Selective Attention in Exchange Rate Forecasting

Abstract

We analyze the exchange rate forecasting performance under the assumption of selective attention. Although currency markets react to a variety of different information, we hypothesize that market participants process only a limited amount of information. Our analysis includes more than 100,000 news articles relevant to the six most-traded foreign exchange currency pairs for the period of 1979–2016. We employ a dynamic model averaging approach to reduce model selection uncertainty and to identify time-varying probability to include regressors in our models. Our results show that smaller sizes models accounting for the presence of selective attention offer improved fitting and forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the euro/dollar exchange rate following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs.

Keywords:

• Exchange rate
• Selective attention
• News
• Forecasting
• Dynamic model averaging

JEL CLASSIFICATION:

• F33
• G41
• C11

Acknowledgement

We benefited from comments and suggestions made by Jesus Crespo-Cuaresma, Makram El-Shagi, Jarko Fidrmuc, Tomáš Holub, Roman Horváth, an anonymous referee, and participants of several presentations. Kočenda ackowledges hospitality of the Kyoto Institute of Economic Research.

Notes

1 From a psychological point of view, there is room for discussion about selective attention when economic agents decide to accept only a limited amount of information. Such a decision does not lead to optimal behaviour and the agents involved instead behave inattentively. For a detailed review of theoretical and empirical papers concerning the economics of attention, see Festré and Garrouste (2015).

2 The global financial crisis (GFC) refers to a sever worldwide financial crisis between mid 2007 and early 2009.

3 All exchange rates are quoted against the U.S. dollar, i.e., one unit of a currency in terms of the U.S. dollar. This is a typical approach in the forex literature – any potential domestic (U.S.) shocks are integrated into all currency quotes.

4 We use publicly available data sources: XE.COM, OECD, Eurostat, FRED, CBOE, Yahoo Finance, and Bloomberg Database. Detailed descriptions of all the regressors are provided in the Appendix, Table A1. All the analyzed time series are transformed by log differences.

5 All selected stock market indices were transformed to differentials of their log returns against S&P 500.

6 The normalized search query index at a given point in time is a ratio of the total search volume for each query to the total number of all search queries. We use keywords “Australian Dollar,” “Canadian Dollar,” “British Pound,” “Euro,” “Japanese Yen,” “New Zealand Dollar,” “United States Dollar,” with emphasis on the searches in the category “Currency.”

Additional information

Funding

The research support by the Czech Science Foundation grant No. 20-11769S is gratefully acknowledged. The usual disclaimer applies.