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Using Twitter to Model 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. The leading assumption of this paper is that information which can be distilled from public debates on Twitter has 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 used Tweet counts collected from January 1, 2012 - September 27, 2013. 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 to select concepts and corresponding univariate time series that had error scores below chance level as defined by the random walk model. 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. This advantage 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, 1970, 1991) which when applied to the FX market maintains that all publicly available information is already integrated into exchange rates.

Suggested Citation

  • Dietmar Janetzko, 2014. "Using Twitter to Model the EUR/USD Exchange Rate," Papers 1402.1624, arXiv.org.
  • Handle: RePEc:arx:papers:1402.1624
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    1. Paul Newbold & Toni Rayner & Neil Kellard & Christine Ennew, 1998. "Is the dollar/ECU exchange rate a random walk?," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 553-558.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    3. Kilian, Lutz & Taylor, Mark P., 2003. "Why is it so difficult to beat the random walk forecast of exchange rates?," Journal of International Economics, Elsevier, vol. 60(1), pages 85-107, May.
    4. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    5. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
    6. Hong, Yongmiao & Li, Haitao & Zhao, Feng, 2007. "Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates," Journal of Econometrics, Elsevier, vol. 141(2), pages 736-776, December.
    7. Geweke, John F & Feige, Edgar L, 1979. "Some Joint Tests of the Efficiency of Markets for Forward Foreign Exchange," The Review of Economics and Statistics, MIT Press, vol. 61(3), pages 334-341, August.
    8. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    9. Michael R. King & Carol Osler & Dagfinn Rime, 2011. "Foreign exchange market structure, players and evolution," Working Paper 2011/10, Norges Bank.
    10. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    11. MacDonald, Ronald & Taylor, Mark P., 1994. "The monetary model of the exchange rate: long-run relationships, short-run dynamics and how to beat a random walk," Journal of International Money and Finance, Elsevier, vol. 13(3), pages 276-290, June.
    12. Aki-Hiro Sato & Hideki Takayasu, 2013. "Segmentation procedure based on Fisher's exact test and its application to foreign exchange rates," Papers 1309.0602, arXiv.org.
    13. Hsien-Yi Lee & Khatanbaatar Sodoikhuu, 2012. "Efficiency Tests in Foreign Exchange Market," International Journal of Economics and Financial Issues, Econjournals, vol. 2(2), pages 216-224.
    14. Josef Falkinger, 2008. "Limited Attention as a Scarce Resource in Information-Rich Economies," Economic Journal, Royal Economic Society, vol. 118(532), pages 1596-1620, October.
    15. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
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    Cited by:

    1. Damien Challet & Ahmed Bel Hadj Ayed, 2014. "Do Google Trend data contain more predictability than price returns?," Papers 1403.1715, arXiv.org.
    2. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas & Konstantinos Diamantaras, 2014. "Market Sentiment and Exchange Rate Directional Forecasting," Working Paper series 37_14, Rimini Centre for Economic Analysis.

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