Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices
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 for...
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The Royal Society
2020-09-01
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| Series: | Royal Society Open Science |
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| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.200863 |
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| author | Z. Keskin T. Aste |
| author_facet | Z. Keskin T. Aste |
| author_sort | Z. Keskin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bd2e2e10f08c4dd595b042ff034a7fcf |
| institution | OA Journals |
| issn | 2054-5703 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj-art-bd2e2e10f08c4dd595b042ff034a7fcf2025-08-20T02:11:58ZengThe Royal SocietyRoyal Society Open Science2054-57032020-09-017910.1098/rsos.200863Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency pricesZ. Keskin0T. Aste1Department of Computer Science & Centre for Blockchain Technologies, University College London, Gower Street, WC1E 6EA London, UKDepartment of Computer Science & Centre for Blockchain Technologies, University College London, Gower Street, WC1E 6EA London, UKInformation 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.https://royalsocietypublishing.org/doi/10.1098/rsos.200863Grangercausalitytransfer entropyinformation theorycryptocurrency |
| spellingShingle | Z. Keskin T. Aste Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices Royal Society Open Science Granger causality transfer entropy information theory cryptocurrency |
| title | Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices |
| title_full | Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices |
| title_fullStr | Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices |
| title_full_unstemmed | Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices |
| title_short | Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices |
| title_sort | information theoretic measures for nonlinear causality detection application to social media sentiment and cryptocurrency prices |
| topic | Granger causality transfer entropy information theory cryptocurrency |
| url | https://royalsocietypublishing.org/doi/10.1098/rsos.200863 |
| work_keys_str_mv | AT zkeskin informationtheoreticmeasuresfornonlinearcausalitydetectionapplicationtosocialmediasentimentandcryptocurrencyprices AT taste informationtheoreticmeasuresfornonlinearcausalitydetectionapplicationtosocialmediasentimentandcryptocurrencyprices |