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Economic significance of predictability in Australian equities

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  • Philip Gray

Abstract

This paper examines evidence of predictability in Australian equities using both statistical and economic metrics of significance. A probit‐based predictive model is used to forecast the probability that the 1 month ahead excess market return will be positive. Funds under management are then switched between equities and fixed income on the basis of this forecast. Although the statistical evidence of the model's predictive ability is mixed, the results suggest convincing evidence of an economically significant degree of return predictability. A $A1 investment in the switching strategy (market) in January 1980 grows to over $A55 ($A39) by June 2007. Although the economic significance of the switching strategy remains even in the presence of high transaction costs, robustness checks suggest that the seemingly impressive full‐sample results might be sample specific. The apparent superiority of the portfolio‐switching strategy can be traced to a handful of observations early in the study during which the predictive model provides a timely signal to exit equities. There is little evidence that the predictive model has forecasting ability across the entire sample. As such, this paper serves both to illustrate how alternate metrics of return predictability can lead to divergent conclusions, and to emphasize the importance of subjecting apparent findings of predictability to robustness checks.

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  • Philip Gray, 2008. "Economic significance of predictability in Australian equities," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 48(5), pages 783-805, December.
  • Handle: RePEc:bla:acctfi:v:48:y:2008:i:5:p:783-805
    DOI: 10.1111/j.1467-629X.2008.00264.x
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    Cited by:

    1. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
    2. Zhanglong Wang & Kent Wang & Zheyao Pan, 2015. "Conditional equity risk premia and realized variance jump risk," Australian Journal of Management, Australian School of Business, vol. 40(2), pages 295-317, May.
    3. Kent Wang & Yuqiang Guo, 2014. "Predictability of time-varying jump premiums: Evidence based on calibration," Australian Journal of Management, Australian School of Business, vol. 39(3), pages 369-394, August.
    4. Dragon Yongjun Tang, 2014. "Potential losses from incorporating return predictability into portfolio allocation," Australian Journal of Management, Australian School of Business, vol. 39(1), pages 35-45, February.
    5. Jurdi, Doureige J., 2022. "Predicting the Australian equity risk premium," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    6. Yiwen (Paul) Dou & David R. Gallagher & David Schneider & Terry S. Walter, 2012. "Out-of-sample stock return predictability in Australia," Australian Journal of Management, Australian School of Business, vol. 37(3), pages 461-479, December.
    7. repec:wyi:journl:002192 is not listed on IDEAS
    8. Afsaneh Bahrami & Abul Shamsuddin & Katherine Uylangco, 2018. "Out‐of‐sample stock return predictability in emerging markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(3), pages 727-750, September.
    9. Jing Tian & Qing Zhou, 2018. "Improving equity premium forecasts by incorporating structural break uncertainty," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 619-656, November.

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