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Examining Intraday Returns with Buy/Sell Information

Author

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  • Shinn-Juh Lin

    (Department of International Business, National Chengchi University)

  • Jian Yang

Abstract

This paper examines high frequency stock returns with buy/sell signals. It demonstrates how such trading information could be utilized in a qualitative threshold framework to explain and predict the asymmetric behaviour of intrady stock returns. The study discovers that the buyer-dominating regime is consistently associated with negative returns, while the seller-dominating regime is consistently associated with positive returns. This is consistent with our suggestion of using the sign of the net buy/sell trading volume as the threshold indicator. Furthermore, the model renders better predicting power than that produced by a pure generalized autoregressive conditional heteroskedasticity model. Most interestingly, these reults are quite robust across all twelve actively traded stocks on the Australian Stock Exchange that we have examined, and hence provide strong support for the potential usefulness of buy/sell signals and the qualitative threshold model in analyzing the dynamics of high frequency financial asset returns.

Suggested Citation

  • Shinn-Juh Lin & Jian Yang, 2000. "Examining Intraday Returns with Buy/Sell Information," Research Paper Series 38, Quantitative Finance Research Centre, University of Technology, Sydney.
  • Handle: RePEc:uts:rpaper:38
    as

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    File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp38.pdf
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    References listed on IDEAS

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    1. Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
    2. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July.
    3. Locke, P R & Sayers, C L, 1993. "Intra-day Futures Price Volatility: Information Effects and Variance Persistence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(1), pages 15-30, Jan.-Marc.
    4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    5. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    6. Durland, J. Michael & McCurdy, Thomas H., 1993. "Duration Dependent Transitions in a Markov Model of U.S. GNP Growth," Queen's Economics Department Working Papers 273295, Queen's University - Department of Economics.
    7. Durland, J Michael & McCurdy, Thomas H, 1994. "Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 279-288, July.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    10. Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
    11. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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