Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds

Kevin C.H. Chiang*

College of Business AdministrationNorthern Arizona University

Flagstaff, AZ 86011-5066

Kirill Kozhevnikov

Lundquist College of BusinessUniversity of OregonEugene, OR 97403

Ming-Long Lee

Department of FinanceNational Yulin University of Science and Technology

Touliu, Yulin, Taiwan 640

Craig H. Wisen

School of ManagementUniversity of Alaska Fairbanks

Fairbanks, AK 99775

Key Words: REIT, performance evaluation, mutual funds

* Correspondence: Kevin C.H. Chiang, College of Business Administration, Northern Arizona University, Flagstaff, AZ 86011-5066. Phone: (928) 523-4586, Fax: (928) 523-7331, E-mail: Kevin.Chiang@nau.edu.

The authors thank Kenneth French for providing the three-factor return series.

Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds

Abstract

Fund of Funds (FOFs) are created when investment companies acquire shares of other

investment companies. The Securities and Exchange Commission (SEC) has recently

proposed several rules under the Investment Company Act of 1940 that would expand the

ability of FOFs to acquire shares of other funds and the proposal would improve the

transparency of disclosures relating to expenses and fees. Although the additional layer

of fees incurred by FOFs has a negative effect on returns, there is empirical evidence that

real estate FOFs have generated superior performance net of fees and risk adjustments.

This study resolves the apparent contradiction and it finds that the performance of real

estate FOFs is consistent with the predominant view that most mutual funds do not

outperform their benchmarks.

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Further Evidence on the Performance of Funds of Funds: The Case of Real Estate Mutual Funds

Funds of funds (FOFs) are generally defined as investment companies that hold

shares of other investment companies. The existence of FOFs suggests that some

investors may benefit from the professional management and enhanced diversification

associated with the separation and specialization of investment analysis and management.

This additional layer of management and the separation of fund selection from security

selection is an old practice that has come in and out of vogue in the U.S. over the last

century. FOFs played a significant role in the creation of the Investment Company Act of

1940, in part because some early FOFs were associated with extreme leverage, market

manipulation, and pyramid schemes. The public perception of FOFs reached a second

low point in the early 1970s when Investors Overseas Services imploded under the

management of Bernie Cornfeld and Robert Vesco.

FOFs have regained their popularity since the 1970s. Based upon 2003 market

values, FOFs held approximately 3% of long-term mutual fund assets. 1 Explaining the

rebound of FOFs is outside the scope of the present analysis, although casual observation

suggests it is likely to be related to the growth in the number and complexity of mutual

funds, regulatory trends, and economies of scale as it relates to investments that would

otherwise be closed to new investors.

The Securities and Exchange Commission (SEC) has recently proposed several

rules that affect FOFs operating under the Investment Company Act of 1940. The rules

would increase the transparency and clarity of fees incurred by FOFs. This is important

1 Stategic Insight FRC Report dated September 30, 2003

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because FOFs generally charge a management fee when fund selections are not

constrained to the same family of funds. This fee is in addition to the fees incurred by

each fund in the FOFs portfolio.

The degree to which the mutual fund industry is competitive plays an important

role in motivating many empirical studies of traditional mutual fund performance. In a

competitive market one would expect the marginal benefit of investing in a FOF to be

equal to the incremental cost.2 Although individual studies reach different conclusions,

there appears to be a growing consensus that traditional mutual funds on average do not

generate excess returns after adjusting for fees and risk. It is therefore puzzling to find

that this consensus has not been reached among studies that focus on the performance of

FOFs, and in particular real estate FOFs. This study resolves this apparent contradiction

and finds that real estate funds do not generally outperform their benchmarks after fees

and risk adjustments. The implication is that the competitive environment among real

estate mutual funds is similar to the environment among traditional mutual funds.

