CITATIONS MADE EASY

REVIEW OF TWENTY YEARS OF RESEARCH IN PRIVATE EQUITY & MORE

I start with a short description of the contributions of each stream of work I have been part of. Next, I provide further details on these contributions and background of the work. All of these articles are freely available here.

First stream of academic work: Net Return and Risk of PE funds

 Posted in 2005: The performance of private equity funds, 2009, with O. Gottschalg, Review of Financial Studies 22(4):1747-1776.

First paper to simulate fee structures on PE fund cash flows, to show cost of different fee arrangements, and to translate fees in an annualized return equivalent; concludes that fees were 6% p.a. Also first paper to propose a method to calculate alphas of PE funds, to show negative correlation between fund duration and performance, and to Beta-adjust a PME. Performance statistics are likely inaccurate, other results have been replicated with other datasets. Well-cited, but usually not for any of the unique contributions. See details below.

Posted in 2007: A new method to estimate risk and return of non-traded assets from cash flows: The case of private equity funds, 2012, with T.C. Lin and J. Driessen, Journal of Financial and Quantitative Analysis 57(3): 511-535.

Extension of the alpha approach introduced in the above paper. Endogenizes Beta to compute Alpha. Idea is to search for Betas that best fit the underlying cash flow streams; identifying condition is ‘NPV is equal to zero across funds’, same identifying condition as Cochrane (2005). Introduces a Monte Carlo simulation to infer accuracy of estimated alpha and beta for different data generating processes. Benefits of the method of moment used here is that one could find Beta in Excel with a simple grid search, for the case of a single factor model (e.g. CAPM). Point estimates for Alpha and Beta are not as relevant due to the TVE dataset being used, but Beta estimates look reasonable and close to what has been found later on.

Posted in 2009: Private equity performance and liquidity risk, 2012, with F. Franzoni and E. Novak, Journal of Finance 67(6): 2341-2374.

First study and estimate of liquidity risk premium in PE. Dataset containing 4,403 LBO cash flow streams gross of fees by individual LBO investments. Estimates betas and alpha from a four factors model including liquidity risk. Liquidity risk premium: 3% annually. Beta estimates seem reasonable. Alpha gross of fees was zero. First paper to look at the cross-sectional determinants of LBO returns and to focus on macro variables. We find a tight relation between liquidity conditions during the life of an LBO and performance. Other variables such as credit spreads, aggregate volatility, industrial production growth, play a minor role or no role at all in explaining the cross section of LBO returns.

Posted in 2011: Performance of buyout funds revisited?, 2014, Review of Finance 18(1): 189-218.

Replicates results in the often-cited Harris, Jenkinson and Kaplan (2014), and shows high sensitivity to the choice of the benchmark. This paper shows, using Capital IQ data, that investments made by PE funds are in companies that are much smaller than the S&P 500; thus, according to asset pricing theory, the S&P 500 is not an appropriate benchmark. Another important contribution is the use of small cap benchmarks that are net of fees and traded. Points out that Russell 2000 has much lower returns than peer indices. Shows PE returns are close to small/mid cap US equity benchmarks over the sample period used in Harris, Jenkinson and Kaplan (2014). See details below.

Posted in 2013: Estimating Private Equity Returns from Limited Partner Cash Flows, with A. Ang, B. Chen, and W. Goetzmann, 2018, Journal of Finance 73(4), p1751-1783.

First paper to derive a set of sufficient assumptions for the condition ‘NPV is zero across funds’ to be an identifying condition for betas in a model-free setup. Key contribution is to not assume a factor model as in Cochrane (2005) and Driessen et al (2012); comes at the cost of using a Bayesian model (thus priors). What is unique in this paper is the ability to generate a time-series of true rates of returns that generated PE fund cash flows without using the self-reported NAVs of the PE funds. The exact point estimates of Alpha/Beta shown in the paper are not the key take-aways. The time-series of returns is what is unique. Other theoretically grounded method to estimate Beta without a time-series of returns, and not using intermediary NAVs, is Korteweg and Nagel (2018).

