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Examining drivers of trading volume in European markets

Author

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  • Bogdan Batrinca
  • Christian W. Hesse
  • Philip C. Treleaven

Abstract

This study presents an in†depth exploration of market dynamics and analyses potential drivers of trading volume. The study considers established facts from the literature, such as calendar anomalies, the correlation between volume and price change, and this relation's asymmetry, while proposing a variety of time series models. The results identified some key volume predictors, such as the lagged time series volume data and historical price indicators (e.g. intraday range, intraday return, and overnight return). Moreover, the study provides empirical evidence for the price–volume relation asymmetry, finding an overall price asymmetry in over 70% of the analysed stocks, which is observed in the form of a moderate overnight asymmetry and a more salient intraday asymmetry. We conclude that volatility features, more recent data, and day†of†the†week features, with a notable negative effect on Mondays and Fridays, improve the volume prediction model.

Suggested Citation

  • Bogdan Batrinca & Christian W. Hesse & Philip C. Treleaven, 2018. "Examining drivers of trading volume in European markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(2), pages 134-154, April.
  • Handle: RePEc:wly:ijfiec:v:23:y:2018:i:2:p:134-154
    DOI: 10.1002/ijfe.1608
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    Cited by:

    1. Richards, Daniel W. & Willows, Gizelle D., 2019. "Monday mornings: Individual investor trading on days of the week and times within a day," Journal of Behavioral and Experimental Finance, Elsevier, vol. 22(C), pages 105-115.
    2. Javier Rojo‐Suárez & Ana Belén Alonso‐Conde & Ricardo Ferrero‐Pozo, 2022. "Liquidity, time‐varying betas and anomalies: Is the high trading activity enhancing the validity of the CAPM in the UK equity market?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 45-60, January.
    3. Xu, Chao & Zhao, Xiaojun & Wang, Yanwen, 2022. "Causal decomposition on multiple time scales: Evidence from stock price-volume time series," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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