-12/2006 7.91% 5.23% 6.28% 4.26% 7.50% 5.34% 5.94% 4.38%

01/2007-09/2010 7.29% 7.49% 4.88% 7.22% 6.68% 7.75% 4.14% 7.48%

Bias Estimation live - (live+dead) live - (live+dead) funds (with backfilling) - funds (without backfilling)

with backfilling without backfilling live - live (live + dead) - (live + dead)

Time Period Δ Return t-value Δ Return t-value Δ Return t-value Δ Return t-value

01/2002-09/2010 2.75% 4.42*** 2.72% 4.24*** 0.69% -1.04 0.66% -1.10

01/2002-12/2006 1.63% 2.90*** 1.56% 2.71*** 0.40% -0.65 0.33% -0.65

01/2007-09/2010 2.41% 3.19*** 2.53% 3.23*** 0.61% -0.78 0.73% -0.97

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

Panel B: Funds of Hedge Funds

Performance with backfilling without backfilling

live funds live + dead funds live funds live + dead funds

Time Period Return Stdev Return Stdev Return Stdev Return Stdev

01/2002-09/2010 4.52% 4.98% 3.61% 5.05% 4.55% 5.01% 3.60% 5.08%

01/2002-12/2006 4.36% 3.50% 4.05% 3.51% 4.38% 3.51% 4.08% 3.51%

01/2007-09/2010 0.36% 6.30% -0.98% 6.39% 0.39% 6.36% -1.07% 6.44%

Bias Estimation live - (live+dead) live - (live+dead) funds (with backfilling) - funds (without backfilling)

with backfilling without backfilling live - live (live + dead) - (live + dead)

Time Period Δ Return t-value Δ Return t-value Δ Return t-value Δ Return t-value

01/2002-09/2010 0.92% 1.78* 0.94% 1.82* -0.03% 0.05 0.00% -0.01

01/2002-12/2006 0.32% 0.77 0.29% 0.71 -0.01% 0.03 -0.03% 0.08

01/2007-09/2010 1.34% 2.05** 1.45% 2.21** -0.03% 0.04 0.09% -0.13


25

Table 7: Attrition Rate

Our data come from the Lipper TASS database. There are 8,935 hedge funds, including 3,603 survived funds and 5,332 dissolved funds as of September 2010. There are 2,847 funds of funds and 6,088 single hedge funds. 1,246 of 2,844 funds of funds are live; 1,586 (or 55.71%) have been dissolved. In contrast, 2,342 of 6,088 single hedge funds are live, while 3,746 (or 61.53%) have been dissolved. Values for 2010 include data until September. The presentation of our results follows Rouah (2006).

Panel A: Single Hedge Funds Equity Hedge Event Driven Relative Value Tactical Trading Total Universe

2002 10.07% 8.47% 7.64% 7.29% 8.71%

2003 8.10% 6.73% 9.40% 7.61% 8.01%

2004 9.26% 5.65% 12.87% 8.54% 9.04%

2005 10.44% 5.46% 16.81% 13.12% 11.20%

2006 10.52% 13.49% 12.16% 13.48% 11.62%

2007 14.40% 14.31% 19.25% 14.50% 14.85%

2008 24.23% 25.83% 31.45% 19.96% 24.11%

2009 18.28% 24.13% 24.29% 14.22% 18.70%

2010 15.46% 16.07% 17.09% 15.02% 15.87%

All 13.42% 13.35% 16.77% 12.64% 13.57%

Panel B: Funds of Hedge Funds Diversified Equity Hedge Event Driven Relative Value Tactical Trading Total Universe

2002 4.34% 5.92% 0.00% 1.13% 5.94% 4.28%

2003 1.53% 2.86% 3.32% 4.50% 0.00% 2.03%

2004 3.03% 3.16% 0.00% 5.36% 4.38% 3.28%

2005 5.62% 2.04% 2.21% 4.87% 3.94% 4.68%

2006 4.06% 4.24% 1.98% 6.39% 1.91% 4.05%

2007 6.11% 4.29% 3.79% 6.46% 5.65% 5.68%

2008 22.50% 21.89% 14.59% 29.79% 16.63% 22.25%

2009 30.79% 27.22% 31.14% 42.09% 25.83% 30.41%

2010 17.78% 21.83% 19.57% 32.38% 15.46% 19.34%

All 10.64% 10.39% 8.51% 14.78% 8.86% 10.67%

26

Table 8: Liquidation Bias

Our data come from the Lipper TASS database. There are 8,935 hedge funds, including 3,603 survived funds and 5,332 dissolved funds as of September 2010. There are 2,847 funds of funds and 6,088 single hedge funds. 1,246 of 2,844 funds of funds are live; 1,586 (or 55.71%) have been dissolved. In contrast, 2,342 of 6,088 single hedge funds are live, while 3,746 (or 61.53%) have been dissolved. The SHF values represent the median values for the 25,000 sampled SHF portfolios consisting of twenty-five SHFs. The FHF values represent the median values of the FHFs in our database.

