# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 2 Literature Review
### NN Techniques in Stock-Price Prediction  
Neural networks (NN) are an ML technique that has recently gained popularity in the domain of stock-price prediction. The earliest application of NNs in predicting stock-market trends dates back to the early 1990s [12; 15]. More recently, NNs have become widely adopted, given their proven practical and powerful attributes in stock-price prediction [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 35; 37; 48; 49; 55; 59; 60; 117; 138]. [PERSON] et al. [80] utilized data on stock movements to analyze whether trading behavior could be mapped and used to predict stock prices. For each individual stock, they first analyzed stock trading activities and mapped a network. Then, they classified the trading relationships and grouped them into appropriate categories. Next, they employed Granger causality analysis to prove that the stock prices were indeed correlated with the different trading categories. Moreover, they used a simple three-layer feed-forward NN to test the trading predictability power. The NN incorporated technical indicators as well as trading indicators. The results revealed that the NN performed well overall. Lastly, it is worth emphasizing that this positive result can be considered relatively intuitive, as it is generally well-known that the activities of one group of traders influence another. Furthermore, [PERSON] and [PERSON] [87] investigated whether market data, simple news item counts, business events, and sentiment scores could improve various ML algorithms in stock-price prediction. The authors considered NNs, decision trees, and basic regression. The results demonstrated that among the algorithms tested, only the NN could fully exploit the more intricate nature of the proposed sentiment/news inputs. The other models could not take advantage of these inputs, given the complicated relationship between price and sentiment/news indicators. [PERSON] et al. [111] utilized Shanghai Composite Index data and emotional data. Emotional data, in this case, involved sentiment analysis from the news and microblogs that were related to a specific company. The authors demonstrated that 15 input variables, comprised of sentiment and technical indicators, could successfully predict a Chinese company's stock opening prices. [PERSON] et al. [117] proposed a simple NN to examine whether the inclusion of accounting variables (generated from the release of accounting disclosures) improved the prediction accuracy of the NNs. The results showed that combining 48,204 daily stock closing prices of 39 companies with the respective accounting disclosure variables improved the NNs' prediction quality. However, this level of improvement drastically dropped when the NN predicted prices in 2011, a time of civil unrest in Tunisia. This extreme example is noteworthy, as it portrays how an observed variable (i.e., one that was able to consistently improve the model accuracy) could lose its impact when emotional events occurred. [PERSON] et al. [122] investigated if the prediction of the price of 20 Australian stocks by a neural network autoregressive (NNAR) model could be improved with the inclusion of inputs in the form of counts of both news articles and tweets. The sentiment-based indicators used in this study were generated by Bloomberg. These types of sentiment-based indicators are increasingly becoming available. Additionally, due to the overall improvement in data-mining techniques, these indicators should theoretically be more reliable than ever. Their study found that the NNAR that incorporated the Bloomberg-generated news and Twitter-based sentiment indicators had a higher quality of stock-price predictions. Due to the indicators being created and readily available by Bloomberg, no text/data-mining models had to be utilized by the authors. The resulting ease of incorporating news and Twitter indicators into NNs was equal to any other technical indicator. As shown in Table 1, LSTM stands out as one of the most popular NN techniques in the price prediction literature.