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Foreign currency forecasting in emerging markets: What can stock, and bond markets tell us?

Phylaktis, K. ORCID: 0000-0001-9392-1682 & Yamani, E. (2025). Foreign currency forecasting in emerging markets: What can stock, and bond markets tell us?. Journal of Empirical Finance, article number 101641. doi: 10.1016/j.jempfin.2025.101641

Abstract

This paper provides the first comprehensive investigation on the informational role of financial market information in the profitable predictability of exchange rates in an out-of-sample (OOS) context in emerging markets. Within a comparative analysis framework across developed and emerging countries, we examine if international stock and bond returns can be exploited as a predictor for future spot exchange rate changes (statistical test), and if an economically profitable trading strategy can be executed (economic test). Our central finding is that currency traders can beat emerging markets conditionally on correctly predicting the direction of the OOS forecasted currency returns induced by emerging stock and, to a lesser extent, by bond market returns. By contrast, this profitability is not evident in developed countries data. This asymmetric evidence, on the power of stock and bond returns in the profitable predictability of currency returns across developed and emerging countries, is affected by the country-specific institutional quality.

Publication Type: Article
Additional Information: © 2025. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: exchange rates, out of sample predictability, emerging markets, equity returns, bond market returns
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School
Bayes Business School > Faculty of Finance
SWORD Depositor:
[thumbnail of Complete paper Foreign Forecasting 06.05.25.pdf] Text - Accepted Version
This document is not freely accessible until 24 January 2027 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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