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Financial Markets and Portfolio Management

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Open Access 08-04-2017

Searching for a listed infrastructure asset class using mean–variance spanning

Authors: Frédéric Blanc-Brude, Timothy Whittaker, Simon Wilde

Published in: Financial Markets and Portfolio Management | Issue 2/2017

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Abstract

This study examines the portfolio-diversification benefits of listed infrastructure stocks. We employ three different definitions of listed infrastructure and tests of mean–variance spanning. The evidence shows that viewing infrastructure as an asset class is misguided. We employ different schemes of infrastructure asset selection (both traditional asset classes and factor exposures) and discover that they do not provide portfolio-diversification benefits to existing asset allocation choices. We also find that defining and selecting infrastructure investments by business model as opposed to industrial sectors can reveal a very different investment profile, albeit one that improves the mean–variance efficient frontier since the global financial crisis. This study provides new insights into defining and benchmarking infrastructure equity investments in general, as well as into the extent to which public markets can be used to proxy the risk-adjusted performance of privately held infrastructure investments.

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See Newell and Peng (2007, 2008), Finkenzeller et al. (2010), Newell et al. (2009), Rothballer and Kaserer (2012), and Bitsch (2012).

Using the same sample as Rothballer and Kaserer (2012), Rödel and Rothballer (2012) examine the inflation-hedging ability of infrastructure. They find no evidence to suggest infrastructure exhibits a greater ability to hedge inflation risks than do listed equities. Even restricting their sample to firms with assumed strong monopoly characteristics fails to yield a statistically significant result.

PFI projects consist of dedicated project firms entering into long-term contracts with the public sector to build, maintain, and operate public infrastructure facilities according to a pre-agreed service output specification. As long as these firms deliver the projects and associated services for which they have contracted, on time and according to specifications, the public sector is committed to pay a monthly or quarterly income to the firm according to a pre-agreed schedule for multiple decades. In the UK, the long-term contract between the public and private parties also stipulates that this “availability payment” is adjusted to reflect changes in the retail Price Index (RPI). Each project company is a special-purpose vehicle created solely to deliver an infrastructure project and financed on a nonrecourse basis with sponsor equity, shareholder loans, and senior debt.

Such factors include the Fama and French (1992) size and value premiums, the term and default premiums (Fama and French 1993), and the momentum anomaly identified by Jegadeesh and Titman (1993). Bender et al. (2010) show that these premiums are uncorrelated with each other and that they increase returns and reduce portfolio volatility over traditional asset class allocations. Likewise, when comparing the diversification benefits of factor-based allocations to alternative assets, Bird et al. (2013) find that factor approaches tend to outperform alternative asset classes. For recent and in-depth analyses of factor investing, see Amenc et al. (2014).

Kan and Zhou (2012) state that if $N \ge 2$, the appropriate formation of the test statistic is given as ${ HK}=\left( {\textstyle \frac{1}{V^{\frac{1}{2}}-1}} \right) \left( {\frac{T-K-1}{2}} \right) $.

As another robustness check, we employ the Gibbons et al. (1989) test of portfolio efficiency. The results are similar to the mean–variance spanning test results presented in this paper. The results are available upon request.

The SIC and GIC codes used to identify infrastructure are available upon request.

The minimum revenue by infrastructure type is reported by SIC or GIC code by Worldscope. This is a crude measure, as it relies on the continuous updating of the revenue codes by Worldscope, as well as on the assumption that GIC or SIC codes represent infrastructure activities.

The number of firms identified as well as their geographic and industry distributions are available upon request.

Extreme monthly returns are identified following Ince and Porter (2006) and set to a missing value. Ince and Porter (2006) set an arbitrary cutoff of 300% for extreme monthly returns. If R1 or $Rt-1$ is greater than 300% and $(1 + R1)/(1 + Rt-1) -1$ is less than 50%, then R1 or $Rt-1$ are set to missing. Furthermore, following Rothballer and Kaserer (2012), 18 months of nonzero returns are required for the stock to be included in the portfolios. Any Datastream-padded price is removed by requesting X(P#S) $U, which returns null values when Datastream does not have a record and any nonequity item is removed by requiring the TYPE description in Datastream to be equal to EQ.

The maximum drawdown statistic is measured as a percentage of maximum cumulative monthly return in the sample period.

The MSCI World Index is a free-float-adjusted market-capitalization-weighted index comprising 1631 mid-size and large capitalization stocks across 23 developed-country equity markets. MSCI states that the index comprises 85% of the free-float-adjusted market capitalization of each country covered. The index is updated quarterly with annual revisions to the investable universe and the removal of stocks with low liquidity.

A brief summary of the indices is available upon request.

The Russell 3000 Index was selected for the US equity market index for two reasons. First, it represents the top 3000 stocks by market capitalization (FTSE Russell 2016). This represents a significant proportion of the investable universe of US stocks. Second, for consistency, in the factor-exposure studies we employ the Russell indices to create the factor proxies.

In future research, a similar test of mean–variance spanning against efficient or “smart” reference indices is necessary to control for such effects.

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Title: Searching for a listed infrastructure asset class using mean–variance spanning
Authors: Frédéric Blanc-Brude
Timothy Whittaker
Simon Wilde
Publication date: 08-04-2017
Publisher: Springer US
Published in: Financial Markets and Portfolio Management / Issue 2/2017
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-017-0286-z

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