# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 1 Introduction  
The financial services sector provides services in the field of finance to individuals and corporations. This part of the economy consists of a variety of financial firms including finance companies, banks, investment houses and insurance companies. The industry of financial services is probably the most important sector of the economy, leading the world in terms of earnings and equity market capitalization. Individuals may access financial markets such as stocks and bonds through investment services. Brokers that are either individuals or online services facilitate the buying and selling of securities. Financial advisors are responsible for the assets management as well as the trades of the portfolio assets. Additionally, quant fund is the latest trend of financial advice and portfolio management, with fully automated algorithmic portfolio optimization and trade executions. Quant funds refer to investment funds whose securities are chosen based on the quantitative analysis of numerical data. The analysis is performed by software programs which utilize advanced mathematical and Artificial Intelligence (AI) models. These models deal with several challenging tasks in the financial services sector, such as the accurate prediction of the stock prices. In contrast with traditional methods, quant funds rely on algorithmic or systematically programmed investment strategies. Thus, theyare not human-oriented and their decisions rely only on data analysis. In this way, they can be very useful tools for supporting investors in their investment's decision making. Regarding the stock prices prediction, many methods have been proposed by researchers since the beginning of the stock market [1; 2; 3; 4; 5; 6; 7]. Moreover, during the last years with the advent of big data and machine learning in Web 3.0 technologies, financial forecasting and model-based trading have gained growing interest. Web 3.0 is the third generation of Internet services for websites and applications that focus on using a machine-based understanding of data to provide a data-driven and semantic web. While the traditional methods for financial forecasting were based on the analysis of the stock prices of the past, nowadays, the efforts consider a variety of data. More specifically, the huge amount of available data can be found on social media and from the media industry (articles and posts) that may be related with significant societal and political events. These events, as well as the extracted information from their sentiment analysis, may reflect the stock prices and trends of certain assets. Additionally, investors tend to follow the general sentiment on a specific topic, something that affects their final decisions. However, the use of machine learning algorithms and the large amount of structured data that is produced on a daily basis can give them a more clear and personalized view of the sentiment and trend of their portfolio assets, assisting their final decisions. In this work, we propose ASPENDYS, an interactive web-platform that offers supportive information to investors. The primary aim of the ASPENDYS platform is to assist investors in the decision making related with stock investments, taking into consideration multiple sources and a large amount of data by using state of the art prediction algorithms. Initially, in Section 2, we review the related literature and defined the research gap and question that our work needs to cover and answer respectively. Then, in Section 3, we describe the methodology, architecture, data, components and functionalities developed in the ASPENDYS project. More specifically, the research methodology is described in Section 3.1 and the architecture of the platform is analyzed in Section 3.2. In Section 3.3, we describe all the data used by the algorithms of the components. Section 3.4 describes the algorithms that were used for the extraction of both articles sentiments and the asset sentiments.