Second stream of academic work: PE Fund Investors (LPs) Due Diligence Issues

 Posted in 2008: Venture capital funds: Performance persistence and flow-performance relation, 2010, Journal of Banking and Finance 34, 568-577

First paper to argue that what has been called PE performance persistence is the correlation between returns of two successively raised funds by the same PE firm, but the two fund returns overlap for most of their life (and investment strategies are likely similar). The paper studies persistence ex ante (i.e. using performance of PE firms as of time they raise a new fund) and finds persistence only among losers. Also first paper to study the flow-performance relation in PE. Document a strong flow-performance, but only among winners. Shows that persistence in Kaplan and Schoar (2005) is no longer significant if requires 5 years between two successive funds.

 Posted in 2009: Giants at the Gate: Investment Returns and Diseconomies of Scale in Private Equity, 2015, with F. Lopez-de-Silanes and O. Gottschalg, Journal of Financial and Quantitative Analysis 50(3), 377-411.

First paper to assemble and study a large cross section of LBO investment returns. Shows distribution and performance across exit channels, duration, and countries. First paper to show lower returns for PE in developing countries and an average duration of 4 years. Shows evidence of ‘exit winners first and hold on to losers’, funds seem to target a multiple of money as investments have a similar multiple irrespective of their duration. Propose to judge risk of a set of PE investments by variance in the loss domain. Key driver of performance difference across funds is not fund size, but number of investments held in the portfolio at any point in time, an effect that is magnified when the PE firm is more hierarchical, consistent with theories of firm boundaries.

Posted in 2013: On secondary buyouts, 2016, with F. Degeorge and J. Martin, Journal of Financial Economics 120, 124-145.

First paper to document the return (on equity) obtained by buyers of secondary buyouts (SBOs). SBOs made by buyers under pressure to spend capital underperform. PE firms buying these SBOs raise less money going forward. SBOs where buyer and seller have complementary skill sets outperform other buyouts. Also discuss and document the situation of LPs who are on both sides of the SBO.

 Posted in 2014: The importance of size in private equity: Evidence from a Survey of Private Equity Limited Partner, 2017, with M. Darin, Journal of Financial Intermediation 31, 64-76.

First comprehensive survey of PE investors (LPs). Shows that investors with a larger capital allocation to PE are more specialized and have a wider scope of due diligence and investment activities. Other investor characteristics (experience, type, location, compensation structure, number of funds under management) play no role in explaining any of the differences in due diligence and monitoring activities. Shows how many days LPs spend on due diligence and their priorities when choosing funds, including reliance on IRR.

Posted in 2015: Private Equity Portfolio Company Fees, with M. Umber and C. Rauch, 2018 Journal of Financial Economics 129(3) p559-585.

First paper to empirically document existence of ex-post discretionary fees charged by fund managers on the assets they manage on behalf of LPs. This is the first type of tunneling documented for a population of institutional investors. These fee payments sum up to $20 billion evenly distributed over time, representing over 6% of the equity invested. Fees do not vary according to business cycles, company characteristics, or GP performance. Fees vary significantly across GPs and are persistent within GPs. GPs charging the least raised more capital post financial crisis, when these fees came in the media spotlight. Following this publication, these fees are said to be fully refunded. It shows that even in a developed market like the US and populated by institutional investors, wide scale tunneling can happen, absent a strong regulatory regime.

Third stream of academic work: Papers on PE for broader audiences

 Posted in 2007: Beware when venturing into private equity, 2009, Journal of Economic Perspectives 23(1): 147–166

First paper to describe the existence of ex-post discretionary fees charged by fund managers on the assets they manage on behalf of LPs, highlighting the significant conflicts of interest between fund managers and their investors in PE, and the lack of interest alignment despite the carried interest. Describes how and why some presentations of track records in fund raising prospectuses are misleading. Highlight that headline fees are not the most important driver of fee bills as huge variation exists in how fees are exactly computed (definitions are key). Contracts are extremely complex and do not enable investors to have a clear view on the fees they pay. Describes the incentives to exit winners first and hold on to losers, not just to increase carried interest, but also to manipulate IRRs.

Posted in 2008: The hazards of using IRR to measure performance: The case of private equity, Fall 2008, Journal of Performance Measurement

First paper to argue that IRR is deeply flawed in the context of PE due to the endogeneity of cash flows. Being able to exit winners first can generate an unrealistic re-investment assumption which then generate an unrealistically high IRR. Proposes MIRR and NPV as solutions.

