Nothing Special   »   [go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/23293.html
   My bibliography  Save this paper

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
    Note: AP IFM
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w23293.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gholampour, Vahid & van Wincoop, Eric, 2019. "Exchange rate disconnect and private information: What can we learn from Euro-Dollar tweets?," Journal of International Economics, Elsevier, vol. 119(C), pages 111-132.
    2. Gu, Chen & Kurov, Alexander, 2020. "Informational role of social media: Evidence from Twitter sentiment," Journal of Banking & Finance, Elsevier, vol. 121(C).
    3. Mark Johnman & Bruce James Vanstone & Adrian Gepp, 2018. "Predicting FTSE 100 returns and volatility using sentiment analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 253-274, November.
    4. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    5. Aleksejs Krecetovs & Pasquale Della Corte, 2016. "Macro uncertainty and currency premia," 2016 Meeting Papers 624, Society for Economic Dynamics.
    6. Javier Bianchi & Saki Bigio & Charles Engel, 2021. "Scrambling for Dollars: International Liquidity, Banks and Exchange Rates," Working Papers 786, Federal Reserve Bank of Minneapolis.
    7. repec:gla:glaewp:2023-03 is not listed on IDEAS
    8. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2015. "Exchange rate forecasts and expected fundamentals," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 235-256.
    9. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2012. "Properties of foreign exchange risk premiums," Journal of Financial Economics, Elsevier, vol. 105(2), pages 279-310.
    10. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    11. Engel, Charles, 2014. "Exchange Rates and Interest Parity," Handbook of International Economics, in: Gopinath, G. & Helpman, . & Rogoff, K. (ed.), Handbook of International Economics, edition 1, volume 4, chapter 0, pages 453-522, Elsevier.
    12. Lock, Eduardo & Winkelried, Diego, 2015. "Flujos de órdenes en el mercado cambiario y el valor intrínseco del Nuevo Sol," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 29, pages 33-54.
    13. Philippe Bacchetta & Eric Van Wincoop, 2006. "Can Information Heterogeneity Explain the Exchange Rate Determination Puzzle?," American Economic Review, American Economic Association, vol. 96(3), pages 552-576, June.
    14. Jorge Selaive & Vicente Tuesta, 2006. "Can fluctuations in the consumption-wealth ratio help to predict exchange rates?," Applied Financial Economics, Taylor & Francis Journals, vol. 16(17), pages 1251-1263.
    15. Shah, Syed Faisal & Albaity, Mohamed, 2022. "The role of trust, investor sentiment, and uncertainty on bank stock return performance: Evidence from the MENA region," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    16. Kim, Soon-Ho & Kim, Dongcheol, 2014. "Investor sentiment from internet message postings and the predictability of stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 708-729.
    17. Lustig, Hanno & Roussanov, Nikolai & Verdelhan, Adrien, 2014. "Countercyclical currency risk premia," Journal of Financial Economics, Elsevier, vol. 111(3), pages 527-553.
    18. Peng Xie, 2022. "The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market," Information Systems Frontiers, Springer, vol. 24(4), pages 1287-1303, August.
    19. Bakshi, Gurdip & Carr, Peter & Wu, Liuren, 2008. "Stochastic risk premiums, stochastic skewness in currency options, and stochastic discount factors in international economies," Journal of Financial Economics, Elsevier, vol. 87(1), pages 132-156, January.
    20. Dong, Wei & Nam, Deokwoo, 2013. "Exchange rates and individual good's price misalignment: Evidence of long-horizon predictability," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 611-636.
    21. Craig Burnside & Mario Cerrato & Zhekai Zhang, 2023. "Foreign exchange order flow as a risk factor," Working Papers 2023_03, Business School - Economics, University of Glasgow.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:23293. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.