# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 4 Results and Discussion
### Results without Event Sentiments  
Here, the results without a sentiment for exchange rate prediction are presented using linear regression, support vector regression, and deep learning model. We considered the exchange rate of the UK, Hong Kong, and Pakistan in terms of US Dollars. Since the exchange rate is highly exposed to global volatile indicators such as crude oil prices and world gold price index, we used crude oil prices and world gold price index as explanatory variables in our models.  
Table 5 shows the results of exchange prediction for Hong Kong (HK), Pakistan, and the UK using linear regression, support vector regression, and deep learning approach. The forecasting parameters _MAE_ (absolute error) and _RMSE_ (root mean square error) for HK were 0.018 and 0.025 in the case of linear regression. Contrary to that, _MAE_ and _RMSE_ were minimum when a deep learning approach was considered for HK--_MAE_ was 0.017 and _RMSE_ was 0.024. The results of Pakistan and UK were more accurate in the case of support vector regression and deep learning models. Therefore, these models were more reliable for exchange rate prediction as compared to the linear regression model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Countries** & **Variables** & \multicolumn{2}{c}{**ADF Test Stat**} & \multicolumn{2}{c}{**Critical Value (\%)**} \\ \hline  & & **Level** & **1 st Difference** & \\ \hline \multirow{3}{*}{Hong Kong} & Exchange rate & \(-\)1.980 & \(-\)23.610 *** & \(-\)2.86 \\  & Oil prices & \(-\)1.613 & \(-\)47.594 *** & \(-\)2.86 \\  & Gold price index & \(-\)3.427 *** & & \(-\)2.86 \\  & Exchange rate & \(-\)0.161 & \(-\)45.311 *** & \(-\)2.86 \\ \multirow{3}{*}{Pakistan} & Oil prices & \(-\)1.613 & \(-\)47.594 *** & \(-\)2.86 \\  & Gold price index & \(-\)3.427 *** & & \(-\)2.86 \\ \cline{1-1}  & Exchange rate & \(-\)2.231 & \(-\)51.735 *** & \(-\)2.86 \\ \cline{1-1}  & Oil prices & \(-\)1.613 & \(-\)47.594 *** & \(-\)2.86 \\ \cline{1-1}  & Gold price index & \(-\)3.427 *** & & \(-\)2.86 \\ \hline \hline \end{tabular}  
*** shows that test stat is significant at 1%. The Phillips–Perron test also yields the same results and can be provided if required.  
\end{table}
Table 4: Augmented Dickey–Fuller (ADF) unit root test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Exchange Rate**} & \multicolumn{3}{c}{**Other Variables**} \\ \hline  & **Hong Kong** & **Pakistan** & **UK** & **Oil Prices** & **Gold Price Index** \\ \hline Mean & 7.769 & 98.257 & 1.536 & 74.645 & 21.424 \\ Median & 7.758 & 101.856 & 1.554 & 79.350 & 19.380 \\ Std. Dev. & 0.0261 & 14.740 & 0.170 & 22.724 & 7.689 \\ Jarque–Bera & 1729.421 *** & 24.135 *** & 164.269 *** & 186.477 *** & 4619.799 *** \\ Observation & 2121 & 2131 & 2632 & 2121 & 2121 \\ \hline \hline \end{tabular}  
*** shows that test stat is significant at 1%.  
\end{table}
Table 3: Descriptive statistic.  
Figure 3 shows the graphical overview of the foreign exchange rate of Hong Kong, Pakistan, and the UK. The plot considers 50 forecasted and actual values of the foreign exchange rate of Hong Kong, Pakistan, Kong, Pakistan, and the UK from top to bottom. All the predicted series were calculated using linear regression, support vector regression, and deep learning models. The spread shows that, in the case of Pakistan and Hong Kong, the results of support vector regression and deep learning model are close to the actual line of the exchange rate series.