# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
###### 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. foreign exchange rate; forecasting; event sentiment; deep learning; regression; SVM Article  
An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System  
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that consider optimistic and pessimistic fundamentals are used because stock markets are ambiguous [4]. The result of technical analysis is well-known statistical models such as autoregressive moving average and generalized auto regressive conditional heteroscedasticity (GARCH) [5; 6]. However, both these methods do not deal with the nonlinearity of time series data. In recent times, different advanced computing methods yield better results for time series forecasting [7]. The success of artificial neural networks (ANNs) is largely due to the discovery of a nonlinear relationship without prior knowledge of the information and self-training and self-prediction ability. ANN is a mathematical technique intended for use in solving optimization problems. ANN is a technique that works well with few assumptions and can tolerate noisy and nonlinear data [8], making it an ideal candidate for financial time series data. ANN mimics the behavior of the human brain, which utilizes previous knowledge for solving unseen problems. ANN is basically a network of nodes arranged in different layers interconnected with some links with associated weights. The network is trained by feeding some training data. This training helps in deciding about a decision function, which is chosen from a group of functions represented by the structure of the ANN. This decision function can be determined by assigning proper weights to the network. If the weights are chosen optimally, the error is minimized [9]. A common technique for minimizing the error is known as backpropagation Neural Network (NN), in which the error is fed backward to adjust the weights resulting in minimizing the error [10]. High accuracy can be achieved using good features and a suitable network pattern, i.e., forward or backward pattern. Various types of ANNs are employed for financial forecasting as well as currency rate prediction. These include functional link ANN [11], quantile regression ANN [12], backpropagation ANN [13], and radial basis function ANN [10] to name a few. Recently, deep learning has seen immense growth in the field of pattern recognition and time series analysis. This study focuses on designing a deep-learning-based model to forecast the exchange rates. The main objective is to design an intelligent model by considering important factors such as crude oil rate and gold price index.