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Does volume help in predicting stock returns? An analysis of the Australian market

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  • Bissoondoyal-Bheenick, Emawtee
  • Brooks, Robert D.

Abstract

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.

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  • Bissoondoyal-Bheenick, Emawtee & Brooks, Robert D., 2010. "Does volume help in predicting stock returns? An analysis of the Australian market," Research in International Business and Finance, Elsevier, vol. 24(2), pages 146-157, June.
    • Handle: RePEc:eee:riibaf:v:24:y:2010:i:2:p:146-157
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    1. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(4), pages 905-939.
    2. McMillan, David G., 2007. "Non-linear forecasting of stock returns: Does volume help?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 115-126.
    3. Conrad, Jennifer S & Hameed, Allaudeen & Niden, Cathy, 1994. "Volume and Autocovariances in Short-Horizon Individual Security Returns," Journal of Finance, American Finance Association, vol. 49(4), pages 1305-1329, September.
    4. Chordia, Tarun & Subrahmanyam, Avanidhar & Anshuman, V. Ravi, 2001. "Trading activity and expected stock returns," Journal of Financial Economics, Elsevier, vol. 59(1), pages 3-32, January.
    5. Lesmond, David A., 2005. "Liquidity of emerging markets," Journal of Financial Economics, Elsevier, vol. 77(2), pages 411-452, August.
    6. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    7. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2001. "Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices," Journal of Financial Economics, Elsevier, vol. 61(3), pages 345-381, September.
    8. Robert Brooks & Robert Faff & Tim Fry & Emma Newton, 2004. "Censoring and its impact on multivariate testing of the Capital Asset Pricing Model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(6), pages 413-420.
    9. Hutson, Elaine & Kearney, Colm & Lynch, Margaret, 2008. "Volume and skewness in international equity markets," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1255-1268, July.
    10. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    11. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    12. Reinganum, Marc R, 1982. "A Direct Test of Roll's Conjecture on the Firm Size Effect," Journal of Finance, American Finance Association, vol. 37(1), pages 27-35, March.
    13. Wang, Changyun & Chin, Shengtyng, 2004. "Profitability of return and volume-based investment strategies in China's stock market," Pacific-Basin Finance Journal, Elsevier, vol. 12(5), pages 541-564, November.
    14. Brooks, Robert D. & Faff, Robert W. & Fry, Tim R. L., 2001. "GARCH modelling of individual stock data: the impact of censoring, firm size and trading volume," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 11(2), pages 215-222, June.
    15. Hueng, C. James & McDonald, James B., 2005. "Forecasting asymmetries in aggregate stock market returns: Evidence from conditional skewness," Journal of Empirical Finance, Elsevier, vol. 12(5), pages 666-685, December.
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    More about this item

    Keywords

    G12 G14 G15 Volume Stock market returns Binary probit;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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