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What can we Learn from Euro-Dollar Tweets?

Author

Listed:
  • Vahid Gholampour
  • Eric van Wincoop
Abstract
We use 633 days of tweets about the Euro/dollar exchange rate to determine their information content and the profitability of trading based on Twitter Sentiment. We develop a detailed lexicon used by FX traders to translate verbal tweets into positive, negative and neutral opinions. The methodologically novel aspect of our approach is the use of a model with heterogeneous private information to interpret the data from FX tweets. After estimating model parameters, we compute the Sharpe ratio from a trading strategy based on Twitter Sentiment. The Sharpe ratio outperforms that based on the well-known carry trade and is precisely estimated.

Suggested Citation

  • Vahid Gholampour & Eric van Wincoop, 2017. "What can we Learn from Euro-Dollar Tweets?," NBER Working Papers 23293, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23293
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    References listed on IDEAS

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    1. Martin D.D. Evans & Richard K. Lyons, 2017. "Order Flow and Exchange Rate Dynamics," World Scientific Book Chapters, in: Studies in Foreign Exchange Economics, chapter 6, pages 247-290, World Scientific Publishing Co. Pte. Ltd..
    2. Charles Engel & Kenneth D. West, 2005. "Exchange Rates and Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 113(3), pages 485-517, June.
    3. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    4. Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
    5. Craig Burnside & Martin Eichenbaum & Isaac Kleshchelski & Sergio Rebelo, 2011. "Do Peso Problems Explain the Returns to the Carry Trade?," The Review of Financial Studies, Society for Financial Studies, vol. 24(3), pages 853-891.
    6. Martin Evans and Dagfinn Rime, 2010. "Micro Approaches to foreign Exchange Determination," Working Papers gueconwpa~10-10-04, Georgetown University, Department of Economics.
    7. King, Michael R. & Osler, Carol L. & Rime, Dagfinn, 2013. "The market microstructure approach to foreign exchange: Looking back and looking forward," Journal of International Money and Finance, Elsevier, vol. 38(C), pages 95-119.
    8. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    9. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 09, European Central Bank.
    10. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    11. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    12. Wang, Jiang, 1994. "A Model of Competitive Stock Trading Volume," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 127-168, February.
    13. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 9, European Central Bank.
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    Cited by:

    1. Charles W. Calomiris & Harry Mamaysky, 2019. "Monetary Policy and Exchange Rate Returns: Time-Varying Risk Regimes," NBER Working Papers 25714, National Bureau of Economic Research, Inc.
    2. Michael Stiefel & Rémi Vivès, 2019. "'Whatever it Takes' to Change Belief: Evidence from Twitter," Working Papers halshs-02053429, HAL.
    3. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    4. Michael Stiefel & Rémi Vivès, 2022. "‘Whatever it takes’ to change belief: evidence from Twitter," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(3), pages 715-747, August.
    5. Zhang, Qisi & Frömmel, Michael & Baidoo, Edwin, 2024. "Donald Trump's tweets, political value judgment, and the Renminbi exchange rate," International Review of Financial Analysis, Elsevier, vol. 93(C).
    6. Tao Chen & Erin P. K. So & Isabel K. M. Yan, 2021. "Are crises sentimental?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 962-985, January.

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    More about this item

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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