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«Robert J. Bianchi*, Michael E. Drew and Timothy Whittaker Department of Accounting, Finance and Economics Griffith Business School Griffith ...»

-- [ Page 1 ] --

The Predictability of Australian Listed Infrastructure and

Public-Private Partnership Returns Using Asset Pricing

Models

Robert J. Bianchi*, Michael E. Drew and Timothy Whittaker

Department of Accounting, Finance and Economics

Griffith Business School

Griffith University

Nathan, Brisbane, Queensland

AUSTRALIA

Version: 31st August 2014

Abstract:

Can asset pricing models predict the future returns of publicly-listed infrastructure and public-private partnerships (PPPs) in Australia? We find that asset pricing models exhibit poor out-of-sample predictive performance when compared to simple, fixed excess return models for the period 1997 through 2012. Similar to the work of Simin (2008) for the U.S., we suggest that using the long-term historical mean return may be a reasonable starting point for superannuation funds seeking to understand the long-term expected returns of publicly-listed infrastructure and PPPs.

Keywords: Asset Pricing, Investment Decisions, Infrastructure.

JEL Codes: G11, G12 * Corresponding author. Email: r.bianchi@griffith.edu.au; Tel: +61-7-3735 7078; Fax: +61-7-3735 3719; Postal address: Griffith Business School, Department of Accounting, Finance and Economics, Nathan campus, Griffith University, 170 Kessels Road, Nathan, Queensland, 4111, Australia. This research was supported by the CSIRO-Monash Superannuation Research Cluster, a collaboration between CSIRO, Monash University, Griffith University, the University of Western Australia, the University of Warwick, and stakeholders of the retirement system in the interest of better outcomes for all. Any remaining errors or ambiguities are our own.

The Predictability of Australian Listed Infrastructure and Public-Private Partnership Returns Using Asset Pricing Models

1. Introduction It has long been recognised that well designed infrastructure investments deliver longterm benefits to investors and the broader economy alike (Demetriades and Mamuneas, 2000; Heintz, 2010; Hulten, 1996; Kamps, 2001, 2004; Munnell, 1992).1 A number of studies suggest that infrastructure investments are low-risk due to: their regular income streams (Newbery, 2002; Rothballer and Kaserer, 2012); the nature of long term contracts and high asset-specificity of investments in the infrastructure industry (Dong and Chiara, 2010); their position in lowly competitive markets due to high barriers to entry (Regan, Smith and Love, 2011a; Sawant, 2010); and the higher regulatory constraints that infrastructure firms operate in (Beeferman, 2008). The work of Newbery (2002) and Rothballer and Kaserer (2012) suggests that infrastructure investments are low risk due to the steady income stream inherent in these long-life assets.

Against this backdrop, this study empirically examines the predictive (or otherwise) performance of conventional asset pricing models on Australian publicly-listed infrastructure and public-private partnership (PPP) investment returns. There are three motivations for this kind of analysis. First, the works of Lewellen and Nagel (2006), Simin (2008) and Welch and Goyal (2008) in the U.S. setting demonstrate that asset pricing models are poor predictors of U.S. stock returns. There is a scarcity of this type of research in the Australian setting and the predictability of infrastructure and PPP returns has never been examined in the literature. Second, the low-risk perception of infrastructure and PPPs makes it a perfect candidate to evaluate the predictive performance of Australian asset pricing models. If infrastructure and PPP risks are lower than conventional equity investments then there is an increased probability that Australian asset pricing models may be able to forecast infrastructure/PPP returns. Third, and perhaps most importantly, respective However, we also acknowledge that there are a number of recent studies in Australia and globally that have documented poorly performing infrastructure investments (Cantarelli, Flyvbjerg, Molin and van Wee, 2010; Flyvbjerg, 2009; Regan, Smith and Love, 2011a and 2011b).

Australian governments employ the principles of the Capital Asset Pricing Model (CAPM) (Sharpe, 1964; Lintner, 1965; Mossin, 1966) in their evaluation of PPPs to finance new infrastructure investments. Governments in Australia employ the Infrastructure Australia (2013b) guidelines for the development of discount rates for PPP infrastructure projects. This study will allow us to assess the efficacy of using the CAPM in this public policy setting.

