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A New Entropic Measure for the Causality of the Financial Time Series

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  • Peter B. Lerner

    (SUNY-Brockport, Brockport, NY 14420, USA
    School of Business Administration, Anglo-American University, ul. Letnikow, 120 Prague, Czech Republic)

Abstract

A new econometric methodology based on deep learning is proposed for determining the causality of the financial time series. This method is applied to the imbalances in daily transactions in individual stocks and also in exchange-traded funds (ETFs) with a nanosecond time stamp. Based on our method, we conclude that transaction imbalances of ETFs alone are more informative than transaction imbalances in the entire market despite the domination of single-issue stocks in imbalance messages.

Suggested Citation

  • Peter B. Lerner, 2023. "A New Entropic Measure for the Causality of the Financial Time Series," JRFM, MDPI, vol. 16(7), pages 1-17, July.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:7:p:338-:d:1195827
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    References listed on IDEAS

    as
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    4. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    5. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    6. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    7. Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
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