# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 2 Related Work
### AI Era Stock Predictions Solutions
Finally, by including basic machine learning models with some hyper-parameter optimization tools, the authors reported that Qlib outperforms many of the existing solutions [21]. There are recommendation systems supporting investment decisions, which are based on extracting buy/sell signals retrieved from technical analyses and predictions of machine learning algorithms. Through the platform's interface, users are able to derive their own conclusions and manage their investment decisions by evaluating the analyzed information by the system. The authors' development was based on retrieving historical financial data regarding various financial products: stocks, funds, government bonds, certificates, etc. The predictions on the future behavior of a product were derived through machine learning regression algorithms, such as Random Forest, Gradient Boosting, MLP and KNNeighbors. The technical analysis factors were calculated by utilizing indicators such as Relative Strength Index (RSI), Stochastic Oscillator (STOCH), Simple Moving Average (SMA), Exponential Moving Average (EMA) and others. Finally, their platform provides representations of prices' time-series, tables and charts including financial products' open-high-low-close values that depend on the aforementioned analyses [22]. Some works integrate sentiment analysis with a SVM-based machine learning method for forecasting stock market trends [23]. Their contribution consists of considering the day-of-week effect to derive more reliable sentiment indexes. The financial anomaly day of-week effect [24], which indicates that the average return on Mondays is much lower than that on the other weekdays, influences the accuracy of the investor sentiment indexes. Researchers are using this effect by introducing an exponential function on past sentiment changes on weekends and then generalize to holidays, to adjust the sentiment indexes. To forecast stock market movement direction, they extract features from unstructured financial review textual documents from which the sentiment indexes are constructed. Lastly, a SVM model is employed to produce the stock price predictions by implementing five-fold cross validation and a rolling window approach. Some works employed sentiment analysis on Twitter posts in an attempt to produce social signals to be used along with historical market data, in order to predict cryptocurrency prices. They tackled the sentiment analysis task using VADER, and, along with features produced from market data, they trained MLP, SVM and RF algorithms [25]. There are works that used stock price historical data in combination with financial news articles' information to predict the prices of stocks. Testing the performance of ARIMA, Facebook Prophet and RNN models without textual information, as well as the performance of RNNs with additional textual information, they concluded that the latter performed better. More precisely, their results show that an RNN using price information combined with textual polarity calculated by the nltk Python library, in most cases performed better than the aforementioned models as well as than that of an RNN operating with price data along with the text as inputs [26].