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Out-of-sample stock return predictability in Australia

Author

Listed:
  • Yiwen (Paul) Dou
  • David R. Gallagher

    (Macquarie Graduate School of Management, Sydney, Australia
    Capital Markets CRC Limited, Sydney, Australia)

  • David Schneider

    (UniSuper Management Limited, Melbourne, Australia)

  • Terry S. Walter

    (School of Finance, University of Technology, Sydney, Australia)

Abstract

We provide one of the first comprehensive studies on out-of-sample stock returns predictability in Australia. While most of the empirically well-known predictive variables fail to generate out-of-sample predictability, we document a significant out-of-sample prediction in forecasting ahead one-year and, to a lesser extent, one-quarter future excess returns, using a combination forecast of variables. We also find improved asset allocation using the combination forecast of these predictors. The combining methods are useful in predicting sector premia. Specifically, a sector rotation strategy relying on the combining methods outperforms the market by 3.27% per annum on a risk-adjusted basis.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:ausman:v:37:y:2012:i:3:p:461-479
    DOI: 10.1177/0312896211428493
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    Cited by:

    1. Kam Fong Chan & John G. Powell & Jing Shi & Tom Smith, 2018. "Dividend persistence and dividend behaviour," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(1), pages 127-147, March.
    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. David R. Gallagher & Katja Ignatieva & James McCulloch & Henk Berkman, 2015. "Industry concentration, excess returns and innovation in Australia," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 55(2), pages 443-466, June.
    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. repec:wyi:journl:002192 is not listed on IDEAS
    6. 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.
    7. 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.
    8. 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.
    9. Jurdi, Doureige J., 2022. "Predicting the Australian equity risk premium," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    10. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2016. "Emerging trends in Asia-Pacific finance research: A review of recent influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 66-76.
    11. 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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