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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
    1. Pauline Shum & Walid Hejazi & Edgar Haryanto & Arthur Rodier, 2016. "Intraday Share Price Volatility and Leveraged ETF Rebalancing," Review of Finance, European Finance Association, vol. 20(6), pages 2379-2409.
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    4. Fabrice Riva & Thomas Marta, 2022. "Do ETFs increase the comovements of their underlying assets? Evidence from a switch in ETF replication technique," Post-Print hal-03969597, HAL.
    5. Bartlett, Robert P. & McCrary, Justin, 2019. "How rigged are stock markets? Evidence from microsecond timestamps," Journal of Financial Markets, Elsevier, vol. 45(C), pages 37-60.
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    7. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
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