# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 5 Conclusions  
Foreign currency exchange rate is an economic indicator that indirectly impacts even those businesses which are not directly involved with imports/exports of goods. Hence accurate prediction of the exchange rate is an issue related to a small business as well as the government of a country. The study is based on a dataset providing information about the exchange rate of currencies from April 2008 to January 2019. The paper proposes a deep-learning-based technique for the prediction of foreign currency exchange rate. We have calculated the sentiment of some major local and global events for Hong Kong, UK, and Pakistan. The sentiment analysis values are used as an input along with other parameters. The results show that the currency market is heavily dependent on sociopolitical issues and higher accuracy can only be achieved if these events, known sentiments in this study, are taken into consideration while predicting the exchange rate. The results obtained through the proposed methodology are compared with linear and support regression models, both well-known statistical techniques for the said problem. The results show that deep learning-based methods perform better than other methods. It has also been observed that the proposed model performed better with sentiment analysis. Moreover, events happening in the US have a significant impact on the currency exchange rate of Hong Kong, Pakistan, and the UK when social media sentiment is incorporated. The results of the prediction improved when sentiment was considered in the model, therefore these countries are said to be more exposed to mega-events happening across borders.  
Conceptualization, M.Y. and M.M.; formal analysis, M.Y., S.A., and M.M.; investigation, S.A. and I.M.; methodology, M.M.; project administration, S.R.; visualization, F.A.; writing--original draft, M.Y.D. and M.M.; writing--review and editing, I.M. and S.R.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1A09919551).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**With Sentiment** & \multicolumn{3}{c}{**Mean Absolute Error (MAE)**} & \multicolumn{3}{c}{**Root Mean Squared Error (RMSE)**} \\ \hline
**The Final UK** & \begin{tabular}{c} **Linear** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Support Vector** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Deep** \\ **Learning** \\ \end{tabular} & \begin{tabular}{c} **Linear** \\ **Regression** \\ \end{tabular} & \
\begin{tabular}{c} **Support Vector** \\ **Regression** \\ \end{tabular} &
\begin{tabular}{c} **Deep** \\ **Learning** \\ \end{tabular} \\ \hline Brexit 2016 & 0.060 \(\pm\) 0.056 & 0.060 \(\pm\) 0.057 & 0.064 \(\pm\) 0.069 & 0.082 \(\pm\) 0.000 & 0.082 \(\pm\) 0.000 & 0.095 \(\pm\) 0.000 \\ US election 2012 & 0.067 \(\pm\) 0.070 & 0.066 \(\pm\) 0.072 & 0.078 \(\pm\) 0.067 & 0.097 \(\pm\) 0.000 & 0.098 \(\pm\) 0.000 & 0.102 \(\pm\) 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results for the UK with event sentiments.  
Figure 6: The plot of 50 forecasted and actual values of the foreign exchange rate of the UK considering linear, support vector, and deep learning model with the sentiment of US election and Brexit. LR: Linear regression, SVR: Support vector regression, DL: Deep learning.