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

Peter B. Lerner ()
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Peter B. Lerner: SUNY-Brockport, Brockport, NY 14420, USA

JRFM, 2023, vol. 16, issue 7, 1-17

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.

Keywords: causality; market microstructure; market imbalance; TAQ-ARCA; C-GAN neural network (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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