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Joint Dynamics of Prices and Trading Volume on the Polish Stock Market

Author

Listed:
  • Henryk Gurgul

    (University of Science and Technology, Poland)

  • Pawel Majdosz

    (School of Economics and Computer Science, Poland)

  • Roland Mestel

    (University of Graz, Austria)

Abstract

This paper concerns the relationship between stock returns and trading volume. We use daily stock data of the Polish companies included in the WIG20 segment (the twenty most liquid companies quoted on the primary market of the Warsaw Stock Exchange). The sample covers the period from January 1995 to April 2005. We find that there is no empirical support for a relationship between stock return levels and trading volume. On the other hand, our calculations provide evidence for a significant contemporaneous interaction between return volatility and trading volume. Our investigations reveal empirical evidence for the importance of volume data as an indicator of the flow of information into the market. These results are in line with suggestions from the Mixture of Distribution Hypothesis. By means of the Granger causality test, we establish causality from both stock returns and return volatility to trading volume. Our results indicate that series on trading activities have little additional explanatory power for subsequent price changes over that already contained in the price series.

Suggested Citation

  • Henryk Gurgul & Pawel Majdosz & Roland Mestel, 2005. "Joint Dynamics of Prices and Trading Volume on the Polish Stock Market," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 3(2), pages 139-156.
    • Handle: RePEc:mgt:youmgt:v:3:y:2005:i:2:p:139-156
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    Citations

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    Cited by:

    1. Rodrigo Aranda & Patricio Jaramillo, 2008. "Nonlinear Dynamic in the Chilean Stock Market: Evidence from Returns and Trading Volume," Working Papers Central Bank of Chile 463, Central Bank of Chile.
    2. Katarzyna Bien-Barkowska, 2012. ""Does it take volume to move fx rates?" Evidence from quantile regressions," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 12, pages 35-52.
    3. Sarika Mahajan & Balwinder Singh, 2008. "An Empirical Analysis of Stock Price-Volume Relationship in Indian Stock Market," Vision, , vol. 12(3), pages 1-13, July.
    4. Muzhao Jin & Fearghal Kearney & Youwei Li & Yung Chiang Yang, 2023. "Order book price impact in the Chinese soybean futures market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 606-625, January.
    5. Go, You-How & Lau, Wee-Yeap, 2020. "The impact of global financial crisis on informational efficiency: Evidence from price-volume relation in crude palm oil futures market," Journal of Commodity Markets, Elsevier, vol. 17(C).
    6. Sarika Mahajan & Balwinder Singh, 2013. "Return, Volume and Volatility Relationship in Indian Stock Market: Pre and Post Rolling Settlement Analysis," Global Business Review, International Management Institute, vol. 14(3), pages 413-428, September.
    7. Rodrigo F. Aranda L. & Patricio Jaramillo G., 2010. "Non-linear Dynamics in the Chilean Stock Market: Evidence on Traded Volumes and Returns," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(3), pages 67-94, December.
    8. Ling-Yun He & Sheng Yang & Wen-Si Xie & Zhi-Hong Han, 2014. "Contemporaneous and Asymmetric Properties in the Price-Volume Relationships in China's Agricultural Futures Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(S1), pages 148-166.
    9. Gurleen Sahota & Balwinder Singh, 2016. "The Empirical Investigation of Causal Relationship between Intraday Return and Volume in Indian Stock Market," Vision, , vol. 20(3), pages 199-210, September.

    More about this item

    Keywords

    abnormal stock returns; return volatility; abnormal trading volume; GARCH-cum-volume; causal relations;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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