Real estate mutual funds are specialized funds that invest primarily in real estate

investment trusts (REITs). Kallberg, Liu, and Trzcinka (2000) classify real estate mutual

funds as FOFs because an REIT is a collection of real estate properties managed by a

separate entity. These authors analyze the performance of real estate funds during the

sample period of December 1986 to June 1998. They demonstrate that the alphas of their

sample of returns under standard asset pricing specifications are mostly positive.3 The

2 There is even evidence from the sector of hedge funds that FOF investors may pay too much for the incremental services (Brown, Goetzmann, and Liang 2003). Nevertheless, the incentive structure in the hedge fund industry is quite different from that in the mutual fund industry.3 Lin and Yung (2004) examine the performance of real estate mutual funds during the sample period of 1993 to 2001. Although Lin and Yung’s sample is largely overlapped with that of Kallberg, Liu, and Trzcinka (2000), they reach the opposite conclusion, and argue that real estate mutual fund managers do not add value. This study performs some of Lin and Yung’s analyses and finds that Lin and Yung’s alpha estimates appear to be systematically too low. For example, their eighth sample fund: Alpine International

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authors conclude that managers of real estate FOFs add value and estimated the

incremental annual return to be approximately 2% relative to passive benchmarks.

This paper adopts a slightly different approach in evaluating the performance of

real estate mutual funds. This is motivated by the diminishing ability of standard asset

pricing models to explain REIT returns. The R-squared from the Capital Asset Pricing

Model (CAPM) and the Fama-French (1993) three-factor model on REIT indices is

approximately 10-30% based upon the last five years of return data. The pricing ability

of CAPM or Fama-French models when applied to equity real estate returns is about the

same as when the models are applied to hedge funds.

Motivated by this observation, the study begins with a simple question: Do real

estate funds on average produce a higher raw return than the return produced by a

strategy of randomly selecting REITs? The answer to this question is important for two

reasons. First, without an accurate description of risk-return tradeoff for real estate

securities, it is beneficial to have several evaluation methods to evaluate the robustness of

individual results. This line of reasoning is abundant in the literature of hedge funds (Lo

2001). Second, Kallberg et al. show that real estate fund managers produce superior

performance by investing in illiquid REITs which typically have small market

capitalizations. Fund managers that invest in this segment of REITs are hypothesized to

have informational advantages. Because this strategy involves a higher level of risk than

investing in more liquid REITs with larger market capitalizations, one would expect that

real estate mutual funds should on average produce higher returns than passively selected

benchmarks. Surprisingly, this study finds the opposite.

Real Estate Y has an intercept of -0.40 under the CAPM with the use of the CRSP and the Morningstar Principia. Yet Lin and Yung’s result is -0.80. This inconsistency is likely due to Lin’s and Yung’s use of daily return data from Yahoo which is likely to be less reliable than data extracted from CRSP.

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This paper also examines the performance of real estate funds under the CAPM

and the Fama-French three-factor model. This study adopts a different testing design

because the two standard asset pricing models have limited abilities to explain REIT

returns. This study estimates real estate fund managers’ incremental alphas with respect

to those alphas produced by passive investing strategies under the two specifications.

With the inclusion of this control mechanism, the results suggest that the environment in

which real estate funds compete is competitive and the funds do not add value net of fees

and risk adjustments.

Data

The study employs the 2003 Morningstar Principia database to identify real estate

mutual funds. The analysis focuses on the subset of real estate mutual funds that satisfied

the following criteria: (1) classified by the Morningstar as a real estate fund, (2) fund

portfolio allocation to bonds and other asset classes less than 10%, and (3) return history

of at least two years. Funds with successful return histories are more likely to issue

multiple share classes representing different fee structures. The fund’s oldest share class

is used for the analysis to prevent multiple share classes from biasing the results.

Monthly returns of this sample funds are then retrieved from the Center for Research in

Security Prices (CRSP) mutual fund database to the end of 2003. The merger of the data

based on the two databases resulted in the final set of 55 real estate FOFs.

