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Is Exchange Rate Moody? Forecasting Exchange Rate with Google Trends Data

Author

Listed:
  • Michał Chojnowski

    (Warsaw School of Economics)

  • Piotr Dybka

    (Warsaw School of Economics)

Abstract

This paper proposes a novel method of exchange rate forecasting. We extend the present value model based on observable fundamentals by including three unobserved fundamentals: credit-market, financial-market, and price-market sentiments. We develop a method of sentiments extraction from Google Trends data on searched queries for different markets. Our method is based on evolutionary algorithms of variable selection and principal component analysis (PCA). Our results show that the extended vector autoregressive model (VAR) which includes markets' sentiment, shows better forecasting capabilities than the model based solely on fundamental variables or the random walk model (naive forecast).

Suggested Citation

  • Michał Chojnowski & Piotr Dybka, 2017. "Is Exchange Rate Moody? Forecasting Exchange Rate with Google Trends Data," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(1), pages 1-21, June.
    • Handle: RePEc:sgh:erfinj:v:2:y:2017:i:1:p:1-21
    DOI: 10.33119/ERFIN.2017.2.1.1
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    References listed on IDEAS

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    2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
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    4. Ko, Hsiu-Hsin & Ogaki, Masao, 2015. "Granger causality from exchange rates to fundamentals: What does the bootstrap test show us?," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 198-206.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Hsiu-Hsin Ko, 2015. "On the indirect causality relation from exchange rates to fundamentals," Economics Bulletin, AccessEcon, vol. 35(3), pages 1518-1524.
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    8. Michele Ca’ Zorzi & Jakub Muck & Michal Rubaszek, 2016. "Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses," Open Economies Review, Springer, vol. 27(3), pages 585-609, July.
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    10. Ince, Onur, 2014. "Forecasting exchange rates out-of-sample with panel methods and real-time data," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 1-18.
    11. Michele Ca’ Zorzi & Jakub Muck & Michal Rubaszek, 2016. "Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses," Open Economies Review, Springer, vol. 27(3), pages 585-609, July.
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    Cited by:

    1. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.
    2. Piotr Dybka, 2020. "One model or many? Exchange rates determinants and their predictive capabilities," KAE Working Papers 2020-053, Warsaw School of Economics, Collegium of Economic Analysis.
    3. Svatopluk Kapounek & Zuzana Kučerová & Evžen Kočenda, 2022. "Selective Attention in Exchange Rate Forecasting," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(2), pages 210-229, May.

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    More about this item

    Keywords

    exchange rate; forecasting; market sentiment; Google Trends; PCA; VAR;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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