## 1 Introduction  
One of the most challenging tasks faced by researchers in modeling dynamic systems is the creation of accurate stock market forecast models. Dynamic systems are governed by complexity. Volatility is another characteristic of market dynamics. As a result, much controversy has been caused as to whether such a forecasting method could exist. Therefore, two main strategies have been encapsulated by analysts, namely the fundamental and the technical strategy [1]. The former states that the stock market change of prices derives from a security's relative data. In a fundamentalist trading philosophy, the price of a security can be determined through the nuts and bolts of financial numbers. These numbers are derived from the overall economy, the particular industry's sector or, most typically, from the company dynamics. Parameters such as inflation, joblessness, return on equity (ROE), debt levels and individual price to earnings (PE) ratios have been identified as components that aid towards determining the price of a stock. On the other axis, that of technical analysis, research is based on the belief that market timing is the key concept. Technicians utilize historical data in the form of charts and figures in order to identify trends in price. These strategists assume that market timing is critical, and thus, opportunities can arise through the careful investigation of historical price and volume trends, comparing them against current prices. Technical analysts also support the claim that certain high/low psychological price barriers exist, such as support and resistance levels where opportunities may lurk. Furthermore, an additional assumption that is adopted is that price movements are not completely unsystematic. Nevertheless, according to a variety of researchers, the goal is not to question the predictability of financial time series data, but to discover a good model, able to cope with the dynamics of stock market. Even though many researchers adopt the aforementioned categorization between fundamentalist trading philosophy and technical analysis, we are of the opinion that good fundamental knowledge could be combined with patterns derived from technical analysis in an attempt to overcome issues such as asymmetric or erroneous information. Towards the latter path, stock market analysis utilizing sophisticated Information and Communication Technology (ICT) has gained a significant amount of attention. Over the past few years, there has been an increasing focus on the development of modeling systems, especially when the expected outcomes appear to yield significant profits to the investors' portfolios. In alignment with modern globalized economy and the expansion of social media platforms that allow for rapid exchange of information among users, the available resources are gradually becoming more plentiful, thus difficult to be analyzed by typical statistical tools. Consequently, financial experts emphasize the utilization of data mining methods, mainly due to the quantity and the increased rate by which data are being formed. Thus far, there has been a significant number of research papers that have focused on applying data mining methods solely upon past data from stock bond prices and other technical indicators. Nevertheless, throughout recent studies, prediction is also based on textual information, based on the logical assumption that the course of a stock price can be influenced by news articles, ranging from companies' releases and local politics to news of superpower economies [2]. However, gaining unrestricted electronic access to news data was not feasible earlier than 2000. Nowadays, news is easily accessible, insights on important information such as inside company data are fairly inexpensive and domain expert estimations emerge from a vast pool of economists, statisticians, journalists, etc., through the Internet.