# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 2 Related Work
### AI Era Stock Predictions Solutions  
Future price prediction is a complicated task in machine learning field as it is not clear if asset prices embody investors' behavior or stock prices time series are random walks. In the literature, various AI-based and state-of-the-art studies attempted to predict future prices [3; 4; 5]. A variant of Neuro-fuzzy system which employs both recurrent network and momentum is utilized for the prediction of four assets of Dhaka stock exchange [3]. Various machine and deep learning methods are used, such as K-Nearest Neighbor regression, Generalized Regression Neural Networks (GRNN), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Gaussian Processes and Long-short term memory (LSTM) to predict future values of 111 stock market assets [4]. The employment of Wavenet model to predict the S&P 500 future prices achieves lower Mean Absolute Scaled Error (MASE) compared with the aforementioned methods [5]. The effectiveness of Wavenet model is the main motivation to be utilized in the prediction of assets' future values in platform. With the emergence of AI-based techniques, deep learning neural networks became the dominant approach to tackle natural language and text processing. These deep learning approaches include many networks types such as Fuzzy Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and more recently transformer-based neural networks [15; 16; 17]. Numerous studies have been conducted aiming to leverage the great amounts of available data to extract more accurate and exploitable stock market information. Sentiment analysis and machine learning principles were applied to find the correlation between \"public sentiment\" and \"market sentiment\"; firstly, Twitter data were used to predict public mood and subsequently stock market movement predictions based on the predicted mood and the previous days' DJIA values were performed [15]. In a later work, sentiments were derived from Yahoo! Finance Message Board texts related to a specific topic of a company (product, service, dividend, etc), employing thus the \"topic sentiment\" and not the \"public sentiment\". The sentiments were incorporated into the stock prediction model along with historical prices [18]. Sentiment analysis and supervised machine learning principles were also applied to the tweets about a company and the correlation between stock market movements of the company and the sentiments in tweets was analyzed, concluding that a strong correlation exists between the rise and fall in stock prices with the sentiments expressed in the tweets [19]. In [6], the proposed prediction model was based on sentiment analysis of financial news and historical stock market prices. In the scope of the specification of trading strategies, taking into account the decaying effect of news sentiment, thus deriving the impact of aggregated news events for a given asset, was also proposed [20]. Trading strategies that utilize textual news along with the historic prices in the form of the momentum to obtain profits were designed by [PERSON] and [PERSON] [7]. Many works have been published concerning financial forecasting and portfolio management platforms utilizing AI technologies. Researchers are addressing the challenges arising from the application of AI technologies in quantitative investment. In particular, they have designed and developed an AI-oriented Quantitative Investment Platform, called Qlib. It is based on attempts to build a research workflow for quantitative researchers, utilizing the potential of AI technologies. Their platform integrates a high-performance data infrastructure with machine learning tools dedicated for quantitative investment scenarios. It provides flexible research workflow modules with pre-defined implementation choices regarding investment, as well as a database dedicated to scientific processing of financial data, typical for tasks in quantitative investment research.