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The dynamic relationship between stock returns and trading volume revisited: A MODWT-VAR approach

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  • Gupta, Suman
  • Das, Debojyoti
  • Hasim, Haslifah
  • Tiwari, Aviral Kumar

Abstract

This paper revisits the relationship between market returns and trading volume in a time-frequency domain using a wavelet-based vector autoregression approach. Over 15 years of almost concurrent data from two major emerging stock markets – China and India – are considered for analysis. The relationship is found to vary across different time horizons. In addition, we report that both Chinese and Indian markets depict the artifact of efficiency in the short to medium run. However, markets become inefficient in the longest time horizon studied.

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  • Gupta, Suman & Das, Debojyoti & Hasim, Haslifah & Tiwari, Aviral Kumar, 2018. "The dynamic relationship between stock returns and trading volume revisited: A MODWT-VAR approach," Finance Research Letters, Elsevier, vol. 27(C), pages 91-98.
    • Handle: RePEc:eee:finlet:v:27:y:2018:i:c:p:91-98
    DOI: 10.1016/j.frl.2018.02.018
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    Cited by:

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    8. Ma, Xinxin & Zong, Xiangyu & Chen, Ximing, 2022. "Economic fitness and economy growth potentiality: Evidence from BRICS and OECD countries," Finance Research Letters, Elsevier, vol. 50(C).
    9. Oktay Ozkan, 2020. "Time-varying return predictability and adaptive markets hypothesis: Evidence on MIST countries from a novel wild bootstrap likelihood ratio approach," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 34(2), pages 101-113.
    10. Ouyang, Zisheng & Zhou, Xuewei, 2023. "Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions," Research in International Business and Finance, Elsevier, vol. 65(C).
    11. Liang, Ruibin & Cheng, Sheng & Cao, Yan & Li, Xinran, 2024. "Multi-scale impacts of oil shocks on travel and leisure stocks: A MODWT-Bayesian TVP model with shrinkage approach," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    12. Bales, Stephan, 2022. "Sovereign and bank dependence in the eurozone: A multi-scale approach using wavelet-network analysis," International Review of Financial Analysis, Elsevier, vol. 83(C).
    13. Suman Gupta & Vinay Goyal & Vinay Kumar Kalakbandi & Sankarshan Basu, 2018. "Overconfidence, trading volume and liquidity effect in Asia’s Giants: evidence from pre-, during- and post-global recession," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(3), pages 235-257, September.
    14. 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).
    15. Rodriguez, E. & Alvarez-Ramirez, J., 2021. "Time-varying cross-correlation between trading volume and returns in US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    16. Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.
    17. Sheng Cheng & Wei Liu & Qisheng Jiang & Yan Cao, 2023. "Multi–Scale Risk Connectedness Between Economic Policy Uncertainty of China and Global Oil Prices in Time–Frequency Domains," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1593-1616, April.
    18. Živkov, Dejan & Balaban, Suzana & Simić, Milica, 2024. "Hedging gas in a multi-frequency semiparametric CVaR portfolio," Research in International Business and Finance, Elsevier, vol. 67(PA).

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    More about this item

    Keywords

    Wavelet; Trading volume; Market returns; Time–frequency domain;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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