# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 4 Results and Discussion
### Results with Event Sentiments
The results indicate that the deep learning model is more accurate as compared to linear regression and support vector regression when the sentiment of the 2012 US election is considered in the model. The _MAE_ was 3.250, 3.127, and 2.613 for linear, support vector regression, and deep learning model, respectively. Similarly, the _RMSE_ was 4.223, 4.590, and 3.534 for linear, support \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**With** & & & & & & \\
**Sentiment** & & & & & & \\ \hline \multirow{2}{*}{**Final HK**} & **Linear** & **Support Vector** & **Deep** & **Linear** & **Support Vector** & **Deep** \\  & **Regression** & **Regression** & **Learning** & **Regression** & **Regression** & **Learning** \\ \hline Hong Kong & & & & & & \\ protest 2014 & & & & & & \\ US election 2012 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for Hong Kong (HK) with event sentiments. Figure 4: The plot of 50 forecasted and actual values of the foreign exchange rate of Hong Kong considering linear, support vector, and deep learning model with the sentiment of Hong Kong protest and US election. LR: Linear regression, SVR: Support vector regression, DL: Deep learning. vector regression, and deep learning model, respectively. The impact of the 2016 Lahore blast was less significant than that of the 2012 US election. Therefore, it is evident that the exchange rate of Pakistan is more exposed to global events. Figure 5 shows the graphical overview of the foreign exchange rate of Pakistan by incorporating the sentiments of the US election and the Lahore blast. The plot considers 50 forecasted and actual values of the foreign exchange rate of Pakistan with the sentiment of US election and Lahore blast from top to bottom. All the predicted series were calculated using linear regression, support vector regression, and deep learning model. Since the plotted values of deep learning forecasted points are close to the actual when the sentiment of US election was considered, it is evident that the exchange rate of Pakistan is more exposed to the events occurring in the US. However, in the case of the Lahore blast sentiment, support vector regression shows a better forecast pattern. Table 8 presents the results of exchange prediction in the presence of sentiments of mega-events that are related to the UK. We considered Brexit 2016 as a local event to predict the UK exchange rate. The 2012 US election was used as a global event to incorporate sentiment for exchange rate prediction. Results of all the three models show that \(MAE\) and \(RMSE\) were lower when sentiments of mega-events were incorporated in the model. Since \(MAE\) and \(RMSE\) were lower compared to the results calculated without sentiment, as a whole, the accuracy of exchange rate prediction improved because of sentiment. Figure 6 shows the graphical overview of the foreign exchange rate of the UK by incorporating the sentiments of the US election and Brexit. The plot considers 50 forecasted and actual values of the foreign exchange rate of the UK with the sentiment of the US election and Brexit from top to bottom. All the predicted series are calculated using linear regression, support vector regression, and deep learning model. Since the plotted values of support vector regression and deep learning forecasted points are close to the actual, in the case of the UK, the results of support vector and deep learning model are more accurate as compared to those of linear regression. However, the US election sentiment shows more significant improvement in forecasted plots as compared to Brexit.