# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 6 Conclusions and Future Research  
This study investigated the influence of Twitter count (TC) and news count (NC) variables on stock-price prediction within both normal and market conditions. We incorporate Bloomberg Twitter and news publication count variables into MLP and LSTM neural networks to assess their predictive influence. Additionally, we analyze these effects during the market panic to evaluate their stability. Our methodology integrates MLP and LSTM neural networks with technical variables (T variables), TC, and NC (creating T+ variables) for price prediction. The models are trained on data from January 2015 to December 2018 and tested on normal (January 2019 to November 2019) and panic periods (December 2019 to May 2020). We applied statistical analyses to these results, which revealed a notable enhancement in stock-price prediction accuracy across various model types when these additional variables were incorporated. Furthermore, the comparison between the T and T+ variables indicated that both traders and researchers could derive substantial benefits from including TC and NC variables as inputs in neural network-based stock-price prediction models. This integration not only enhanced prediction accuracy and provided significant value to traders and investors, but it also facilitated the seamless incorporation of public opinion into prediction models. Given the escalating impact of social media on societal perspectives, the inclusion of TC and NC variables allows traders to consider the public's perception of corporations and products in their analyses. This strategic utilization of social media data empowers traders to make more informed decisions, reflecting a nuanced understanding of market sentiment and public opinion. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **T Vars** & **T+ Vars** & **Nor-Set** & **Pan-Set** \\ \hline Mean & & 0.073395 & 0.069357 & 0.045699 & 0.097053 \\ Range & & 0.141446 & 0.158834 & 0.072171 & 0.138198 \\ ST.Dev & & 0.040658 & 0.040633 & 0.016259 & 0.041585 \\ CV & & 0.554 & 0.586 & 0.356 & 0.428 \\ Mean & & 0.037357 & 0.024891 & 0.025188 & 0.037060 \\ Range & & 0.147494 & 0.035011 & 0.036088 & 0.142744 \\ ST.Dev & & 0.035671 & 0.008374 & 0.008748 & 0.035683 \\ CV & & 0.955 & 0.336 & 0.347 & 0.963 \\ \hline \hline \multicolumn{6}{c}{MLP vs. LSTM} & Average \\ \(+/-\)Mean & & \(-0.036038\) & \(-0.044466\) & \(-0.020511\) & \(-0.059993\) & \(-0.0403\) \\ \(+/-\)CV & & \(-0.401\) & 0.249 & 0.008 & \(-0.534\) & \(-0.1693\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The RMSE analysis for T vs. T+ variables and the panic test set vs. the normal test set. While the proposed models aimed to analyze the impact of news and tweets on stock-price prediction accuracy, it is important to acknowledge certain limitations for further research. First, this study focused primarily on the COVID-19 pandemic as a period of market distress. The impact identified in this case might not capture the full range of possible market behaviors under different types of panic conditions, crises, financial crashes, or geopolitical events. In addition, this study used LSTM and MLP models, which, while established, may not represent the cutting edge in predictive modeling. More advanced techniques, like hybrid approaches, might provide better performance or additional insights. Furthermore, the study used sentiment analysis tools to quantify investor sentiment from news and tweets. The accuracy of these tools can vary, and errors in sentiment classification could affect the overall findings. Finally, the impact of news and tweets on stock prices might vary across different regions and industry sectors. The study does not explicitly address whether the findings are consistent across various markets and industries or if specific segments primarily drive the results. There are several avenues to develop future studies in this area. The current configuration of TC and NC variables in T+ entails countable measures and shares similarities with other variables.