IDEAS home Printed from https://ideas.repec.org/a/eee/mulfin/v62y2021ics1042444x21000402.html
   My bibliography  Save this article

Big data analytics, order imbalance and the predictability of stock returns

Author

Listed:
  • Akyildirim, Erdinc
  • Sensoy, Ahmet
  • Gulay, Guzhan
  • Corbet, Shaen
  • Salari, Hajar Novin

Abstract

Financial institutions have adopted big data to a considerable extent to provide better investment decisions. Consequently, high-frequency algorithmic traders use a vast amount of historical data with various statistical models to maximize their trading profits. Until recently, high-frequency algorithmic trading was the domain of institutional traders with access to supercomputers. Nowadays, any investor can potentially make high-frequency trades because of easy access to big data and software to analyze and execute trades. With that in mind, Borsa Istanbul introduced real time big data analytics as a product to its customers. These analytics are derived in real time from order book and trade data and aim to level the playing field between investment firms and retail traders. Using classical benchmark models in the literature, we show that Borsa Istanbul’s order imbalance-based data analytics are useful in predicting both time-series and cross-sectional intraday excess future returns, proving that this product is extremely beneficial to market participants, particularly day traders.

Suggested Citation

  • Akyildirim, Erdinc & Sensoy, Ahmet & Gulay, Guzhan & Corbet, Shaen & Salari, Hajar Novin, 2021. "Big data analytics, order imbalance and the predictability of stock returns," Journal of Multinational Financial Management, Elsevier, vol. 62(C).
  • Handle: RePEc:eee:mulfin:v:62:y:2021:i:c:s1042444x21000402
    DOI: 10.1016/j.mulfin.2021.100717
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1042444X21000402
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.mulfin.2021.100717?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yamamoto, Ryuichi, 2012. "Intraday technical analysis of individual stocks on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3033-3047.
    2. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2014. "The Price Impact of Order Book Events," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 47-88.
    3. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2002. "Breadth of ownership and stock returns," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 171-205.
    4. Narayan, Paresh Kumar & Narayan, Seema & Westerlund, Joakim, 2015. "Do order imbalances predict Chinese stock returns? New evidence from intraday data," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 136-151.
    5. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    6. repec:bla:jfinan:v:44:y:1989:i:4:p:827-48 is not listed on IDEAS
    7. Jonathan J.J.M. Seddon & Wendy L. Currie, 2017. "A model for unpacking big data analytics in high-frequency trading," Post-Print hal-01404316, HAL.
    8. Andrade, Sandro C. & Chang, Charles & Seasholes, Mark S., 2008. "Trading imbalances, predictable reversals, and cross-stock price pressure," Journal of Financial Economics, Elsevier, vol. 88(2), pages 406-423, May.
    9. Chordia, Tarun & Subrahmanyam, Avanidhar, 2004. "Order imbalance and individual stock returns: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 72(3), pages 485-518, June.
    10. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2002. "Order imbalance, liquidity, and market returns," Journal of Financial Economics, Elsevier, vol. 65(1), pages 111-130, July.
    11. Lee, Yi-Tsung & Liu, Yu-Jane & Roll, Richard & Subrahmanyam, Avanidhar, 2004. "Order Imbalances and Market Efficiency: Evidence from the Taiwan Stock Exchange," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(2), pages 327-341, June.
    12. Glantz, Morton & Kissell, Robert, 2013. "Multi-Asset Risk Modeling," Elsevier Monographs, Elsevier, edition 1, number 9780124016903.
    13. Wang, Yichuan & Hajli, Nick, 2017. "Exploring the path to big data analytics success in healthcare," Journal of Business Research, Elsevier, vol. 70(C), pages 287-299.
    14. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    15. Seddon, Jonathan J.J.M. & Currie, Wendy L., 2017. "A model for unpacking big data analytics in high-frequency trading," Journal of Business Research, Elsevier, vol. 70(C), pages 300-307.
    16. Njuguna, Christopher & McSharry, Patrick, 2017. "Constructing spatiotemporal poverty indices from big data," Journal of Business Research, Elsevier, vol. 70(C), pages 318-327.
    17. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    18. Yacine Aït-Sahalia & Mehmet Saglam, 2013. "High Frequency Traders: Taking Advantage of Speed," NBER Working Papers 19531, National Bureau of Economic Research, Inc.
    19. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    20. Viktoria Dalko & Michael H. Wang, 2020. "High-frequency trading: Order-based innovation or manipulation?," Journal of Banking Regulation, Palgrave Macmillan, vol. 21(4), pages 289-298, December.
    21. Zhang, Ting & Gu, Gao-Feng & Zhou, Wei-Xing, 2019. "Order imbalances and market efficiency: New evidence from the Chinese stock market," Emerging Markets Review, Elsevier, vol. 38(C), pages 458-467.
    22. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    23. Cushing, David & Madhavan, Ananth, 2000. "Stock returns and trading at the close," Journal of Financial Markets, Elsevier, vol. 3(1), pages 45-67, February.
    24. Nimmagadda, Shastri L. & Reiners, Torsten & Wood, Lincoln C., 2018. "On big data-guided upstream business research and its knowledge management," Journal of Business Research, Elsevier, vol. 89(C), pages 143-158.
    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. Tang, Mengxuan & Hu, Yang & Corbet, Shaen & Hou, Yang (Greg) & Oxley, Les, 2024. "Fintech, bank diversification and liquidity: Evidence from China," Research in International Business and Finance, Elsevier, vol. 67(PA).
    2. Kyriazis, Nikolaos & Papadamou, Stephanos & Tzeremes, Panayiotis & Corbet, Shaen, 2023. "The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 307-317.
    3. Kou, Mingting & Yang, Yuanqi & Chen, Kaihua, 2024. "Financial technology research: Past and future trajectories," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 162-181.