 Posted in 2011: Yale’s Endowment Returns: Case Study in GIPS Interpretation Difficulties, Spring 2013, Journal of Alternative Investments

To illustrate the issue with IRR, the case of Yale Endowment is used. This is the first paper to show that the Yale model is based on an IRR, which exaggerates the true performance of their PE investment program. Their often cited 30% return is an IRR and is highly influenced by the large distribution that VC funds generated in the early 1990s. As a result, the return reported by Yale Endowment never changes from one year to the next (always about 30%) and their wealth accumulation is inconsistent with them earning 30% per year on their investments. Following this publication Yale changed how their report performance, confirmed that they were using an IRR, and added some disclaimers in their footnotes. As hypothesized in this paper, when Yale reported BO and VC separately, their BO returns were closed to those of the stock-markets but their VC returns were absurdly high (reaching 250% p.a. in 2019). This IRR issue is common-place in PE where nearly all PE firms put forward they since inception IRR to describe their track record, that number is often around 30% p.a. and hardly changes over time.

 Posted in 2011: A new approach to regulating private equity, 2012, with P. Morris, Journal of Corporate Law Studies, 12(1): 59-84

This paper reviews the Industrial Organization and Finance literature on price shrouding and shows that the PE industry features such price shrouding characteristics. PE funds, just like printers, have a headline price divorced from the total cost of the product. This prevents competition mechanisms to work, even if the two contracting parties are well-resourced. PE contracts are take-it-or-leave it rather than negotiated, made very complicated (and competition between lawyers is often about making them more complicated to increase cost) and not comparable to one another. The crucial role of a regulator is discussed in this context.

Posted in 2015: Aligning Financial Interests in Private Equity, 2020, Journal of Investing.

This paper shows that, at least in the traditional setup, there is a clear and sharp alignment of financial interest between a PE fund and the management team. Carried interest waterfalls use similar vocabulary and headline figures but offers a weak alignment of financial interests. This paper describes how the carried interest waterfall and definitions can be modified to better align financial interests between GPs and LPs, and this change would also make carried interest a capital gain.

Posted in 2019: Thirty years after Jensen’s prediction: Is private equity a superior form of ownership?, 2020, with P. Morris, Oxford Review of Economic Policy, 36(2): 291-313.

This paper points out that the limitations of the literature that looks at value-addition in PE via accounting data. Any comparison of growth rates (revenue, earnings, profit) are not comparable between PE and non-PE companies because of different payout rates and frequency of externally financed acquisitions. Using ratios such as return on asset is also problematic because total asset changes mechanically at the time of an LBO transaction, mainly due to goodwill treatments. In addition, in many instances, goodwill is amortized over the years following an LBO which mechanically bias the return on asset. In other words, we still do not know the magnitude of any gain in earnings following LBOs.

Posted in 2020: An inconvenient fact: Private Equity Returns & the Billionaire Factory, Journal of Investing, 2020.

This paper shows the performance of the PE industry, in aggregate, as of 2019. Computes the total Carry due as of that date for the performance delivers. Offers a comprehensive discussion on return measurement, empirical evidence, and fees.

Fourth stream of academic work: Other topics

 Posted in 2004: Can recent risk-based explanations explain the value premium?, 2007, Review of Finance 11(2):143-166

First paper to point out the existence of a factor zoo, and illustrated this claim by showing that prominent models proposed to solve asset pricing puzzles from a rational angle were not robust to a change in the test assets, indicating evidence of ‘model snooping.’

Posted in 2004: Where is the value premium?, Spring 2008, Financial Analyst Journal

Contemporaneous and independent work to Nagel (2005), showing that the value premium is only present in stocks with low institutional ownership (rather than being present only in small stocks), indicating that when limits to arbitrage are high, there is no anomaly; hence, risk-based explanations are unlikely to hold true. Unlike Nagel (2005), I found the result to hold true both for the long side and the short side.

Posted in 2012: Acquiring acquirers: New evidence on the drivers of acquirer’s announcement returns in corporate takeovers, with F. Xu, and H. Zhao, 2015, Review of Finance 19(4), 1489-1541.

First paper to document a striking empirical fact: Half of the value destroyed in all the M&As is due to a small subset of transactions (7%) in which the target was a serial acquirer. These acquisitions seem to be motivated by ‘eat in order not to be eaten’ and are highly value destroying. The acquisitiveness of the target company is the best predictive variable both for the probability that an acquisition will go through and for the announcement returns of the acquirer.

