# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 2 Related Work
### Before AI Stock Predictions Solutions  
The first research attempts in the field of stock prediction values are based either on econometric models or on technical indexes. The most popular statistical models, which are widely utilized in econometrics for future prices prediction, are ARIMA and ARMA [2]. Two popular indexes are utilized for stock assets price prediction: Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI). The Volatility of Average Convergence Divergence [8] is found to be effective for the prediction of a specific asset [8]. Moreover, both indexes are proved to be more efficient in stock market trends prediction than SELL and BUY strategies [9]. Nowadays, state-of-the-art methods are based mainly on machine learning and deep learning algorithms that have been found that they achieve better results in stock market prediction.  
Moreover, the stock prediction can be enhanced by, or even solely based on, market information that can be derived from the news spread and circulated in the societal sphere. Numerous studies have been conducted with the aim to capitalize on this available information, particularly since it can be accessed conveniently via digital media. Thus, the field of sentiment analysis, and the closely related opinion-mining field, emerges. The subject of the sentiment analysis is the automatic extraction of sentiments, evaluations, attitudes, emotions and opinion. The emergence and growth of these field takes place in tandem with the rise of the social media on the web, e.g., reviews, forum discussions, blogs, micro-blogs, Twitter and social networks, and is clearly related to the fact that a huge volume of data containing opinions is recorded and available. Sentiment analysis has evolved to be one of the most active research areas in natural language processing. Data mining, web mining and text mining also employ sentiment analysis. Apart from being a computer science field, it has expanded to management sciences and social sciences due to its importance to business and society and sentiment analysis systems have found their applications in almost every business and social domain [10; 11]. The stock market domain of course could not constitute an exception.  
Two main approaches to the problem of extracting sentiment automatically, prior to the emergence of AI, can be identified: the lexicon-based approach for automatic sentiment analysis and the statistical classifier approach. The lexicon-based approach involves calculating a sentiment for a document from the semantic orientation of words or phrases in the document. The lexicons contain words or multiword terms tagged as positive, negative or neutral (sometimes with a value reflecting the sentiment strength or intensity). Examples of such lexicons include the Hu & Liu Opinion Lexicon, the SentiWordNet Lexicon, the Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, the General Inquirer, the National Research Council Canada (NRC) Word-Sentiment Association Lexicon and the Semantic Orientation Calculator (SO-CAL). The statistical text classification approach involves building classifiers from labeled instances of texts or sentences, essentially a supervised classification task [11; 12; 13]. Most notably, two lexicon-based mood tracking tools, OpinionFinder that measures positive versus negative mood and Google-Profile of Mood States that measures mood in terms of six dimensions, were used in a context related to the scope of the present work to investigate the hypothesis that public mood states are predictive of changes in DJIA closing values [14].  
Issues regarding lexicon-based methods include the fact that the dictionaries are deemed to be unreliable, as they are either built automatically or hand-ranked by humans [11], as well as the need for the lexicons to be domain specific. Moreover, given a sufficiently large training corpus, a machine learning model is expected to outperform a lexicon-based model [13].