Predicting foreign exchange in emerging markets with a nearest neighbor approach: fundamentals versus online attention indicators

Klender Cortez ()
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Klender Cortez: Facultad de Contaduría Pública y Administración

Financial Innovation, 2025, vol. 11, issue 1, 1-20

Abstract: Abstract Recently, internet users have significantly increased their use of search engines, and market investors are no exception. As a result, predictive models that incorporate scattered web-based information are developing as an area of forecasting. The objective of this research is to compare the predictive accuracy of fundamental macroeconomic variables, online attention series measured by the Google Trends search volume index, and a combination of both data types for the Mexican, Brazilian, Chilean, and Colombian currencies paired with the USD. The exchange rate series used in this study are sourced from a real-time platform. Four indicators capturing the fundamental macroeconomic differences between these emerging economies and the U.S. from January 2004 to March 2021 (monthly) were analyzed. To assess the predictive performance of the KNN algorithm, OLS regression and the random walk with drift model were compared. Considering in-sample predictions, the results generally exhibit lower estimation errors in the random walk with drift model, but in the joint fundamental–online attention data, the KNN and OLS predictions are more accurate than those of the random walk with drift. However, the KNN predictions based on out-of-sample fit generate the lowest estimation errors and the most accurate predictions for the joint fundamental–online attention data. Additionally, performance testing indicates that the KNN extended model outperforms the out-of-sample forecast for the OLS regression and the random walk with drift model.

Keywords: Exchange rate prediction; Google trends; Machine learning; KNN algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s40854-025-00863-z

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