Table 1 reports summary statistics. During the sample period of 01/1982-

12/2003, the 55 sample funds on average yield a 1.05% monthly return. The returns are

stable over this time period, evident by the monthly standard deviation of 0.26%. As of

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12/2003, the average real estate FOFs held $267.68 million in assets. The average fund

expense ratio was 1.26% and the average fund age was 7.8 years.

Because quarterly Morningstar files do not go back to 1987, the need to rely upon

the 2003 file creates a survivorship bias. Although the size of the bias is unknown, it is

positive because funds with poor performance records tend to fail. In the presence of

survivorship bias the alpha estimates of current study are inflated upwards by the absence

of terminated funds and the conclusion that real estate FOFs operate in a competitive

environment is even more robust.

Statistical Methods

The study performs two sets of statistical analyses. The first set involves a Monte

Carlo experiment. For each sample fund, this experiment compares the accumulated

return of the fund during its sample period to a large number of accumulated returns that

are based on a strategy of randomly selecting REITs. This strategy takes equally

weighted positions in the portfolio which is then rebalanced on a monthly basis. The

naïve strategy randomly selects one-half of all REIT returns available for that month in

the CRSP stock file.4 Portfolio positions are equal-weighted because this study is

interested in the question of whether real estate mutual fund managers on average

outperform their benchmarks. This experiment is repeated 1,000 times. Since the

empirical distribution of accumulated returns is obtained through the experiments,

statistical inferences can be conducted in the usual manner.

The second set of analyses involves time-series regressions based on two

specifications. The first specification is based upon the CAPM:

4 The study also tries strategies of investing in one-quarter, one-third, two-third, and three-quarter of available REITs. The results are not sensitive to these variations.

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Ri,t = i + bi Rm,t + i,t

where Ri,t is the excess return on sample fund i net of one-month T-Bill rate and Rm,t is the

excess return on the CRSP value-weighted portfolio net of one-month T-Bill rate.

The second specification uses the Fama-French three-factor model:

Ri,t = i + bi Rm,t + si SMBt + hi HMLt + i,t

where SMB is the difference between the returns on portfolios of small and big stocks,

and HML is the difference between the returns on portfolios of high- and low-BE/ME

(book-to-market ratio) stocks. These two models are used in this study because they have

been widely used in REIT and real estate mutual fund studies (Peterson and Hsieh 1997;

Kallberg, Liu, and Trzcinka 2000; Buttimer, Hyland, and Sanders 2005; Chiang, Lee, and

Wisen 2004, 2005; among many others).

Although performance evaluation is meaningful only when it is done on a risk-

adjusted basis, performance evaluation models are subject to the bad model problem

(Fama 1998). This caveat is particularly important for real estate funds because the asset

pricing of REITs is still in its nascent stage. To mitigate the inaccuracy associated with

the CAPM and the Fama-French model, this study examines the two specifications that

subtract the regression counterparts on the National Association of Real Estate

Investment Trusts (NAREIT) equity REIT returns:

Ri,t − RNAREIT,t = i + bi Rm,t + si SMB,t + hi HML,t + (i,t −NAREIT,t)

Ri,t − RNAREIT,t = i + bi Rm,t + si SMBREIT,t + hi HMLREIT,t + (i,t −NAREIT,t)

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Under these two specifications, i’s measure the incremental alphas due to active

selection of REITs. If managers’ active REIT selection adds value to real estate mutual

funds, one would expect i’s to be positive.5

Empirical Results

Monte Carlo results based on accumulated raw returns are depicted in Figure 1.

The histogram of p-values under the null of superior performance shows that 37 and 43

out of 55 sample funds are rejected at the 5% and the 10% level, respectively. That is,

the majority of real estate mutual funds yield returns that are no better than a simple

strategy of randomly investing in a large number of REITs. There are only three real

estate mutual funds that show consistently superior raw returns at the 5% level.

