Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices.

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ID: 171403
2020
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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 -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.
Reference Key
keskin2020informationtheoreticroyal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Keskin, Z;Aste, T;
Journal Royal Society open science
Year 2020
DOI
10.1098/rsos.200863
URL
Keywords
information theory cryptocurrency causality granger transfer entropy

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