Posted in 2005: Mutual Funds and the Market for Liquidity, with M. Massa

We decided not to submit the paper any further after some initial failed attempts. This was the first paper to test for the existence of a liquidity risk premium among mutual funds: are mutual funds holding less liquid portfolios having higher returns? We found that it was not the case if we clustered standard errors correctly (to account for persistence in illiquidity) or estimated the model with a Fama-Mcbeth approach, but the effect was indeed strongly negative if we simply use pooled OLS. This no result was deemed uninteresting. Subsequent work showing the strong negative relation in a pooled OLS setup was published and our liquidity variable has been widely used in the literature (with no attribution).

Detailed Discussion

Posted in 2005: The performance of private equity funds, 2009, with O. Gottschalg, Review of Financial Studies 22(4):1747-1776.

As part of my PhD (2000-2004), I wrote several versions of the paper that ended up being called ‘Performance of Private Equity Funds.’ I started in 2002, presented it at the 2005 EFA, posted it on ssrn in 2005, and the paper was finally published, quite miraculously, in 2009.

This paper contains several contributions, but is usually not cited for either one of them. The paper is cited for finding low performance of PE funds. Sometimes, the result is attributed to a potential anomaly in the TVE dataset used in the paper. It is important to bear in mind that the dataset is the same as that of Kaplan and Schoar (2005) and the performance statistics in the two papers are the same. Kaplan and Schoar (2005) report a PME for LBOs of 0.93. The key difference is that they report a PME for VCs of 1.21, but this is due to a coding error which I never reported on, except to the referee and editor of our paper. I had the same dataset and could match their yearly number of observations for VC funds, and reported performance, only if I was not applying their last data filter (the one on inactivity). For LBO funds I matched their yearly number of observations and performance figures. Their main result, on performance persistence, is not affected by coding error.

Our paper was the first to report an odd pattern in TVE data: some funds had no cash flows and their NAVs was the same from one quarter to the next. An agreement with the data provider did not allow us to question the quality of the data and the data provider told us that these were the correct data points. If the data was correct, then these mature funds had no exit for a prolong period of time and repeated their NAVs. A reasonable choice was then to assume that after three years with no cash flows, these NAVs were worthless. Often the finding of underperformance is attributed to this choice. It turns out that this data issue was there only for VC funds. VC funds that had high returns until 2000, then did not have any cash flows for three years, but their 1999-2000 NAVs stayed at the same level.

For LBO funds, this filter made no difference. This is why all the performance on LBO funds are the same as in Kaplan and Schoar (2005). All the correction for weighting of vintages, NAV treatment, and sample selection correction have a negligible effect on LBO figures.

The high PME for VCs in Kaplan and Schoar (2005) is due to them not applying the inactivity filter that they themselves proposed, to VC funds. If they had, they would also have reported low returns for VC funds as well. But these are just the descriptive statistics in our paper, and this is not where the contributions lie, especially since, as just mentioned, these statistics are the same as in Kaplan and Schoar (2005).

Another interesting anecdote is that using NPV to judge whether a series of cash flows is more than what would have been generated by an investment in a benchmark time-series of rates of return (will just call it benchmark) is proposed in any corporate finance textbook. Using the ratio, rather than the difference in present value of investments and divestments, is also proposed in nearly any corporate finance textbook. Back in 2002, we opted to call the ratio of present value the Profitability Index because this is how it was called in the Brealey-Myers textbook back then. Ljungqvist and Richardson who also had a dataset of PE fund cash flows at that time, used the same measure and the same name. Interestingly, this ratio of present value is now always referred to as Kaplan-Schoar PME.

Our paper also proposes the first measure of ‘alpha’ in this context by proposing to add a constant the benchmark returns. The figure needed to bring NPV to zero is called alpha in our paper. Nowadays, most papers report both PME and ‘direct alpha’, the latter being pretty much computed in this fashion but no reference is made to Phalippou and Gottschalg (2009) for it. Instead, what is cited is sometimes a 2014 unpublished paper by Gredil and two practitioners which is a thorough discussion of this approach. In the working paper version of Phalippou and Gottschalg (2009), a footnote discussed the properties of this alpha and demonstrated it was valid only as alpha tended towards zero. Intuitively, if alpha is large the re-investment assumption kicks in and exaggerates the alpha (in either direction). As this is a major drawback, this construction of an alpha in our paper was not pushed as an important contribution.

What are the key contributions then?

