IDEAS home Printed from https://ideas.repec.org/p/uts/rpaper/38.html
   My bibliography  Save this paper

Examining Intraday Returns with Buy/Sell Information

Author

Listed:
  • 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

    Download full text from publisher

    File URL: http://www.qfrc.uts.edu.au/research/research_papers/rp38.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. 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.
    6. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July.
    7. 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.
    8. 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.
    9. 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.
    10. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shinn-Juh Lin & Jian Yang, 2003. "Examining intraday returns with buy/sell information," Applied Financial Economics, Taylor & Francis Journals, vol. 13(6), pages 447-461.
    2. Chung-Ming Kuan, 2013. "Markov switching model (in Russian)," Quantile, Quantile, issue 11, pages 13-40, December.
    3. Urom, Christian & Onwuka, Kevin O. & Uma, Kalu E. & Yuni, Denis N., 2020. "Regime dependent effects and cyclical volatility spillover between crude oil price movements and stock returns," International Economics, Elsevier, vol. 161(C), pages 10-29.
    4. Ming-Yuan Leon Li & Chun-Nan Chen, 2010. "Examining the interrelation dynamics between option and stock markets using the Markov-switching vector error correction model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(7), pages 1173-1191.
    5. Chen, Shyh-Wei, 2006. "Simultaneously modeling the volatility of the growth rate of real GDP and determining business cycle turning points: Evidence from the U.S., Canada and the UK," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 71(2), pages 87-102.
    6. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    7. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    8. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    9. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    10. Maheu, John M. & McCurdy, Thomas H., 2000. "Volatility dynamics under duration-dependent mixing," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 345-372, November.
    11. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
    13. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    14. Oscar V. De la Torre-Torres & Evaristo Galeana-Figueroa & José Álvarez-García, 2021. "A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance," Mathematics, MDPI, vol. 9(9), pages 1-28, May.
    15. Yuan, Chunming, 2011. "The exchange rate and macroeconomic determinants: Time-varying transitional dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 22(2), pages 197-220, August.
    16. Fong, Wai Mun & See, Kim Hock, 2002. "A Markov switching model of the conditional volatility of crude oil futures prices," Energy Economics, Elsevier, vol. 24(1), pages 71-95, January.
    17. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Fear of disruption: a model of Markov-switching regimes for the Brazilian country risk conditional volatility," Econometrics 0509005, University Library of Munich, Germany.
    18. Rossi, Alessandro & Gallo, Giampiero M., 2006. "Volatility estimation via hidden Markov models," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 203-230, March.
    19. Roche, Bruno B. & Flôres Junior, Renato Galvão, 1999. "Volatility modelling in the forex market: an empirical evaluation," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 361, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    20. Shyh-Wei Chen & Chung-Hua Shen, 2007. "Evidence of the duration-dependence from the stock markets in the Pacific Rim economies," Applied Economics, Taylor & Francis Journals, vol. 39(11), pages 1461-1474.

    More about this item

    Keywords

    qualitative threshold model; buy/sell information;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:uts:rpaper:38. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Duncan Ford (email available below). General contact details of provider: https://edirc.repec.org/data/qfutsau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.