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

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The overall findings from Tables 6 and 7 show that the CAPM with no intercept is the best predictor of future returns when employing conventional asset pricing The detailed output of the Giacomini and White (2006) tests from 1997-2012 which compare every asset pricing model with its alternatives are available upon request.

Whilst the ‘CAPM with no intercept’ model (CAPMNI) is best frameworks.

predictor of one month returns, the empirical evidence presented in this study suggests that fixed excess return models outperform both CAPM and Fama-French models at both 1 year and 2 year time horizons. This evidence is consistent with the previous work by Simin (2008) in the U.S. setting and Whittaker (2013) in the Australian setting.

Figure 1 Cumulative Square Prediction Error of Two Best Forecasting Models (1 Month) Cumulative square prediction error of the best forecasting model minus the cumulative square prediction error of the second best (ie. alternative) forecasting model.

0.200 0.150 0.100 0.050 0.000 1/01/2002 1/08/2002 1/03/2003 1/10/2003 1/05/2004 1/12/2004 1/07/2005 1/02/2006 1/09/2006 1/04/2007 1/11/2007 1/06/2008 1/01/2009 1/08/2009 1/03/2010 1/10/2010 1/05/2011 1/12/2011 1/07/2012

-0.050 Figure 2 Cumulative Square Prediction Error of Two Best Forecasting Models (1 Year) Cumulative square prediction error of the best forecasting model minus the cumulative square prediction error of the second best (ie. alternative) forecasting model.

0.005 0.000 1/07/2005 1/07/2008 1/01/2002 1/07/2002 1/01/2003 1/07/2003 1/01/2004 1/07/2004 1/01/2005 1/01/2006 1/07/2006 1/01/2007 1/07/2007 1/01/2008 1/01/2009 1/07/2009 1/01/2010 1/07/2010

-0.005

-0.010

-0.015

-0.020

-0.025

-0.030

-0.035

-0.040 Figure 3 Cumulative Square Prediction Error of Two Best Forecasting Models (2 Years) Cumulative square prediction error of the best forecasting model minus the cumulative square prediction error of the second best (ie. alternative) forecasting model.

0.010 0.005 0.000 1/07/2004 1/01/2002 1/07/2002 1/01/2003 1/07/2003 1/01/2004 1/01/2005 1/07/2005 1/01/2006 1/07/2006 1/01/2007 1/07/2007 1/01/2008 1/07/2008 1/01/2009 1/07/2009 1/01/2010 1/07/2010

-0.005

-0.010

-0.015

-0.020

-0.025

-0.030 IV. Predictive Performance and the 2008 Global Financial Crisis (GFC) As a final check of robustness, we follow Rapach, Strauss and Zhou (2010) and we illustrate the cumulative prediction errors of the two best forecasting models. In our case, we combine the prediction errors of all indices and we compare the difference in these errors between the best forecasting model against the second best forecasting model for 1, 12 and 24 month time horizons, respectively. The time series plots in Figures 1 to 3 are important because they illustrate the consistency of the forecasting performance of the two best models for each time horizon.

Figure 1 compares the prediction errors of the two best one month forecasting models, namely, the Australian Fama and French (1993) three-factor asset pricing model with no intercept term (FFNI) versus the single-factor Capital Asset Pricing Model with no intercept term (CAPMNI). Positive values in Figure 1 denote that the FFNI is a better predictor than CAPMNI while negative values illustrates the opposite. Panel A of Table 7 shows that both FFNI and CAPMNI forecasting models report the same level of predictive performance when forecasting 1 month returns, however, on closer inspection, Figure 1 reveals that the FFNI model reports lower cumulative prediction errors than the CAPMNI model. Put simply, an investor is better served in employing the FFNI model when forecasting 1 month returns.

Figures 2 and 3 illustrate the analysis of the two best forecasting models for 1 and 2 year time horizons, respectively, which are the fixed excess return models of 9% versus 10%. Negative values in Figures 2 and 3 indicate that the 10% excess return model exhibits lower prediction errors than the 9% excess return model while positive values indicates the opposite. From the early 2000s to 2006, the 10% fixed excess return model was a better predictor of future returns in comparison to the 9% fixed excess return model. However, as the market stalled preceding the GFC, we observe that the 9% excess return model began to outperform the 10% excess return model.

Overall, the differences in the cumulative prediction errors (see the y-axis) are negligible between these two forecasting models for both time horizons.

Overall, the analysis in Figures 1 to 3 shows the importance of visualising the dynamics and the consistency of competing forecasting models and how the consistency changes through time. These illustrations convey the message that even the best predictive model in these studies exhibit changes in consistency over time if investors were to employ them in applied settings over the short, medium and longterm.

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6. Concluding Remarks

Our findings suggest that the predictive performance of the CAPM with no intercept is the best performing asset pricing model, however, it is important to note that simple, fixed excess return models generally tend to outperform the CAPM and Fama-French models. These findings in the Australian setting have important implications for practitioners. An interesting finding from the analysis (consistent with Simin, 2008) is that the predictive performance of the constant return models tends to gravitate towards their long term unconditional historical mean returns. The findings presented in this study (and those of Simin, 2008) suggest that employing the long-term historical mean return is a reasonable starting point for superannuation funds seeking to understand the long-term expected returns of infrastructure. In short, the evidence to date supports employing a simple historical mean return as this seems to outperform conventional asset pricing models.





Our findings provide researchers with a number of avenues for future research. First, our study is limited to the 16 years of empirical data available on Australian infrastructure returns from 1997 through 2012. In comparison, U.S. studies that have evaluated the predictive performance of asset pricing models employ much longer data samples. For instance, Lewellen and Nagel (2006), Simin (2008) and Welch and Goyal (2008) analyse the 1964-2001, 1931-2004, 1926-2005 data sample periods, respectively. A similar type of research on longer term U.S. infrastructure data may be fruitful in understanding infrastructure returns over the long-run.

A second avenue for further research is the efficacy of the 60 month rolling window employed in this analysis. It is standard practice in the finance literature to employ a rolling 60 month window to capture the inputs for the asset pricing model, however, this itself must be an issue of contention. Researchers may need to experiment with other time frames to evaluate the efficacy of these methods in the finance literature.

We leave these challenges for future research endeavours.

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Ferson, W. and Korajczyk, R., 1995, Do arbitrage pricing models explain the predictability of stock returns, Journal of Business 60, 309-349.

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Kamps, C., 2004, The dynamic effects of public capital: VAR evidence for 22 OECD countries, International Tax and Public Finance 12(4), 533-558.

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