This result is quite surprising. Kallberg et al. find that real estate mutual fund

managers add value under standard asset pricing specifications by investing in small,

illiquid REITs. One would expect that real estate mutual funds should on average

produce higher returns than passively selected benchmarks because in equilibrium small,

illiquid investments should be compensated with higher returns. This expectation stands

in contrast to the empirical results. A second observation that is rather puzzling is that

the raw return distribution of real estate mutual funds appears to be polarized. There are

only a few real estate mutual funds that yield returns that are approximately on par with

randomly selected benchmarks. The majority of real estate mutual funds yield lower

returns than the benchmark and only a few of them generated higher returns than the

benchmark.

5 Note that this control mechanism is, at best, a weak one. One would prefer to evaluate real estate mutual funds’ performance directly under a well-specified model but as of yet this model does not exist.

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It is important to highlight that the ability of the CAPM or the Fama-French

model to describe REIT returns is rather low. Table 2 reports the time-series regression

results of NAREIT equity REIT returns based on the two specifications. During the

sample period of 01/1982 to 12/2003, the R-squared values under the two specifications

are 24.60% and 40.19%. The alphas for the passive portfolios are 4.78% and 2.42% per

annum under the CAPM and the Fama-French three-factor model, respectively. The

corresponding t-statistics are 2.09 and 1.20. These alphas are economically significant

and their magnitudes are in line with Kallberg et al’s estimates that used active real estate

mutual fund returns. It is clear that testing control mechanisms are needed to mitigate the

positive alpha bias under the two standard asset pricing models.

Table 3 summarizes the regression results for each real estate mutual fund under

the CAPM and the Fama-French three-factor model. As expected, without any control

mechanism, the average alpha is positive and large, and has a value of 7.96% per annum.

The average t–statistic is 1.65. A t-test for a population mean on these 55 alpha estimates

yields 14.30, which is statistically significant at the 1% level. These results indicate that

real estate funds have statistically superior performance under the CAPM.

The average alpha is 4.78% per annum under the Fama-French three-factor

model. The average t–statistic is 1.12. A t-test for a population mean on these 55 alpha

estimates yield a static of 11.09, which is statistically significant at the 1% level. The

average R–squared is 29.93%. The estimates overall are quite close to those results in

Kallberg et al.

Table 4 reports the regression results with the proposed control mechanisms.

The incremental alphas due to active selection of REITs are 0.24% and 0.60% per annum

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under the CAPM and the Fama-French three-factor model, respectively. The average t-

statistics of these incremental alphas are 0.07 and 0.43, respectively. The t-statistics for

testing a population mean are 0.91 and 2.23, respectively. Overall, there appears no

superior performance from real estate mutual fund managers’ active REIT selection.

One interesting result is that the differential loading on the SMBREIT factor is

positive and has a value of 0.576. The sign indeed indicates that real estate mutual funds

tend to invest in small, illiquid REITs. This result, together with the performance

distribution shown in Figure 1, suggests that there might be a few strong performers that

might skew average incremental alpha estimates. An examination of the 55 incremental

three-factor alphas indicates that the largest three incremental alphas are 12.68%, 7.31%

and 3.66% per annum.

Further Checks

A standard robustness check for time-series regressions is to evaluate calendar-

time regressions. Sample fund returns are aggregated into a portfolio and the monthly

time-series are regressed under the previous specifications. An additional benefit for

applying this robustness check is that it accounts for the cross-correlation in alphas.

The test results for the portfolio of aggregated real estate funds under the CAPM

and the Fama-French three-factor model are reported in Table 5. The results are similar

to those reported in Table 3. The average alphas under the CAPM and the Fama-French

three-factor model are 3.91% and 1.81% per annum, respectively. The t-statistics for

testing a population mean are 1.81 and 0.92 for the two specifications, and both are not

statistically significant at any conventional level. The R-squared values from the two

specifications are 28.57% and 40.34%, respectively.

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Table 6 reports the calendar-time regressions with the control mechanisms. The

incremental alphas under the CAPM and the Fama-French three-factor model become

negative, and are -0.84% and -0.60% per annum. Their t-statistics of -0.57 and -0.44

suggest that the skills of real estate mutual fund managers are not statistically different

from zero.

