Selective Attention in Exchange Rate Forecasting

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 considering selective attention improves forecasting results. Specifically, we document a growing impact of foreign trade and monetary policy news on the Euro/United States of America dollar currency pair following the global financial crisis. Overall, our results point to the existence of selective attention in the case of most currency pairs.