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Forecasting extreme performance: The experience with Australian equities

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Abstract

Over any 12-month period, there is an enormous difference between the returns realised from investing in the best- and worst-performing stocks. We investigate the characteristics of these stocks and find that they share several features: extreme performers tend to be small companies that have volatile share prices and spend significant amounts of money on research and development. Accounting variables tend to be useful in separating out the best and worst performers; the latter also tend to be smaller companies which have lower share prices.

Suggested Citation

  • Abidin Kusno & Ron Bird & Danny Yeung, 2013. "Forecasting extreme performance: The experience with Australian equities," Published Paper Series 2013-5, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
  • Handle: RePEc:uts:ppaper:2013-5
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    References listed on IDEAS

    as
    1. Ron Bird & Lorenzo Casavecchia, 2007. "Sentiment and Financial Health Indicators for Value and Growth Stocks: The European Experience," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 769-793.
    2. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    3. Wayne E. Ferson & Sergei Sarkissian & Timothy T. Simin, 2003. "Spurious Regressions in Financial Economics?," Journal of Finance, American Finance Association, vol. 58(4), pages 1393-1413, August.
    4. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    5. Wayne E. Ferson & Sergei Sarkissian & Timothy T. Simin, 2003. "Spurious Regressions in Financial Economics?," Journal of Finance, American Finance Association, vol. 58(4), pages 1393-1414, August.
    6. D Glickman & AG DiRienzo & R Ochman, 2001. "Extreme stock returns," Journal of Asset Management, Palgrave Macmillan, vol. 2(2), pages 107-127, September.
    7. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    8. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    9. Palepu, Krishna G., 1986. "Predicting takeover targets : A methodological and empirical analysis," Journal of Accounting and Economics, Elsevier, vol. 8(1), pages 3-35, March.
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    Cited by:

    1. Kamphol Panyagometh, 2017. "Implementation of Reinganum's Investment Strategy in Long Term Equity Fund in the Stock Exchange of Thailand," International Journal of Economics and Financial Issues, Econjournals, vol. 7(2), pages 492-499.

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