Conclusion

The study finds that the prior documentation of superior performance of real

estate mutual funds is quite sensitive to model specification. The study shows that the

usual strategy of investing in small, illiquid REITs employed by real estate mutual fund

managers does not lead to superior performance. Under two other performance

evaluation specifications, the study shows that real estate mutual funds do not produce

abnormal returns. These results are consistent with the mutual fund literature that fund

managers on average do not outperform their benchmarks.

As FOFs, real estate mutual funds provide administrative services and additional

diversification benefits. Their economic functions are important for promoting real estate

securitization, and their economic roles are unique. This study finds that the performance

of real estate mutual funds during the last two decades is consistent with an equilibrium

in which competition drives away abnormal returns.

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References

Brown, S.J., W.N. Goetzmann, and B. Liang. 2003. Fees on Fees in Funds of Funds. Working Paper. New York University.

Buttimer, R.J., D.C. Hyland, and A.B. Sanders. 2005. REITs, IPO Waves, and Long Run Performance. Real Estate Economics, forthcoming.

Chiang, K., K. Kozhevnikov, M. Lee, and C. Wisen. 2004. Another Look at the Asymmetric REIT-Beta Puzzle. Journal of Real Estate Research 26(1): 25-42.

Chiang, K., K. Kozhevnikov, M. Lee, and C. Wisen. 2005. On the Time-Series Properties of Real Estate Investment Trust Betas. Real Estate Economics, forthcoming.

Fama, E.F. 1998. Market Efficiency, Long-Term Returns, and Behavioral Finance. Journal of Financial Economics 49: 283-306.

Fama, E.F., and K.R. French. 1993. Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics 33: 3-56.

Kallberg, J.G., C.L. Liu, and C. Trzcinka. 2000. The Value Added from Investment Managers: An Examination of Funds of REITs. Journal of Financial and Quantitative Analysis 35: 387-408.

Lin, C.Y., and K. Yung. 2001. Real Estate Mutual Funds: Performance and Persistence. Journal of Real Estate Research 26(1): 69-95.

Lo, A. 2001. Risk Management for Hedge Funds: Introduction and Overview. Financial Analysts Journa 57: 16-33.

Peterson, J. and C. Hsieh. 1997. Do Common Risk Factors in the Returns on Stocks and Bonds Explain Returns on REITs? Real Estate Economics 25(2): 321-345.

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Table 1 ■ Summary statistics

Mean Median Standard Deviation

Monthly Return (%) 1.05 1.01 0.26Net Assets ($MM) 267.68 105.55 535.10Expense Ratio (%) 1.26 1.24 0.47Age (Year) 7.80 6.92 3.54Note: These summary statistics are base on 55 real estate mutual funds. The sample period is from 01/1982 to 12/2003.

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Table 2 ■ Time-series regressions of NAREIT equity REIT returns based on the CAPM and the Fama-French three-factor model

Estimate t-StatisticPanel A: The CAPMai 0.0039 2.09bi 0.3727 9.25R2 (%) 24.60Panel B: The Fama-French (1993) Three-Factor Modelai 0.0020 1.20bi 0.4286 11.11si 0.3572 6.34hi 0.3541 7.00R2 (%) 40.19Note: The regressions in Panel A are based on the following specification: Ri,t = i + bi Rm,t + i,t, where the dependent series is the excess return of NAREIT equity index net of one-month T-Bill rate. The independent variable is the market excess return net of one-month T-bill rate. The regressions in Panel B are based on the following specification: Ri,t = i + bi Rm,t + si SMBt + hi

HMLt + i,t. Explanatory factors consist of the market excess return net of one-month T-bill rate, and the SMB and the HML factors of Fama and French (1993).

