Measuring Institutional Investors’ Skill at Making Private Equity Investments

• Published date: 01 December 2019
• Date: 01 December 2019
• Author: YINGDI WANG,MICHAEL S. WEISBACH,DANIEL R. CAVAGNARO,BERK A. SENSOY
• DOI: http://doi.org/10.1111/jofi.12783

THE JOURNAL OF FINANCE •VOL. LXXIV, NO. 6 •DECEMBER 2019

Measuring Institutional Investors’ Skill at

Making Private Equity Investments

DANIEL R. CAVAGNARO, BERK A. SENSOY, YINGDI WANG,

and MICHAEL S. WEISBACH∗

ABSTRACT

Using a large sample of institutional investors’ investments in private equity funds

raised between 1991 and 2011, we estimate the extent to which investors’ skill affects

their returns. Bootstrap analyses show that the variance of actual performance is

higher than would be expected by chance, suggesting that some investors consistently

outperform. Extending the Bayesian approach of Korteweg and Sorensen, we estimate

that a one-standard-deviation increase in skill leads to an increase in annual returns

of between one and two percentage points. These results are stronger in the earlier

part of the sample period and for venture funds.

INSTITUTIONAL INVESTORS HAVE BECOME the most important investors in the

U.S. economy, controlling more than 70% of the publicly traded equity, much

of the debt, and virtually all of the private equity. Their investment decisions

have far-reaching consequences for their beneficiaries. Universities’ spending

decisions, pension plans’ ability to fund promised benefits, and foundations’

ability to support charitable endeavors all depend crucially on the returns they

receive on their investments. Surprisingly, however, little work has been done

to measure differences in investment skill across institutional investors.

One place where investment officers’ skill is potentially important is in se-

lecting private equity funds. The private equity industry has experienced dra-

matic growth since the 1990s, with total assets under management over $3.4

trillion in June 2013 (Preqin). Most of the money in this industry comes from

institutional investors, and private equity investments represent a substantial

∗Daniel R. Cavagnaro and Yingdi Wangare with California State University, Fullerton. Berk A.

Sensoy is at Owen Graduate School of Management, Vanderbilt University. Michael S. Weisbach

is with Ohio State University and NBER. Andrea Rossi provided exceptional research assistance.

We thank Arthur Korteweg; Ludovic Phalippou; Stefan Nagel; Andrei Simonov; Campbell Harvey;

Victoria Ivashina; Steven Kaplan; two referees; an Associate Editor; and seminar participants

at UC Berkeley, California State University, Fullerton, Georgia State University, University of

Hong Kong, Hong Kong Polytechnic University, University of Kansas, the 9th Annual London

Business School Private Equity Conference, NBER’s Entrepreneurship Working Group Meeting,

the Midwest Finance Association Meetings, University of North Carolina, Ohio State University,

Temple University, University of Southern California, University of Washington, and Vanderbilt

University for helpful suggestions. We have read the Journal of Finance’s disclosure policy and

have no conflicts of interest to disclose. No other party had the right to review the paper prior to

its circulation.

DOI: 10.1111/jofi.12783

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portion of their portfolios. Moreover, the variation in returns across private

equity funds is large, with the difference between top-quartile and bottom-

quartile returns averaging approximately 19 percentage points of internal rate

of return (IRR). Evaluating private equity partnerships, especially new ones,

thus requires substantial judgment from potential investors, who must assess

a partnership’s strategy, talents, experience, and even how the various part-

ners interact with one another. Consequently, the ability to select high-quality

partnerships is one place where an institutional investor’s talent is likely to be

particularly important.

In this paper, we consider a large sample of limited partners’ (LPs’) private

equity investments in venture and buyout funds and estimate the extent to

which manager skill affects the returns on their private equity investments.

Our sample includes 27,283 investments made by 1,209 unique LPs, each of

which has at least four private equity investments in either venture capital

or buyout funds during the 1991 to 2011 period. We first test the hypothesis

that skill in fund selection, in addition to luck, affects investors’ returns. We

then estimate the importance of skill in determining returns. Our main results

imply that a one-standard-deviation increase in skill leads to an increase in

IRR of approximately one to two percentage points. The magnitude of this effect

suggests that variation in skill is an important driver of institutional investors’

returns.

