Is Exchange Rate Moody? Estimating the Influence of Market Sentiments With Google Trends

Michał Chojnowski, Piotr Dybka

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 (naïve forecast).


Full Text:

PDF

References


Angeletos, G.-M. (2008). Idiosyncratic Sentiments and Coordination Failures. MIT Department of Economics Working Paper No. 08-12.

Askitas, N., & Zimmerman, K. F. (2009). Google Econometrics and Unemployment Forecasting. IZA Discussion Papers 4201.

Ca’ Zorzi, M., Muck, J., & Rubaszek, M. (2016, July). Real Exchange Rate Forecasting and PPP: This Time the Random Walk Loses. Open Economies Review, vol. 27(3), pp. 585-609.

Choi, H., & Varian, H. (2012). Predicting the Present with Google Trends. The Economic Record, 2-9.

D'Amuri, F., & Marcucci, J. (2012). The predictive power of google searches in forecasting unemployment. Temi di discussione (Economic working papers) 891, Bank of Italy, 1-32.

Diebold, F. X., & Mariano, R. S. (1995). Comparing Predicitive Accuracy. Journal of Business & Economic Statistics, 13(3), pp. 253 - 263.

Engel, C., & West, K. D. (2005). Exchange rate and fundamentals. Journal of Political Economy, 113(3), pp. 485-517.

Garratt, A., & Mise, E. (2014). Forecasting exchange rates using panel model and model averaging. Economic Modelling, 37, pp. 32-40.

Ince, O. (2014). Forecasting exchange rates out-of-sample with panel methods and real-time data. Journal of International Money and Finance, pp. 1-18.

Ko, H.-H., & Ogaki, M. (2015). Granger causality from exchange rates to fundamentals: What does the bootstrap test show us? International Review of Economics & Finance, 38 (C), pp. 198-206.

Mark, N. C., & Sul, D. (2012). When are pooled panel-data regression forecasts of exchange rates more accurate than the time-series regression forecasts? In I. M. J. James, Handbook of Exchange Rates (pp. 256–281). John Wiley and Sons Inc.

McLaren, N., & Shanbhogue, R. (2011). Using internet search data as economic indicators. Bank of England Quarterly Bulletin, vol. 51(2), pp. 134-140.

Morales-Arias, L., & Moura, G. V. (2013). Adaptive forecasting of exchange rates with panel data. International Journal of Forecasting, 29(3)(493-509).


Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Econometric Research in Finance

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.