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Predictive modeling in turbulent times – What Twitter reveals about the EUR/USD exchange rate

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  • Dietmar Janetzko

Abstract

Fast, global, and sensitively reacting to political, economic and social events of any kind – these are attributes that social media like Twitter share with foreign exchange markets. Does the former allow us to predict the latter above chance level? The leading assumption of this paper is that time series of Tweet counts have predictive content for exchange rate movements. This assumption prompted a Twitter-based exchange rate model that harnesses regARIMA analyses for short-term out-of-sample ex post forecasts of the daily closing prices of EUR/USD spot exchange rates. The analyses made use of Tweet counts collected from January 1, 2012 – September 27, 2013 via the Otter API of topsy.com . To identify concepts mentioned on Twitter with a predictive potential the analysis followed a 2-step selection. Firstly, a heuristic qualitative analysis assembled a long list of 594 concepts, e.g., Merkel, Greece, Cyprus, crisis, chaos, growth, unemployment expected to covary with the ups and downs of the EUR/USD exchange rate. Secondly, cross-validation using window averaging with a fixed-sized rolling origin was deployed. This was instrumental in selecting concepts and corresponding univariate time series with error scores below chance level as defined by the random walk model that is based only on the EUR/USD exchange rate. With regard to a short list of 17 concepts (covariates), in particular SP(Standard & Poor’s) and risk, the out-of-sample predictive accuracy of the Twitter-based regARIMA model was found to be repeatedly better than that obtained from both the random walk model and a random noise covariate in 1-step ahead forecasts of the EUR/USD exchange rate. The increase in predictive strength facilitated by information gleaned from Twitter was evident on the level of forecast error metrics (MSFE, MAE) when a majority vote over different estimation windows was conducted. The results challenge the semi-strong form of the efficient market hypothesis (Fama Journal of Finance, 25, 383-417, 1970 , Fama Journal of Finance, 46(15), 1575-1617, 1991) which when applied to the FX market maintains that all publicly available information is already integrated into exchange rates. Copyright Springer Science+Business Media New York 2014

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  • Dietmar Janetzko, 2014. "Predictive modeling in turbulent times – What Twitter reveals about the EUR/USD exchange rate," Netnomics, Springer, vol. 15(2), pages 69-106, September.
    • Handle: RePEc:kap:netnom:v:15:y:2014:i:2:p:69-106
    DOI: 10.1007/s11066-014-9087-y
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    3. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    4. Leighton Vaughan Williams & James Reade, 2014. "Prediction Markets, Twitter and Bigotgate," Economics Discussion Papers em-dp2014-09, Department of Economics, University of Reading.
    5. Omotosho, Babatunde S., 2020. "Central Bank Communication during Economic Recessions: Evidence from Nigeria," MPRA Paper 99655, University Library of Munich, Germany.

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