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Table 3 ■ Time-series regressions

Mean Estimate t-StatisticPanel A: The CAPMai 0.0064

(1.65)14.30

bi 0.2572(3.57)

17.08

Average R2 (%) 12.30Panel B: The Fama-French (1993) Three-Factor Modelai 0.0039

(1.12)11.09

bi 0.2873(4.28)

18.01

si 0.2801(3.16)

24.21

hi 0.3002(4.23)

34.13

Average R2 (%) 29.93Note: The regressions in Panel A are based on the following specification: Ri,t = i + bi Rm,t + i,t, where the dependent series are the excess returns of 55 real estate mutual funds net of one-month T-Bill rate. The independent variable is the market excess return net of one-month T-bill rate. Average t-statistics from the 55 regressions are in parentheses. The regressions in Panel B are based on the following specification: Ri,t = i + bi Rm,t + si SMBt + hi HMLt + i,t. Explanatory factors consist of the market excess return net of one-month T-bill rate, and the SMB and the HML factors of Fama and French (1993). The last column reports t-statistics for one population mean based on the 55 sets of point estimates.

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Table 4 ■ Controlled time-series regressions

Mean Estimate t-StatisticPanel A: The CAPMai 0.0002

(0.07)0.91

bi 0.0387(0.81)

2.89

Average R2 (%) 4.60Panel B: The Fama-French (1993) Three-Factor Modelai 0.0005

(0.43)2.23

bi 0.0341(0.65)

2.64

si -0.0262(-1.38)

-2.50

hi -0.0388(0.13)

-5.15

Average R2 (%) 13.49Note: The regressions in Panel A are based on the following specification: Ri,t − RNAREIT,t = i + bi Rm,t + (i,t −NAREIT,t), where the dependent series are the differences between the excess returns of 55 real estate mutual funds and the excess returns of the NAREIT equity REIT returns. The independent variable is the market excess return net of one-month T-bill rate. Average t-statistics from the 55 regressions are in parentheses. The regressions in Panel B are based on the following specification: Ri,t − RNAREIT,t = i + bi Rm,t + si SMBt + hi HMLt + (i,t−NAREIT,t). Explanatory factors consist of the market excess return net of one-month T-bill rate, and the SMB and the HML factors of Fama and French (1993). The last column reports t-statistics for one population mean based on the 55 sets of point estimates.

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Table 5 ■ Calendar-time regressions

Estimate t-StatisticPanel A: The CAPMai 0.0032 1.81bi 0.3951 10.24R2 (%) 28.57Panel B: The Fama-French (1993) Three-Factor Modelai 0.0015 0.92bi 0.4561 12.04si 0.2605 4.70hi 0.3290 6.62R2 (%) 40.34Note: The regressions in Panel A are based on the following specification: Ri,t = i + bi Rm,t + i,t, where the dependent series is the excess return of the equal-weight portfolio of real estate mutual funds net of one-month T-Bill rate. The independent variable is the market excess return net of one-month T-bill rate. The regressions in Panel B are based on the following specification: Ri,t = i + bi Rm,t + si SMBt + hi HMLt + i,t. Explanatory factors consist of the market excess return net of one-month T-bill rate, and the SMB and the HML factors of Fama and French (1993).

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Table 6 ■ Controlled calendar-time regressions

Estimate t-StatisticPanel A: The CAPMai -0.0007 -0.57bi 0.0223 0.90R2 (%) 3.06Panel B: The Fama-French (1993) Three-Factor Modelai -0.0005 -0.44bi 0.0275 1.04si -0.0967 -2.50hi -0.0251 -0.72R2 (%) 2.64Note: The regressions in Panel A are based on the following specification: Ri,t − RNAREIT,t = i + bi Rm,t + (i,t −NAREIT,t), where the dependent series is the difference between the excess return of the equal-weight portfolio of real estate mutual funds and the excess return of the NAREIT equity REIT returns. The independent variable is the market excess return net of one-month T-bill rate. The regressions in Panel B are based on the following specification: Ri,t − RNAREIT,t = i + bi Rm,t

+ si SMBt + hi HMLt + (i,t−NAREIT,t). Explanatory factors consist of the market excess return net of one-month T-bill rate, and the SMB and the HML factors of Fama and French (1993).

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Figure 1 ■ The distribution of p-values under the null of superior performance

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