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

Michał Chojnowski and Piotr Dybka

Econometric Research in Finance, 2017, vol. 2, issue 1, 1-21

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).

Keywords: exchange rate; forecasting; market sentiment; Google Trends; PCA; VAR (search for similar items in EconPapers)
JEL-codes: C53 F31 F37 G17 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://erfin.org/journal/index.php/erfin/article/view/13 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sgh:erfinj:v:2:y:2017:i:1:p:1-21

DOI: 10.33119/ERFIN.2017.2.1.1

Access Statistics for this article

Econometric Research in Finance is currently edited by Dobromił Serwa and Piotr Wdowiński

More articles in Econometric Research in Finance from SGH Warsaw School of Economics, Collegium of Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Dobromił Serwa (dserwa@sgh.waw.pl).