Our results suggest that Australian asset pricing models are poor predictors of future publicly-listed infrastructure/PPP returns. In fact, a simple fixed excess return model provides similar or better forecasts than conventional asset pricing models in many cases. Our initial tests before the 2008 Global Financial Crisis from 1997-2007 shows that the conventional asset pricing models (including the CAPM and Fama-French three factor model) deliver similar or lower levels of predictive performance than simple fixed excess return models. We then continue the test from 1997-2012 and find that the best predictor of infrastructure returns is a fixed excess return model of 10% per year. We proceed as follows. Section 2 reviews the literature relating to asset pricing and infrastructure returns. Section 3 explains the methodology employed in this study. Section 4 describes the data used in the study while Section 5 summarises the ex-post and predictive performance of asset pricing models on listed infrastructure/PPP returns. Finally, Section 6 provides concluding remarks and the implications that these findings have on investors.





2. Related Literature

Whilst the OECD (2007) and Infrastructure Australia (2013) estimate an infrastructure deficit around the world and in Australia, there is a paucity of research on the performance of these types of investments. Our understanding of infrastructure and PPPs is limited by the scarcity of empirical studies that analyse the behaviour of these types of investments. Early studies by Beeferman (2008) suggest that the analysis of infrastructure is difficult due to the large scale idiosyncratic nature of individual projects. Despite the generally large size of infrastructure transactions, Croce (2011) states that these investments are desirable to pension and superannuation funds because of the long-term nature of these income generating assets which can offset their long-term pension liabilities. This issue becomes ever more important in a modern world where pension funds are exposed to longevity risk.

Others researchers such as Inderst (2009) suggest that institutions are interested in infrastructure investments in order to develop new sources of returns and portfolio diversification. For instance, Finkenzeller, Dechant and Schafers (2010) show that there are sufficient differences in the portfolio characteristics between infrastructure and real estate. Some of the early empirical infrastructure studies provide us with initial insights based on the knowledge available. Newell and Peng (2008) estimate a

0.70 correlation between U.S. stocks and listed infrastructure. Newell, Peng and De Francesco (2011) also report a strong and positive 0.48 correlation between Australian stocks and unlisted infrastructure. Other studies such as Newell and Peng (2007) reveal that various segments of Australian stocks classified as ‘infrastructure’ may indeed exhibit different return and risk profiles, thereby making it more difficult to determine the systematic return and risk of these types of investments.

In the context of asset pricing, Bird, Liem and Thorp (2012) is one of the first studies to examine the behaviour of infrastructure. Bird et. al., (2012) reveal that infrastructure indices exhibit excess returns with low levels of systematic risks.

Rothballer and Kaserer (2012) argue that the reason for the low systematic risk is due to the lower levels of market competition in infrastructure based industries, due to the high levels of fixed capital investment required. Others including Newbery (2002) and Finkenzeller et. al., (2010) argue that many infrastructure investments operate in oligopolistic and nearly monopolistic markets and these market structures may explain the low systematic risks identified in infrastructure returns.

Traditional methods of assessing the predictability of asset pricing models by Ferson and Harvey (1991), Ferson and Korajczyk (1995) and Ghysels (1998) are based on the comparison of a model’s fitted value versus the expected returns based on a longterm mean or a conditional estimate of the mean return. Furthermore, the literature finds it difficult to compare the predictive ability of two or more competing asset pricing models. To resolve these issues, the recent work of Giacomini and White (2006) has developed a two-step procedure to directly evaluate and compare the predictive performance of competing forecasting models in a unified framework. The application of the predictive performance of asset pricing models by Lewellen and Nagel (2006), Simin (2008) and Welch and Goyal (2008) in the U.S. setting demonstrate that U.S. asset pricing models exhibit poor predictive performance.

Furthermore, Simin (2008) employs the Giacomini and White (2006) methodology and finds that the variance of asset pricing model forecast errors are so large that they cannot outperform the predictive abilities of a constant benchmark return. In this study, we employ the same methodology to evaluate the predictive performance of asset pricing models on Australian Securities Exchange (ASX) listed infrastructure and PPP returns. Given the low-risk nature of infrastructure, we expect the forecastability of asset pricing models to be better suited to these investments.

