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Market Sentiment and Exchange Rate Directional Forecasting

Author

Listed:
  • Vasilios Plakandaras

    (Department of Economics, Democritus University of Thrace, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, Greece)

  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, Greece; The Rimini Centre for Economic Analysis, Italy)

  • Konstantinos Diamantaras

    (Department of Information Technology, TEI of Thessaloniki, Greece)

Abstract

The microstructural approach to the exchange rate market claims that order flows on a currency can accurately reflect the short-run dynamics its exchange rate. In this paper, instead of focusing on order flows analysis we employ an alternative microstructural approach: we focus on investors' sentiment on a given exchange rate as a possible predictor of its future evolution. As a proxy of investors' sentiment we use StockTwits posts, a message board dedicated to finance. Within StockTwits investors are asked to explicitly state their market expectations. We collect daily data on the nominal exchange rate of four currencies against the U.S. dollar and the extracted market sentiment for the year 2013. Employing econometric and machine learning methodologies we develop models that forecast in out-of-sample exercise the future direction of the four exchange rates. Our empirical findings reject the Efficient Market Hypothesis even in its weak form for all four exchange rates. Overall, we find evidence that investors' sentiment as expressed in public message boards can be an additional source of information regarding the future directional movement of the exchange rates to the ones proposed by economic theory.

Suggested Citation

  • 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.
    • Handle: RePEc:rim:rimwps:37_14
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    References listed on IDEAS

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    Cited by:

    1. Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    2. Periklis Gogas & Theofilos Papadimitriou & Dimitrios Karagkiozis, 2018. "The Fama 3 and Fama 5 factor models under a machine learning framework," Working Paper series 18-05, Rimini Centre for Economic Analysis.
    3. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    4. Shahzad, Syed Jawad Hussain & Kyei, Clement Kweku & Gupta, Rangan & Olson, Eric, 2021. "Investor sentiment and dollar-pound exchange rate returns: Evidence from over a century of data using a cross-quantilogram approach," Finance Research Letters, Elsevier, vol. 38(C).
    5. Rangan Gupta & Vasilios Plakandaras, 2019. "Efficiency in BRICS Currency Markets Using Long-Spans of Data: Evidence from Model-Free Tests of Directional Predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 152-165.
    6. Omotosho, Babatunde S. & Tumala, Mohammed M., 2019. "A Text Mining Analysis of Central Bank Monetary Policy Communication in Nigeria," MPRA Paper 98850, University Library of Munich, Germany.
    7. Rahimi, Fatemeh & Mousavian Anaraki, Seyed Alireza, 2020. "Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(4), pages 423-444, October.
    8. Omotosho, Babatunde S., 2020. "Central Bank Communication during Economic Recessions: Evidence from Nigeria," MPRA Paper 99655, University Library of Munich, Germany.
    9. Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024. "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers 2405.11431, arXiv.org, revised Jun 2024.

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    • C00 - Mathematical and Quantitative Methods - - General - - - General

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