It is the first paper to attempt a risk correction by estimating a Beta and modifying the PME calculations by using this Beta-corrected PME. Many papers did this afterwards, with no reference to our paper as far as I can tell.

An extra dataset enabled to correct partially for sample selection biases and it was shown that the key result in Kaplan and Schoar (2005) was still holding true after such a correction.

The most important contribution is to be the first simulation of a fee structure using actual net of fees cash flows, and to translate the fee cash flow stream into an annualized number (the 6% per annum). In addition, we showed the sensitivity of fees to key aspects of the fee contract, and that fees mainly come from the fixed component (rather than the performance-related component). The finding of the 6% annual fees was confirmed in subsequent work in different ways, but again usually without reference to our paper, same for the predominance of fixed fees (although the exact fraction depends on overall performance, hence is cycle dependent), and the simulation exercise.

Finally, our paper is the first to point out important issues with IRR. It is usually known that IRRs cannot be averaged, but we were the first to show a negative correlation between fund duration and performance, which meant that any averaging was systematically upward biased. The negative correlation duration-performance is also an important robust stylized fact that is important for other applications. Our paper is also the first to discuss how practitioners present performance and may be mislead by IRRs, and make the mistake of comparing stock market average geometric returns to IRRs. There was also a discussion of benefits other than returns offered by investing in PE funds.

Posted in 2007: A new method to estimate risk and return of non-traded assets from cash flows: The case of private equity funds, 2012, with T.C. Lin and J. Driessen, Journal of Financial and Quantitative Analysis 57(3): 511-535.

Idea is to search for Betas that best fit the underlying cash flow streams as judged by the condition NPV is equal to zero across funds. The first paper to do this is Cochrane (2005). We use a method of moments whereas Cochrane (2005) uses maximum likelihood and he works with individual investments, and we had fund level data. The latter matters because this approach does not work well when there is a high degree of idiosyncratic risk, something that I will prove much later in a paper described below. As the literature on estimating risk of non traded assets like PE funds is thin, this paper like the others below on risk, is not highly cited. The Monte Carlo simulation to verify that one can retrieve the alpha and beta of the data generating process is a useful contribution. The attraction of the method of moment is that one could find a Beta in Excel with a simple grid search (for the case of a single factor model, like the CAPM). The point estimates are indeed not as interesting however due to the TVE dataset being used and the limitations highlighted above. That said, the Beta estimates look reasonable.

Posted in 2009: Giants at the Gate: Investment Returns and Diseconomies of Scale in Private Equity, 2015, with F. Lopez-de-Silanes and O. Gottschalg, Journal of Financial and Quantitative Analysis 50(3), 377-411.

This paper was rejected in each of the top 3 journals, and also lost an appeal at the RFS. The reason was that even though we had assembled the largest dataset of individual LBO returns, as these data came from fund raising prospectuses, they were biased. We conduct several tests to tackle this issue and also argued that there were few large enough LBO firms that went out of business for it to be an issue. Also, several results were not biased one way or the other by this feature of our data. Interestingly, few years later, several papers were published in top journals using the same data source: fund raising prospectuses of PE firms (e.g. Braun, Jenkinson and Stoff, JFE 2016). Again, this paper contains several key descriptive statistics such as investment duration, correlation of duration and performance that were unique and important to understand the industry.

Posted in 2009: Private equity performance and liquidity risk, 2012, with F. Franzoni and E. Novak, Journal of Finance 67(6): 2341-2374.

Given the TVE dataset issues, we obtained a different dataset containing 4403 LBO cash flow streams gross of fees by individual LBO investments. The big problem to assess risk and return of PE funds is intermediary cash flows (in the absence of frequent market valuation). We always need to make a re-investment assumption. For example, when using KS-PME the implicit assumption is that all cash flows are invested into the S&P 500 until liquidation. The advantage of working with individual investments in the LBO industry is that although there are intermediate cash flows, they tend to be relatively small and infrequent. Hence, in this paper we simply compute a modified IRR assuming that intermediaries cash flows were re-invested into the S&P 500 and verified that results were similar with IRR or other choices. We then had a single investment and divestment for each observation, and that allowed us to use standard techniques to infer the risk exposure of LBO investments.

It has happened that people claim that our results are driven by our use of MIRR despite us having verified it does not and despite the fact that intermediary cash flows are small and cannot drive results.

