## 1 Introduction
Despite the great amount of data, advances in natural language processing and data mining allow for effective computerized representation of unstructured document collections, analysis for pattern extraction and discovery of relationships between documents and time-stamped data streams of stock market quotes. Not only news can play an important role towards influencing stock market trends. Public attitude states or sentiment, as expressed through various means that promote inter-connectivity, such as Web 2.0 platforms, may also play a similarly important role. Targeted research in the domain of psychology has proven that emotions in addition to information have a direct impact on human decision-making [3]. Therefore, a logical assumption would be for someone to consider opinions originating from social media as an additional factor that could also affect stock market values. In this work, the main objective is to study and model the impact of technical analysis, news articles and public opinions for the task of predicting the closing price of the stocks. The importance of this study lies in the fact that technical analysis contains the event and not the cause of the change, while textual data may be interpreted as a cause. Despite the fact that there are several attempts that have incorporated technical analysis data with textual information, we are motivated by the fact that all of these works take a sliding window of time into consideration, i.e., they only focus on the characteristics that are very close to the event being examined in each time period. Therefore, we propose a totally different approach, which is based on the potential periodicity of events, that could further improve forecasting performance. The paper uses time series analysis techniques such as Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) to study the existence of a relation between price data and textual information, either from news or social media. In order to accomplish this first goal, pattern matching techniques from time series data are incorporated. Upon identification of such patterns and their periodicity, the second goal is the main objective described above, namely creating a forecasting model that exploits the previously discovered patterns in order to augment the forecasting accuracy. Results obtained from the experimental phase are promising. The performance of the classifier shows clear signs of improvement and robustness within the time periods where patterns between stock price and the textual information have been identified, compared to the periods where patterns did not exist. Certainly, as it is tedious for a human investor to read all daily news and public reactions concerning a company and other financial information, a prediction system that could analyze such textual resources and find relationships with price movement at future time windows is beneficial. The paper is structured as follows: Section 2 provides an overview of the literature concerning stock market prediction from textual and financial resources using data mining techniques. Section 3 gives some theoretical background, while in Section 4, the methodology of the approach is presented. Section 5 presents the experimental setting and the obtained results. In Section 6, the shortcomings of the paper are presented, and Section 7 concludes the paper.