# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 6 Conclusions and Future Research
In contrast to more generalized Twitter and news-based indicators, Bloomberg's generated Twitter and news count variables employ clearly defined and replicable terms linked to objective measures. Moreover, certain indicator providers, such as Yahoo Finance, may yield identical values for TC and NC, which can be integrated into price prediction models. However, further analysis is imperative to ascertain why these variables demonstrate efficacy across the majority of scenarios but exhibit sub-optimal performance in specific instances. Thus, exploring these cases and identifying their underlying sources of impact may be a promising avenue for future research. Another prospective area for future investigation involves considering whether the influence of TC and NC variables correlates with the size of the company. More specifically, future studies are encouraged to explore whether the inclusion of TC and NC variables equally enhances stock-price prediction accuracy for small-, medium-, and large-sized companies. Furthermore, the observed variations in prediction performance between MLP and LSTM models can be attributed to the test-period length and the extent of data preprocessing. Traders and researchers should be conscious of these factors when selecting stock-price prediction models. To enhance model applicability across different scenarios, it is recommended to standardize data-preparation techniques to ensure the optimal performance of various model types. This standardization should be replicable for each use of a specific model and thereby promote consistency in the analyses related to stock-price prediction. Likewise, it should advance research aimed at refining neural networks. Additionally, exploring alternative neural network architectures beyond MLP and LSTM is advisable, in an attempt to identify simpler models that may outperform stock-price prediction. Moreover, the current study focused solely on the quantity of tweets and news (TC and NC) while neglecting their sentiment and diverse impacts. Future research could cover the varying impact weights of distinct tweets or news items for each stock and involve their dominant sentiment. Such considerations can be promising areas for future research, as they may refine prediction accuracy in the dynamic landscape of stock-market forecasting. Conceptualization, H.Z. and S.R. ; methodology, H.Z., M.N. and S.R. ; software, S.R. ; validation, M.N. and S.R. ; formal analysis, M.N. and S.R. ; investigation, H.Z. and S.R. ; resources, H.Z. and A.H.; data curation, S.R. ; writing--original draft preparation, M.N. ; writing--review and editing, H.Z., S.R. and A.H.; visualization, M.N. and S.R. ; supervision, H.Z. ; project administration, A.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability StatementData can be accessed on Bloomberg Market News website at [[https://www.bloomberg.com/](https://www.bloomberg.com/)]([https://www.bloomberg.com/](https://www.bloomberg.com/)). The dataset we used was daily and spanned from January 2015 to May 2020. The authors declare no conflict of interest.