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Revisiting the empirical linkages between stock returns and trading volume

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  • Chen, Shiu-Sheng

Abstract

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.

Suggested Citation

  • Chen, Shiu-Sheng, 2012. "Revisiting the empirical linkages between stock returns and trading volume," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1781-1788.
  • Handle: RePEc:eee:jbfina:v:36:y:2012:i:6:p:1781-1788
    DOI: 10.1016/j.jbankfin.2012.02.003
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    Cited by:

    1. Tangmongkollert, K. & Suwanna, S., 2016. "Asset price and trade volume relation in artificial market impacted by value investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 126-133.
    2. Liu, Xinghua & Liu, Xin & Liang, Xiaobei, 2015. "Information-driven trade and price–volume relationship in artificial stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 73-80.
    3. Gebka, Bartosz & Wohar, Mark E., 2013. "Causality between trading volume and returns: Evidence from quantile regressions," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 144-159.
    4. Ralf Brüggemann & Markus Glaser & Stefan Schaarschmidt & Sandra Stankiewicz, 2014. "The Stock Return - Trading Volume Relationship in European Countries: Evidence from Asymmetric Impulse Responses," Working Paper Series of the Department of Economics, University of Konstanz 2014-24, Department of Economics, University of Konstanz.
    5. Elina Pradkhan, 2016. "Information Content of Trading Activity in Precious Metals Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(5), pages 421-456, May.
    6. Manahov, Viktor & Hudson, Robert & Linsley, Philip, 2014. "New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 299-316.
    7. Baller, Stefanie & Entrop, Oliver & McKenzie, Michael & Wilkens, Marco, 2016. "Market makers’ optimal price-setting policy for exchange-traded certificates," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 206-226.
    8. Tzu-Kuang Hsu & Chin-Chang Tsai, 2017. "Explore the Impact of the Trading Value, The Oil Price and Quantitative Easing Policy on the Taiwan and Korea Stock Market Return with Quantile Regression," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 7(1), pages 15-26, January.
    9. Bertone, Stephen & Paeglis, Imants & Ravi, Rahul, 2015. "(How) has the market become more efficient?," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 72-86.
    10. Gong, Fuzhou & Liu, Hong, 2016. "Asymmetric information, heterogeneous prior beliefs, and public information," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 100-120.
    11. Cathy W.S. Chen & Mike K.P. So & Thomas C. Chiang, 2016. "Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach," The Japanese Economic Review, Japanese Economic Association, vol. 67(1), pages 96-124, March.

    More about this item

    Keywords

    Stock returns; Trading volume; Stock market fluctuations;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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