The main findings here is the betas obtained on four factor model including liquidity risk. We are the first to estimate an annual liquidity risk premium. We find it to be 3% annually. Subsequent studies propose a similar figure but do not seem to often refer to our contribution. The betas we obtain seem reasonable and with these Betas, the alpha gross of fees was zero.

Another important contribution is that this paper is the first to look at the cross-sectional determinants of LBO returns and focus on macro variables. We find a tight relation between liquidity conditions during the life of an LBO and performance. Other variables such as credit spreads, aggregate volatility, industrial production growth, play a minor role or no role in explaining LBO returns. This result is hardly ever cited, if at all.

Posted in 2011: Performance of buyout funds revisited?, 2014, Review of Finance 18(1): 189-218.

Shows that the results in the often-cited Harris, Jenkinson and Kaplan (2014), winner of the prestigious Brattle Group Prize, are very sensitive to the choice of the benchmark. In particular, this paper shows, using Capital IQ data, that investments made by PE funds are in companies that are much smaller than the S&P 500 and, as a result, the S&P 500 cannot be used as a benchmark. Apparently, the counter-argument has been that the S&P 500 is the opportunity cost for large investors. First, if results and conclusion depends on investor size, this is worth knowing and caveating. Second, asset pricing theory usually think of the benchmark as what a ‘monkey’ would have achieved. When buying and selling a company, the monkey would have transaction at what the comparable companies would have been valued. If the monkey invests in small companies, chances are that the difference between the buying and selling price would have matched the change in valuation of small publicly traded companies. This point, however, seems to gain acceptance now that the S&P 500 is no longer in a period of abnormally low performance (as it was from mid 1990s to 2007), but in a period of high abnormal performance. Yet, instead of switching to more closely related benchmarks, the literature seems to favor one that is further afield: MSCI world.

Another important contribution is the use of small cap benchmarks that are net of fees and traded, rather than using the commonly used Russell 2000, which displays a significant lower performance than peer indices. Another contribution is to show that Beta-adjusting the PME does impact performance unlike what had been argued in other studies. When using the S&P 500 during a period of low performance, Beta-adjustments naturally had little impact on PE performance. Using a different benchmark, like small and mid-cap stock indices, the Beta adjustment does have a large impact.

Posted in 2013: Estimating Private Equity Returns from Limited Partner Cash Flows, with A. Ang, B. Chen, and W. Goetzmann, 2018, Journal of Finance 73(4), p1751-1783.

The key contribution of this paper is to derive the exact conditions under which searching for Betas such that NPV is zero does deliver the Beta that generated the cash flows in a linear multi factor model. This is where we see that data need to be aggregated at least at the fund level because idiosyncratic volatility generates a systematic bias. This bias is therefore expected to be higher for VC, for example. The other key contribution is to not assume a factor model as in Driessen et al (2012). It is easier to identify Beta if we specify a model ex ante, but if we do not want this joint hypothesis then we need another type of restrictions to narrow down the searches. This is what we did by using a standard Bayesian approach (MCMC). As a result, what is unique in this paper is the ability to generate a time-series of true rates of returns that generated PE fund cash flows without using the self-reported NAVs of the PE funds. Once this time-series is obtained we can do anything like looking at how different types of PE funds correlate with inflation (this is often a selling argument for real estate for example). We did not find much evidence of good inflation hedging properties.

The exact point estimates of Alpha/Beta shown in the paper are not the key take-aways. The time-series of returns is more interesting and unique. Overall, the key contribution is methodological but as not many people seem interested in devising risk measures for non-traded assets. An exception is Korteweg and Nagel (2018), who propose the only (theoretically grounded) alternative I know of for estimating the risk of PE funds. The advantage of their approach is that they can provide a simple code for people to risk-adjust PE returns. The MCMC code does not have this benefit.

Posted in 2014: The importance of size in private equity: Evidence from a Survey of Private Equity Limited Partner, 2017, with M. Darin, Journal of Financial Intermediation 31, 64-76.

Fairly ironically, paper was desk rejected at the JFE on the basis that the JFE does not publish survey-based research. Our paper is a comprehensive survey of how institutional investors carry their due diligence when investing in a PE fund and how they monitor them. This was the first survey of this type. A few years later, the survey-based paper of Gompers et al. (2020), which looks at how PE funds carry due diligence on their investments and how their monitor these investments, was judged of interest to the JFE which accepted the paper.