# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 5 Results and Discussion
### Comparing the Impact of T and T+ Variables  
To determine whether using T+ variables leads to a significant improvement in price prediction, we analyzed instances where the utilization of T+ variables, as opposed to T variables, resulted in improvements in terms of RMSE. As shown in Table 5, there are 25 instances where using T+ variables led to an RMSE reduction. Among those instances that do not lead to improvement, four are associated with Walmart, two are related to Tesla, and one is concerned with Facebook. Furthermore, among these seven instances, four are associated with the LSTM model, and three are related to the MLP model. It is also worth noting that Tesla had a model that improved by 42% and another that lowered by 89%. In essence, this signifies an ample need for researchers and traders to test across a wide variety of scenarios before deciding to use a variable as an input in a stock-price prediction model. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & **TC** & **NC** & **Price** \\ \cline{3-5}  & & **Train vs. Pan-Set** & **Train vs. Pan-Set** & **Train vs. Pan-Set** \\ \hline \multirow{2}{*}{Google} & CV & \(-7\%\) & \(-15\%\) & \(-14\%\) \\  & Mean & \(-46\%\) & \(23\%\) & \(-49\%\) \\ \hline \multirow{2}{*}{Amazon} & CV & \(-19\%\) & \(-7\%\) & \(-37\%\) \\  & Mean & \(-55\%\) & \(-17\%\) & \(114\%\) \\ \hline \multirow{2}{*}{Walmart} & CV & \(-27\%\) & \(-35\%\) & \(6\%\) \\  & Mean & \(-26\%\) & \(132\%\) & \(129\%\) \\ \hline \multirow{2}{*}{Facebook} & CV & \(-48\%\) & \(-46\%\) & \(-16\%\) \\  & Mean & \(-10\%\) & \(-71\%\) & \(47\%\) \\ \hline \multirow{2}{*}{Netflix} & CV & \(22\%\) & \(38\%\) & \(-45\%\) \\  & Mean & \(-69\%\) & \(-53\%\) & \(117\%\) \\ \hline \multirow{2}{*}{Apple} & CV & \(-59\%\) & \(-20\%\) & \(-16\%\) \\  & Mean & \(-71\%\) & \(0\%\) & \(105\%\) \\ \hline \multirow{2}{*}{Ford} & CV & \(-103\%\) & \(-2\%\) & \(12\%\) \\  & Mean & \(-14\%\) & \(34\%\) & \(-44\%\) \\ \hline \multirow{2}{*}{Tesla} & CV & \(-35\%\) & \(-13\%\) & \(-10\%\) \\  & Mean & \(-57\%\) & \(13\%\) & \(52\%\) \\ \hline \multirow{2}{*}{CV} & Top 50\% & \(-25\%\) & \(-26\%\) & \(-15\%\) \\  & Bottom 50\% & \(-44\%\) & \(1\%\) & \(-15\%\) \\ \hline \multirow{2}{*}{Mean} & Top 50\% & \(-35\%\) & \(17\%\) & \(60\%\) \\  & Bottom 50\% & \(-53\%\) & \(-2\%\) & \(57\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: T+ variables’ mean and coefficient of variation in the panic test set and training data. A summary of these impacts is presented in Table 6. As highlighted in Table 6, the most considerable improvement belongs to Ford, which has a 36% improvement over the stock prediction using T+ variables. Notably, both MLP and LSTM configurations for Ford exhibit superior performance with the pan-test set compared to the normal test set. This ranking will guide the analysis of input variables, test data, and NN selection in future assessments. Notably, no clear trends are apparent concerning the magnitude of mean TC and NC variables and their relationship to RMSE% improvement. Table 7, however, reveals insights when comparing the average TC to the average NC ratios. The top 50% of the list in terms of RMSE% reduction was associated with the data featuring an average ratio of 1.66 TC/NC. In contrast, the bottom 50% of the list exhibited an almost 57% increase in the average TC to average NC ratio, reaching 2.92. A potential interpretation of these findings suggests that neither the TC nor NC alone necessarily fortifies the variables, emphasizing the nuanced interaction of factors in predictive modeling.