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Delivering Value: Drivers of Fund Performance

Delivering Value: Drivers of Fund Performance. Dr. Franz Fuerst University of Cambridge Professor George Matysiak Master Management Group & Henley Business School Wayne Lim University of Cambridge. Background.

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Delivering Value: Drivers of Fund Performance

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  1. Delivering Value: Drivers of Fund Performance Dr. Franz Fuerst University of Cambridge Professor George Matysiak Master Management Group & Henley Business School Wayne Lim University of Cambridge

  2. Background • Rapid growth of nonlisted real estate funds over last decade; now a major investment vehicle for gaining exposure to commercial real estate. • Academic and industry research has not kept up with this development. -> Very little ‘hard’ evidence on fundamental drivers of nonlisted fund returns. • This study aims to identify the factors driving the total return over a 10-year period using a rich database of almost 300 funds from 2001 to 2012 compiled by INREV. • Database contains data on total returns, fund attributes (size, vintage etc.) and detailed asset allocation information of nonlisted funds. • This database was enriched with information on IPD sector and market returns, GDP, stock and bond market performance in each country and other economic and financial indicators.

  3. Background (continued) • Panel data analysis allows us to track fund performance over time as well as make comparisons across funds. • Key research questions: • Do funds add value, i.e. how does fund performance compare to the (weighted) underlying direct property returns in each country and sector? • Which impact does gearing have? Is there an asymmetric impact when periods are broken down into upturns and downturns? • Do large funds generally outperform smaller funds? • Is there co-movement between the nonlisted sector and other asset classes such as stocks and bonds?

  4. Constructing an adequate Benchmark The Weighted Market Return (WMR) Example

  5. Distribution of Fund Returns

  6. Data – Fund Size & Number of Funds

  7. Data – Average Fund Return & WMR

  8. Data – Median Fund Return & WMR

  9. Data – Gearing Trend

  10. Data – Median Fund Return & WMR Gearing & Fund Returns

  11. Model 1 – Fund Age / Vintage FundAge1only = 1 year since inception of fund FundAge2only = 2 years since inception of fund …

  12. Model 1 – Fund Age / Vintage • Underlying sector and market returns (Weighted Market Returns, WMR) are highly significant for predicting fund-level returns. • As expected, funds tend to be more volatile than the underlying direct market. • No strong support found for hypothesis that age of less than 3 years is associated with significanctly lower returns (J curve effect) • These two factors alone explain about 43% of the variation in total returns both over time and across funds.

  13. Model 2 - Gearing GearingAve=average level of gearing in year y SmallMedium= fund size 2nd quartile MediumLarge= fund size 3rd quartile Large=fund size 4th quartile

  14. Model 2 – Gearing • Perhaps surprisingly, gearing is negatively associated with total returns at fund level. An effect of the financial crisis? • To analyse the effect of gearing in more depth, we analyse its impact separately for periods of positive and negative fund returns. • Many options for defining ‘good’ times and ‘bad’ times (above/below long-term fund return, above/below general market environment etc.)

  15. Model 2 – Gearing Asymmetric Effect Effects when Gearing is High and Low • Regression identifies ‘good times’ and ‘bad times’ separately • Finds that downside of gearing outweighs upside at all levels

  16. Model 3 – Competing Asset Classes *p < 0.05, **p < 0.01, ***p < 0.001

  17. Model 3 – Competing Asset Classes • Macro-economic environment (proxied by GDP growth) is a strong driver of nonlisted fund returns throughout the 2001-11 study period. • All three competing asset classes (aggregate EU bond market, EU stock market and EU REIT market) are positively linked to nonlisted fund sector. • Stock and REIT markets appear to have higher levels of significance for nonlisted sector than bond market.

  18. Conclusion & Further Work • Using a unique database and panel data analysis, this study presents first evidence of drivers of non-listed property fund returns over a 10-year period • Fund age, size, level of gearing and returns of competing asset classes are confirmed to be significant drivers • The downside negative effect of gearing is confirmed to outweigh the upside positive effect considerably FURTHER WORK: • Dynamic effects model • Quantifying manager skill of ‘picking winners’ both at sector/country and individual asset level • Using adjusted returns (return per unit of risk etc.) • Real Return (inflation-adjusted) • Gearing adjusted returns • Risk (volatility) adjusted return (Sharpe ratio etc)

  19. Thank you!For further information please email me atff274@cam.ac.uk

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