Our initial test of whether there is differential skill in selecting private

equity investments is model-free. We use a bootstrap approach to simulate

the distribution of LPs’ performance under the assumption that all LPs are

identically skilled. We measure performance first in terms of the proportion

of an LP’s investments that are in the top half of the return distribution for

funds of the same type in the same vintage year, and then in terms of average

returns across all of the LP’s private equity investments. Comparisons of the

distributions of actual performance with the bootstrapped distributions suggest

that more LPs do consistently well (above median) or consistently poorly (below

median) in their selection of private equity funds than what one would expect in

the absence of differential skill. Furthermore, statistical tests of the standard

deviation of LP performance show that there is more variation in performance

than what one would expect in the absence of differential skill. These results

hold when we restrict the analysis to subsamples by time period, fund, and

investor type, and when we impose different reasonable sampling restrictions

in creating the bootstrap distributions. Overall, the bootstrap analyses suggest

that more LPs are consistently able to earn abnormally high returns than

one would expect by chance. Some LPs appear to be better than other LPs at

selecting the general partners (GPs) who will subsequently earn the highest

returns.

To quantify the magnitude of LP skill, we extend the method of Korteweg

and Sorensen (KS) (2017). The KS model assumes that the net-of-fee return on

a private equity fund is determined by a firm-specific persistent effect, a firm-

time random effect that applies to each year of the fund’s life, a fund-specific

random effect, and various other controls. We first use this model to estimate

Measuring Institutional Investors’ Skill 3091

the firm-specific component that measures the skill of each GP managing the

private equity funds in our sample. We use these estimates to strip away any

idiosyncratic random effects from the returns on each fund, thereby adjusting

them so that they reflect only the skill of the GP. Then, using Bayesian regres-

sions, we estimate the extent to which LPs can pick high-skill GPs for their

investments. The estimation is conducted using Bayesian Markov Chain Monte

Carlo (MCMC) techniques, which allow us to measure the extent to which more

skillful LPs earn higher returns.

The results from the extended KS model imply that a one-standard-deviation

increase in LP skill leads to a one- to two-percentage-point increase in annual

IRR from their private equity investments. The effect is even larger for venture

capital investments, in which a one-standard-deviation increase in skill leads

to a 2- to 4.5-percentage-point increase in returns. Moreover, the effect declines

as the sample period progresses, consistent with related work on the maturing

of the private equity industry (Sensoy, Wang, and Weisbach (2014)). These

estimates highlight the importance of skill in earning returns from private

equity investments.

An alternative explanation for these results is that LPs have different risk

preferences. LPs with higher risk tolerance would take riskier investments

that lead to higher average returns. To evaluate whether differences in risk

preferences could lead to the differences in returns across LPs that we docu-

ment, we first evaluate whether the differences in performance differ within

types of investors. Presumably, LPs within the same type are more likely to

have the same risk preferences and investment objectives. Within each type,

we continue to observe more variation in LP performance than would be ex-

pected if LPs had no differential skill. Second, we conduct a test similar to

Andonov, Hochberg, and Rauh (2018) and break down the entire distribution

of returns by estimated skill level. If LPs with the highest estimated skill are

simply taking more risk, they should have the most risky or widest distribution

of returns. This is not the case. The LPs that we estimate to have high skill

outperform the LPs estimated to have low skill throughout the distribution of

returns, not just at the high end. Therefore, it does not appear that the pattern

we document of some LPs systematically outperforming others occurs because

high-performing LPs invest in riskier funds with higher expected returns.

In addition, it is possible that some LPs are pressured to invest in particular

funds that could affect their investment decisions and hence their returns.

In particular, Hochberg and Rauh (2013) find that public pension funds tend

to concentrate their investments in local funds, whereas Barber, Morse, and

Yasuda (2016) document that a number of LPs are pressured to invest in

“impact funds” that undertake socially responsible investments. Both of these

practices tend to lower returns. Of the LPs in our sample, public pension funds

likely face the greatest such pressures, since there is direct evidence that their

boards negatively affect the selection of private equity funds for reasons of

political expediency (Andonov, Hochberg, and Rauh (2018)). To evaluate the

importance of political pressure in explaining the difference in returns across

LPs, we first reestimate our model using a specification that allows for the