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Performance and Persistence in Private Equity Infrastructure Funds

Performance and Persistence in Private Equity Infrastructure Funds Annual Private Capital Conference Montreux, 27-28 th June 2019. Martin Haran – Ulster University Stanimira Milcheva – University College London Daniel Lo – Ulster University. Research Context.

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Performance and Persistence in Private Equity Infrastructure Funds

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  1. Performance and Persistence in Private Equity Infrastructure Funds Annual Private Capital Conference Montreux, 27-28th June 2019. Martin Haran – Ulster University StanimiraMilcheva – University College London Daniel Lo – Ulster University

  2. Research Context Dearth of research examining the performance of PEIFs. New market entrants with varying degrees of expertise and competence entering the sector. Performance revisions of post-GFC funds. Inability of funds to execute deals – dry powder. Contrasting motivations Brownfield versus Greenfield. The appropriateness of the PEIF model for Infrastructure.

  3. Private Equity Infrastructure Fund (PEIF) Universe • 28 PEIFs achieved financial close during 2000-2004 vs 858 during 2005-2018. • Value of the PEIF universe has increased by more than quadrupled over the past ten years. (USD 100 billion in 2007 vs USD 418 billion in 2017). • New market entrants (e.g. institutional investors such as insurance companies and pension funds) have added to the diversity and depth of PEIFs. • McKenzie Global Institute (2016) estimated infrastructure investment need at USD3.3 trillion annually through to 2030 – a shortfall of USD 350 billion per annum.

  4. Literature Review Kaplan and Scholar (2005) the seminal work exploring performance persistence within the PE funds universe. Page et al. (2008): PEIFs should be utilised more effectively in serving to redress the infrastructure funding gap – including the provision of greenfield assets. Orr (2009) highlighted the added value of PE investments contributing to a faster, cheaper and better infrastructure project delivery. Chowdhury et al. (2009) highlight that the PEIF structure represented an important new point of access for institutional investors into infrastructure projects. Gatti and Della Croce (2015) examined investor sentiment with the performance of PEIFs during the post-GFC economic cycle.

  5. Data Barriers and Challenges • Uneven disclosure performance data (esp. for cash flow data). • Absence of stringent reporting structures and consistent disclosure timeframes. • Data collection methods by different organisations are different and at times inconsistent. • Use of different performance indicators (IRR, Gross return, TVPI, NAV etc). • Data disclosures are by and large confined to N. American domiciled funds.

  6. Data Overview Preqin PEIF Universe: • 1,229 Funds (November 2018) • 728 PEIFs with performance reported data • 72 of which have cash flow data Investigation period: 2000-2014. Recent funds vintage (2015-2019) are intentionally removed to avoid statistical bias. IRR, PME and TVPI are computed for Funds 72 using Preqin’s cash flow data.

  7. Fund Overview by Vintage

  8. Fund Investment Strategy

  9. Fund Domicile

  10. Fund Focus (Sector)

  11. Fund Focus (Region)

  12. PME of Funds 72

  13. IRR of Funds 72

  14. TVPI of Funds 72

  15. Methodology

  16. Main Findings – Primary and Successor Funds Subsequent funds tend to outperform their preceding counterparts (for sequence variables constructed based on sister funds (Model 5 and firm IDs Models 1-3) (statistically significant at the 5% level). There is a statistically significant diminishing marginal effect on PME as sequence number grows (statistically significant at the 5%) as revealed by the negative coefficient on the squared term of the sequence variable. The performance of first-time funds is relatively weaker than that of follow-on funds (Model 7 - statistically significant at the 5% level). We didn’t find any statistical evidence that fund size has an impact on performance (statistically insignificant at the 5% level). Spline analysis further confirms this.

  17. Main Findings - Geographic and Sector Specialisation Geographic concentration seems to have a positive impact on PME (statistically significant at the 10% level – Model 8). Funds that focus on the U.S. market carry a performance premium (statistically significant at the 5% level). Funds that specialised by infra sectors could capture superior returns (statistically significant at the 5% level). Results of the IRR models are largely consistent with those of PME models, although the latter models exhibit much better inference powers in terms of R-2 and t-statistics of individual variables.

  18. Persistence of Fund Performance

  19. Relationship between PEIFs and Stock Market Regress the (log) of aggregate annual number of new PEIFs (N) and aggregate amount of capital raised by new PEIFs (C) on the performance of stock markets (S&P500 and NASDAQ) for the period of 1991 – 2014. N is positively correlated with S&P500(t) but negatively correlated with S&P500(t-1), albeit statistical insignificance. IRR and lagged IRR of the PEIF market have a stronger influence on the creation of new PEIFs than the two stock market variables do (statistically significant at the 5% level). In addition, it’s found that N can be explained by the performance of the PEIF market more than by the stock markets in view of the models’ R2 and adjusted R2.

  20. Relationship between Business Cycle and PEIF Market We test with a linear probability model whether the likelihood that an existing fund raises a follow on fund (P) depends on the stock market returns (NASDAQ and S&P500) and IRR of PEIFs. The coefficients on NASDAQ(t) and S&P500(t) are positive and statistically significant. This suggests that PEIFs started in boom periods are more likely to outperform and therefore are able to raise follow-on funds. The positive and significant coefficient on “fund sequence” further indicates that funds with a longer history tend to be more capable of raising a new fund. The positive and significant coefficient on “fund size” implies that larger funds are more prone to raise a new fund.

  21. Conclusions Results are akin to Jenkinson et al. (2013) who found that the IRR performance figures reported by PE funds pre-liquidation had little power to predict ultimate returns whilst using PME measures served to increase predictability. There appears to be a ‘learning curve’ at play within the PEIF universe and this plays out for example in ability to acquire assets. Persistence is present but this has a diminishing impact over time. The PEIF market does seem to exhibit diversification potential relative to the wider equites market potentially offering diversification. More work needed on the potential counter-cyclical plays of PEIFs within the PE fund universe relative to buy-out and M&A funds.

  22. Contact Details Martin Haran – m.haran@ulster.ac.uk StaniMilcheva – s.milcheva@ucl.ac.uk Daniel Lo – d.lo@ulster.ac.uk

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