Panel A: Total Universe Return SHF Stdev SHF Return FHF Stdev FHF Δ Return Max 12m Δ t-value

01/2002-09/2010 7.6% 6.0% 5.3% 5.3% 2.3% 11.1% 1.57*

01/2002-12/2006 10.4% 4.5% 9.3% 3.7% 1.2%

01/2007-09/2010 4.0% 7.6% 0.3% 6.6% 3.7%

Panel B: Equity Hedge Return SHF Stdev SHF Return FHF Stdev FHF Δ Return Max 12m Δ t-value

01/2002-09/2010 7.9% 8.5% 5.4% 6.6% 2.5% 18.9% 1.87**

01/2002-12/2006 11.6% 6.2% 9.7% 5.2% 2.0%

01/2007-09/2010 3.1% 10.8% -0.1% 8.0% 3.1%

Panel C: Event Driven Return SHF Stdev SHF Return FHF Stdev FHF Δ Return Max 12m Δ t-value

01/2002-09/2010 7.9% 6.1% 6.8% 5.2% 1.1% 7.7% 1.29

01/2002-12/2006 11.7% 3.7% 11.0% 3.2% 0.7%

01/2007-09/2010 3.0% 8.2% 1.4% 6.8% 1.6%

Panel D: Relative Value Return SHF Stdev SHF Return FHF Stdev FHF Δ Return Max 12m Δ t-value

01/2002-09/2010 5.8% 3.3% 4.0% 4.2% 1.8% 12.0% 3.66**

01/2002-12/2006 7.2% 1.7% 7.9% 2.3% -0.7%

01/2007-09/2010 4.0% 4.6% -0.9% 5.6% 4.9%

Panel E: Tactical Trading Return SHF Stdev SHF Return FHF Stdev FHF Δ Return Max 12m Δ t-value

01/2002-09/2010 8.5% 6.7% 8.1% 6.5% 0.4% 11.0% 0.99

01/2002-12/2006 9.1% 7.1% 10.1% 6.6% -1.0%

01/2007-09/2010 7.7% 6.2% 5.5% 6.3% 2.2%


27

Endnotes

1 Hartmann and Kaiser (2011) argue that, during times of crisis, the high equity market beta of FHFs and the

general low liquidity of these investment structures (e.g., notice and redemption periods or lockups), and during other extraordinary circumstances (e.g., maximum redemptions, gates, side-pockets), the most efficient way to decrease unwanted exposure is through overlay hedging strategies using futures.

2 In general, there is a great deal of heterogeneity in hedge fund index returns, which stems from the fact that many hedge funds report to only one database. Fung and Hsieh (2006), for example, analyzed the overlap of five major hedge fund databases, and found that only 3% of the funds reported to all five databases. More recently, Fung and Hsieh (2009) found that, as of December 2007, BarclayHedge had 3,280 hedge funds managing assets of $864 billion; CISDM had 3,031 funds with $632 billion; HFR had 4,324 funds with $876 billion; and Lipper TASS had 4,047 funds with $634 billion in assets under management. However, after merging all four databases, the authors only found 6,449 unique hedge funds with $1.397 trillion in assets under management. Additionally, approximately 49% of these unique hedge funds came from a single database, while 31% reported to two databases, 13% reported to three, and only 7% reported to all four databases.

3 The hypothesis that bigger hedge funds tend to outperform smaller hedge funds has been rejected by various empirical studies. However, most of those studies are subject to the self-selection bias because they build on commercial hedge fund databases. See, for example, Ammann and Moerth (2005), Gregoriou and Rouah (2002), and Kaiser (2008).

4 Lipper TASS and HFR were the first database providers to realize the importance of the survivorship bias. From 1994, they marked discontinued or dead funds, and continued to publish them in a separate database called the “graveyard.”

5 Fung and Hsieh (2000) investigated 602 single hedge funds from the TASS database that stopped reporting. They concluded that 60% of the funds closed down, 28% decided not to provide any further data, and 4% merged with other funds. There was no explanation for the remaining 8%.

6 We believe that the liquidation bias could also be subsumed into the self-selection bias, as a decision to stop reporting to a database because of poor performance is also a discretionary one made by the hedge fund manager. However, in the literature, the self-selection bias mostly refers to the date of entry of a fund into the database, while the liquidation bias refers to the effect of the date of exit.

7 Graveyard funds no longer report performance data to the Lipper TASS, either because they have been liquidated, they are closed to new investments, or they have been restructured or merged with another hedge fund. The live funds continue to report to Lipper on a monthly basis. We combine both the live and dead funds within our sample in order to minimize the effects of survivorship bias.

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