    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. Ting Zhang & George J. Jiang & Wei‐Xing Zhou, 2021. "Order imbalance and stock returns: New evidence from the Chinese stock market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 2809-2836, June.
    2. 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.
    3. Nyborg, Kjell G. & Östberg, Per, 2014. "Money and liquidity in financial markets," Journal of Financial Economics, Elsevier, vol. 112(1), pages 30-52.
    4. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2011. "Recent trends in trading activity and market quality," Journal of Financial Economics, Elsevier, vol. 101(2), pages 243-263, August.
    5. Qian, Xiaolin, 2014. "Small investor sentiment, differences of opinion and stock overvaluation," Journal of Financial Markets, Elsevier, vol. 19(C), pages 219-246.
    6. Justin Cox, 2021. "ISO order imbalances and individual stock returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 44(1), pages 5-23, April.
    7. Xiaojun Chu & Jianying Qiu, 2021. "Forecasting stock returns using first half an hour order imbalance," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3236-3245, July.
    8. Zhang, Ting & Gu, Gao-Feng & Zhou, Wei-Xing, 2019. "Order imbalances and market efficiency: New evidence from the Chinese stock market," Emerging Markets Review, Elsevier, vol. 38(C), pages 458-467.
    9. Chen, Zhiyu & Xu, Yun & Wang, Yu, 2023. "Can convertible bond trading predict stock returns? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    10. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    11. Sensoy, Ahmet & Omole, John, 2022. "Information content of order imbalance in the index options market," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 418-432.
    12. Jagjeev Dosanjh, 2017. "Exchange Initiatives and Market Efficiency: Evidence from the Australian Securities Exchange," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2017, January-A.
    13. repec:uts:finphd:34 is not listed on IDEAS
    14. Ibikunle, Gbenga & Gregoriou, Andros & Hoepner, Andreas G.F. & Rhodes, Mark, 2016. "Liquidity and market efficiency in the world's largest carbon market," The British Accounting Review, Elsevier, vol. 48(4), pages 431-447.
    15. Narayan, Paresh Kumar & Narayan, Seema & Westerlund, Joakim, 2015. "Do order imbalances predict Chinese stock returns? New evidence from intraday data," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 136-151.
    16. Anna Obizhaeva, 2009. "Portfolio Transitions and Stock Price Dynamics," Working Papers w0224, New Economic School (NES).
    17. Frieder, Laura, 2008. "Investor and price response to patterns in earnings surprises," Journal of Financial Markets, Elsevier, vol. 11(3), pages 259-283, August.
    18. Lin, William T. & Tsai, Shih-Chuan & Chiu, Peter, 2016. "Do foreign institutions outperform in the Taiwan options market?," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 101-115.
    19. Reza Bradrania & Andrew Grant & Peter Joakim Westerholm & Wei Wu, 2017. "Fool's mate: What does CHESS tell us about individual investor trading performance?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(4), pages 981-1017, December.
    20. Anna Obizhaeva, 2009. "Portfolio Transitions and Stock Price Dynamics," Working Papers w0224, Center for Economic and Financial Research (CEFIR).
    21. Espen Sirnes & Minh Thi Hong Dinh, 2021. "Tick Size and Price Reversal after Order Imbalance," IJFS, MDPI, vol. 9(2), pages 1-13, March.

    More about this item

    Keywords

    Fintech; Big data; Data analytics; Order imbalance; Algorithmic trading;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    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:eee:mulfin:v:62:y:2021:i:c:s1042444x21000402. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/mulfin .

    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.