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Market Sentiment and Exchange Rate Directional Forecasting

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  • 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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    1. King, Michael & Sarno, Lucio & Sojli, Elvira, 2010. "Timing exchange rates using order flow: The case of the Loonie," Journal of Banking & Finance, Elsevier, vol. 34(12), pages 2917-2928, December.
    2. 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..
    3. Vasilios Plakandaras & Theophilos Papadimitriou & Periklis Gogas, 2015. "Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 560-573, November.
    4. Rime, Dagfinn & Sarno, Lucio & Sojli, Elvira, 2010. "Exchange rate forecasting, order flow and macroeconomic information," Journal of International Economics, Elsevier, vol. 80(1), pages 72-88, January.
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    6. Gehrig, Thomas & Menkhoff, Lukas, 2004. "The use of flow analysis in foreign exchange: exploratory evidence," Journal of International Money and Finance, Elsevier, vol. 23(4), pages 573-594, June.
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    Cited by:

    1. 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.
    2. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2017. "The Role of Economic Uncertainty in Forecasting Exchange Rate Returns and Realized Volatility: Evidence from Quantile Predictive Regressions," Working Papers 201774, University of Pretoria, Department of Economics.

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