In the Australian literature, Durack, Durand and Maller (2004) and Nguyen, Faff and Gharghori (2007) show that information variables provide little or no additional power in explaining the variation of Australian equity returns.2 In a thorough test of Australian conditional information variables, Whittaker (2013) evaluates conditioning information including inflation, industrial production, dividend yield, short-term interest rate, term premium, the short-term term premium, the January effect and finds that conditional versions of asset pricing models produce higher mean squared errors than unconditional versions for one month step ahead predictions.3 Consistent with the U.S. literature, it is clear that Australian conditional asset pricing models do not improve the predictive performance of their unconditional counterparts. In our study, we examine the performance of unconditional models on infrastructure/PPP returns.

3. Data

The analysis in this study is based on 16 years of monthly return data from January 1997 to December 2012. The focus of this study is on ASX publicly listed infrastructure and PPP firms whose individual performances are summarised into a number of broad based proxies. The first proxy is the MSCI Australia Infrastructure Index which reflects the performance of ASX firms related to infrastructure assets.

Durack et. al., (2004) employs the U.S. government Term premium and Australian average weekly earnings as information variables in Australian conditional asset pricing models. Nguyen et. al., (2007) use the Australian term spread, default spread as their information variables.

Whittaker (2013) finds that Australian inflation and the 90 day bank bill rate are the most important conditioning information variables which report statistical significance at the 10% level over the sample period from 1991 through 2010.

The MSCI Australia Infrastructure Index is a market capitalisation-weighted index of companies from the telecommunications services, utilities, energy, transportation and social infrastructure sectors. The second proxy for infrastructure used in this study is

–  –  –

Alinta Limited (AAN) was delisted on 7 September 2007 after being acquired by the Singapore Power/Babcock and Brown consortium. Australian Gas Light Company (AGL) was delisted on 26 October 2006 following the merger of Alinta Limited (AAN) and The Australian Gas Light Company’s (AGL) infrastructure businesses. Alinta Infrastructure Holdings (AIH) was delisted on 27 February 2007 when Alinta Limited (AAN) completed the full acquisition of the firm. Alinta Gas (ALN) was renamed to Alinta Limited (AAN) on 14 May 2003. Babcock & Brown Power was renamed Alinta Energy Group (AGK) on 4 January 2010. Babcock & Brown Wind Partners Group (BBW) was renamed to Infigen Energy (IFN) on 4 May 2009. China Construction Holdings Ltd (CIH) was delisted on 27 March 2009. Gasnet Australia Group (GAS) delisted on 17 November 2006 as it was acquired by Australian Pipeline Trust and is now renamed Aust. Pipeline Ltd (APA). Infrastructure Trust of Australia Group (IFT) was renamed Macquarie Infrastructure Group (MIG) from 17 August 1999 and then was renamed again to Intoll Group on 5 th February 2010. Prime Infrastructure Group (PIH) was renamed to Babcock & Brown Infrastructure Ltd (BBI) on 5 th July 2005 and then renamed itself back to PIH on 7th December 2009. United Energy (UEL) was acquired by Alinta Ltd (AAN) on 28 July 2003.

Horizon Roads completed the 100% acquisition of ConnectEast Group (CEU) on 26 October 2011.

Transurban Group (TLC) completed a takeover of Hills Motorway Group (HLY) in April 2005. The Rivercity Motorway Group (RCY) appointed voluntary administrators on 25 February 2011.

the S&P/ASX 200 Utilities Index which reflects the performance of companies who operate in the production and/or distribution of electricity, water utilities or gas. The third infrastructure proxy employed in this study is a market value-weighted index of all 37 firms combined from the MSCI Infrastructure, the S&P/ASX Utilities Index and Sirca database with the industry classification of ‘infrastructure’. The fourth and final proxy is a custom portfolio of the four ASX publicly listed PPPs, namely, BrisConnections, ConnectEast, Rivercity Motorways and Transurban.

Table 1 reports the full list of ASX listed companies which are the constituents of the indices employed in this study. Panel A reports the constituents of the S&P Utilities Index and MSIC Infrastructure index and all other firms recorded in the Sirca database with the ‘infrastructure’ classification. Panel B reports the four ASX listed PPP firms in the sample period.



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