IDEAS home Printed from https://ideas.repec.org/a/sae/metjou/v16y2017i2p122-140.html
   My bibliography  Save this article

Exploring the Causal Relationship Between Stock Returns, Volume, and Turnover across Sectoral Indices in Indian Stock Market

Author

Listed:
  • Parab Narayan
  • Y. V. Reddy

Abstract

The traditional saying “Market Discounts Everything†is applicable to stock returns, trading volume, and turnover as well. The present study is an analytical attempt to examine the causal relationship between stock returns, trading volume, and turnover across 10 sectoral indices of National Stock Exchange (NSE) for the period 2006–2016. To critically examine this relation, the study uses various statistical techniques such as descriptive statistics, correlation analysis, regression analysis, and econometric tests such as Granger causality test and augmented Dickey–Fuller test. The required analyses have been performed using statistical software E-views, SPSS, and Microsoft Excel. The study noticed a weak positive relationship between stock returns and turnover for Nifty Auto Index, Nifty Bank Index, Nifty Financial Services Index, Nifty Media Index, Nifty Metal Index, and Nifty Private Bank Index. The study also found a significant impact of turnover on stock returns in the case of Nifty Auto Index, Nifty Bank Index, Nifty FMCG Index, Nifty Metal Index, and Nifty Pharma Index and a significant impact of volume on stock returns in the case of Nifty Bank Index, Nifty FMCG Index, and Nifty Pharma Index. Augmented Dickey–Fuller test suggests that there exists no unit root in the data ( p

Suggested Citation

  • Parab Narayan & Y. V. Reddy, 2017. "Exploring the Causal Relationship Between Stock Returns, Volume, and Turnover across Sectoral Indices in Indian Stock Market," Metamorphosis: A Journal of Management Research, , vol. 16(2), pages 122-140, December.
  • Handle: RePEc:sae:metjou:v:16:y:2017:i:2:p:122-140
    DOI: 10.1177/0972622517730140
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0972622517730140
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0972622517730140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kumar, Brajesh & Singh, Priyanka & Pandey, Ajay, 2009. "The Dynamic Relationship between Price and Trading Volume:Evidence from Indian Stock Market," IIMA Working Papers WP2009-12-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Ning, Cathy & Wirjanto, Tony S., 2009. "Extreme return-volume dependence in East-Asian stock markets: A copula approach," Finance Research Letters, Elsevier, vol. 6(4), pages 202-209, December.
    3. Thomas C. Chiang & Zhuo Qiao & Wing-Keung Wong, 2010. "New evidence on the relation between return volatility and trading volume," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 502-515.
    4. Hsin-Yi Lin, 2013. "Dynamic Stock Return–Volume Relation: Evidence From Emerging Asian Markets," Bulletin of Economic Research, Wiley Blackwell, vol. 65(2), pages 178-193, April.
    5. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    6. Darrat, Ali F. & Rahman, Shafiqur & Zhong, Maosen, 2003. "Intraday trading volume and return volatility of the DJIA stocks: A note," Journal of Banking & Finance, Elsevier, vol. 27(10), pages 2035-2043, October.
    7. Alexander Eastman & Brian Lucey, 2008. "Skewness and asymmetry in futures returns and volumes," Applied Financial Economics, Taylor & Francis Journals, vol. 18(10), pages 777-800.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Satyaban Sahoo & Sanjay Kumar, 2021. "Existence of Cointegration between the Public and Private Bank Index: Evidence from Indian Capital Market," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(4), pages 152-172, December.

    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. Koubaa, Yosra & Slim, Skander, 2019. "The relationship between trading activity and stock market volatility: Does the volume threshold matter?," Economic Modelling, Elsevier, vol. 82(C), pages 168-184.
    2. Brian Sing Fan Chan & Andy Cheuk Hin Cheng & Alfred Ka Chun Ma, 2018. "Stock Market Volatility and Trading Volume: A Special Case in Hong Kong With Stock Connect Turnover," JRFM, MDPI, vol. 11(4), pages 1-17, October.
    3. Wei Lin & Gloria González‐Rivera, 2019. "Extreme returns and intensity of trading," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1121-1140, November.
    4. Marius Cristian Miloș, 2021. "Impact of MiFID II on the Market Volatility—Analysis on Some Developed and Emerging European Stock Markets," Laws, MDPI, vol. 10(3), pages 1-11, June.
    5. Malay K. Dey & Chaoyan Wang, 2022. "Asymmetric volume volatility causality in dual listing H-shares," Journal of Asset Management, Palgrave Macmillan, vol. 23(5), pages 419-428, September.
    6. Kao, Yu-Sheng & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2020. "The empirical linkages among market returns, return volatility, and trading volume: Evidence from the S&P 500 VIX Futures," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Rilwan Sakariyahu & Mohamed Sherif & Audrey Paterson & Eleni Chatzivgeri, 2021. "Sentiment‐Apt investors and UK sector returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3321-3351, July.
    8. Daouda Lawa tan Toe & Salifou Ouedraogo, 2022. "Dynamic relationship between trading volume, returns and returns volatility: an empirical investigation on the main African’s stock markets," Journal of Asset Management, Palgrave Macmillan, vol. 23(5), pages 429-444, September.
    9. Sibel ?EL?K, 2013. "New Evidence on the Relation between Trading Volume and Volatility," Business and Economic Research, Macrothink Institute, vol. 3(1), pages 176-186, June.
    10. Kao, Yu-Sheng & Zhao, Kai & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2024. "The asymmetric relationships between the Bitcoin futures’ return, volatility, and trading volume," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 524-542.
    11. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    12. Dinh, Minh Thi Hong, 2018. "The relationship between volume imbalance and spread," Research in International Business and Finance, Elsevier, vol. 44(C), pages 76-87.
    13. Do, Hung Xuan & Brooks, Robert & Treepongkaruna, Sirimon & Wu, Eliza, 2014. "How does trading volume affect financial return distributions?," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 190-206.
    14. Abhinava Tripathi, 2021. "The Arrival of Information and Price Adjustment Across Extreme Quantiles: Global Evidence," IIM Kozhikode Society & Management Review, , vol. 10(1), pages 7-19, January.
    15. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    16. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    17. Medovikov, Ivan, 2014. "Can analysts predict rallies better than crashes?," Finance Research Letters, Elsevier, vol. 11(4), pages 319-325.
    18. Hervé, Fabrice & Zouaoui, Mohamed & Belvaux, Bertrand, 2019. "Noise traders and smart money: Evidence from online searches," Economic Modelling, Elsevier, vol. 83(C), pages 141-149.
    19. Manh Cuong Dong & Cathy W. S. Chen & Manabu Asai, 2023. "Bayesian non‐linear quantile effects on modelling realized kernels," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 981-995, January.
    20. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Forecasting the CNY-CNH pricing differential: The role of investor attention," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 232-247.

    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:sae:metjou:v:16:y:2017:i:2:p:122-140. 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: SAGE Publications (email available below). General contact details of provider: .

    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.