Keskin, Z;
Aste, T;
(2020)
Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices.
Royal Society Open Science
, 7
(9)
, Article 200863. 10.1098/rsos.200863.
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Abstract
Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z-score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.
| Type: | Article |
|---|---|
| Title: | Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsos.200863 |
| Publisher version: | https://doi.org/10.1098/rsos.200863 |
| Language: | English |
| Additional information: | © 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | causality, transfer entropy, information theory, cryptocurrency |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10111783 |
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