An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

TLDR

Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. This paper proposes a deep‑learning model to forecast foreign exchange rates. The model integrates event‑sentiment analysis from 5.9 million tweets, incorporates volatile commodity prices, and is evaluated on PKR/USD, GBP/USD, and HKD/USD. The model outperforms conventional statistical methods and demonstrates that the three currencies are highly sensitive to U.S.

Abstract

Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.