An AI-Enabled Stock Prediction Platform Combining News and Social Sensing with Financial Statements

Traianos-Ioannis Theodorou, Alexandros Zamichos, Michalis Skoumperdis, Anna Kougioumtzidou, Kalliopi Tsolaki, Dimitris Papadopoulos, Thanasis Patsios, George Papanikolaou, Athanasios Konstantinidis, Anastasios Drosou and Dimitrios Tzovaras
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Traianos-Ioannis Theodorou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Alexandros Zamichos: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Michalis Skoumperdis: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Anna Kougioumtzidou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Kalliopi Tsolaki: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimitris Papadopoulos: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Thanasis Patsios: Media2Day Publishing S.A., 15232 Athens, Greece
George Papanikolaou: Media2Day Publishing S.A., 15232 Athens, Greece
Athanasios Konstantinidis: Department of Electrical Engineering, Imperial College London, London SW7 2AZ, UK
Anastasios Drosou: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
Dimitrios Tzovaras: Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece

Future Internet, 2021, vol. 13, issue 6, 1-22

Abstract: In recent years, the area of financial forecasting has attracted high interest due to the emergence of huge data volumes (big data) and the advent of more powerful modeling techniques such as deep learning. To generate the financial forecasts, systems are developed that combine methods from various scientific fields, such as information retrieval, natural language processing and deep learning. In this paper, we present ASPENDYS, a supportive platform for investors that combines various methods from the aforementioned scientific fields aiming to facilitate the management and the decision making of investment actions through personalized recommendations. To accomplish that, the system takes into account both financial data and textual data from news websites and the social networks Twitter and Stocktwits. The financial data are processed using methods of technical analysis and machine learning, while the textual data are analyzed regarding their reliability and then their sentiments towards an investment. As an outcome, investment signals are generated based on the financial data analysis and the sensing of the general sentiment towards a certain investment and are finally recommended to the investors.

Keywords: Web 3.0; machine learning; sentiment analysis; portfolio optimization; portfolio management; media industry; social media; model-based trading (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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