# An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements
## 6 Conclusions  
As mentioned in Section 2, there are several works that deal with stock prediction; most of them are based on stocks' historical values, without taking into consideration information that may affect the stock prices, such as social or political events. While referring to RQ1, the system takes into account both financial data (e.g., stocks' closing values) and textual data retrieved from either reputable news websites or social networks (i.e., Twitter and Stocktwits). For processing the financial data, different methods of technical analysis and machine learning [5; 50] are utilized. The outcome of the analysis is the generation of investment signals for buying or selling stocks. Because the textual data are retrieved from various sources and in large quantities, they are firstly correlated through text analysis with the available financial symbols of the platform, and then filtered based on their reliability. Then, only the reliable data are analyzed for extracting their sentiment. By aggregating and recording the daily sentiment of each stock, time series with the sentiment of the stocks are created. By combining these time series and the price indices of the stocks, the system is able to recommend additional signals based on the general sentiment for the stocks.  
Additionally, regarding RQ2, the ASPENDYS platform can serve users as a mean for managing and monitoring their investments. After being registered on the platform, the users can define a set of assets that they are interested in investing into or monitoring their course. This set of assets forms the user's portfolio. The system is capable of predicting investment signals, related to a portfolio, and alerting the end users when necessary. Finally, the platform provides, through dedicated tools, the complete analysis of the user's investment movements, presenting historical data with their investments, their portfolio profit within a certain period and the course of each asset independently.  
However, there are some limitations in the proposed platform. More specifically, the fine tuning of sentiment analysis models is a difficult task because there is a lack of open source annotated datasets that contain articles and tweets in the field of economics. Therefore, as mentioned in Section 3.7.1, we have to use datasets from another sector that is a sub-optimal option. From a technical point of view, the specific implementation has not been tested in terms of scalability; however, a future plan is the deployment of the ASPENDYS platform in a business infrastructure where these kind of tests can be done. Moreover, another future plan for the proposed platform could involve the implementation and the adaptation of the algorithms and models to the cryptocurrency industry. In addition, the part of the sentiment analysis could be enhanced in the future by integrating lexicons such as the VADER (Valence Aware Dictionary for sEntiment Reasoning) lexicon [61].  
Conceptualization, A.D., A.K. ([PERSON]) and G.P.; methodology, T.-I.T., A.Z. and A.K. ([PERSON]); software, T.-I.T., A.Z. and T.P.; validation, M.S., A.K. ([PERSON]), K.T. and D.P.; formal analysis, G.P.; investigation, M.S. and A.K. ([PERSON]); resources, K.T. and D.P.; data curation, T.-I.T. and A.Z.; writing--original draft preparation, T.-I.T. and A.Z.; writing--review and editing, T.-I.T. and A.Z.; visualization, T.-I.T. and A.Z.; supervision, A.D.; project administration, D.T.; and funding acquisition, A.D. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the ASPENDYS project co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code:T1 EDK-02264).  
All data that are not subjected to institutional restrictions are available through the links provided within the manuscript.  
The authors declare no conflict of interest.