Supporting Investment Decisions Based on Cognitive Technology

  • Piotr Oleksyk
  • Marcin HernesEmail author
  • Bartłomiej Nita
  • Helena Dudycz
  • Agata Kozina
  • Jakub Janus
Part of the Studies in Computational Intelligence book series (SCI, volume 887)


The aim of the chapter is to present the conception of the prediction module for supporting investment decisions based on the cognitive technology. Managers must make investment decisions that are very important for a company’s development. Decisions are made based on scenario analysis. Preparation of these scenarios is based on data and information from sources from the company and its environment. An important source of data are social media, which can contain valuable information, but also rubbish. The problem is to extract from a huge amount of data in social media information which is valuable and useful for a company in forecasting future situations. The chapter contains conclusions from the study on the use of cognitive technology in obtaining valuable information from Twitter to forecast investment scenarios. We have discussed the proposal of the prognostic investment decision supporting module and present a case study analysis that refers to the use of cognitive technologies in the this module to support investment decision making by managers in small and medium-sized enterprises. The contribution of this chapter is a proposal of the prediction module for supporting investment decisions based on the cognitive technology.


Cognitive technology Prognostic decisions Investment forecasting Cognitive agents Social media exploration 



The project is financed by the Ministry of Science and Higher Education in Poland under the program “Regional Initiative of Excellence” 2019—2022 project number 015/RID/2018/19 total funding amount 10 721 040,00 PLN.


  1. 1.
    Rekik, A., Jamoussi, S., & Hamadou, A. B. (2019). Violent vocabulary extraction methodology: Application to the radicalism detection on social media. In N. Nguyen, R. Chbeir, E. Exposito, P. Aniorté, & B. Trawiński (Eds.), Lecture Notes in Computer Science: Computational Collective Intelligence. ICCCI 2019 (Vol. 11684). Cham: Springer.Google Scholar
  2. 2.
    Guenther, C., Johan, S., & Schweizer, D. (2018). Is the crowd sensitive to distance?—How investment decisions differ by investor type. Small Business Economics, 50(2), 289–305.CrossRefGoogle Scholar
  3. 3.
    Gennaioli, N., Ma, Y., & Shleifer, A. (2016). Expectations and investment. NBER Macroeconomics Annual, 30(1), 379–431.CrossRefGoogle Scholar
  4. 4.
    Go, R. S., Munoz, F. D., & Watson, J. P. (2016). Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards. Applied Energy, 183, 902–913.CrossRefGoogle Scholar
  5. 5.
    Muntermann, J. (2007). Event-driven mobile financial information services: Design of an intraday decision support system. Springer Science & Business Media.Google Scholar
  6. 6.
    Kim, M., & Han, S. (2018). Cognitive social network analysis for supporting the reliable decision-making process. The Journal of Supercomputing, 74(8), 3654–3665.CrossRefGoogle Scholar
  7. 7.
    Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872–1888.CrossRefGoogle Scholar
  8. 8.
    Elliot, B., & Elliot, J. (2006). Financial accounting and reporting (10th ed.). Essex: Pearson Education Limited.Google Scholar
  9. 9.
    Tjia, J. S. (2004). Building financial models. A guide to creating and interpreting financial statements. New York: McGraw-Hill.Google Scholar
  10. 10.
    Jaworski, J. (2012). Informacja finansowa w zarządzaniu małym przedsiębiorstwem Potrzeby - źródła – wykorzystanie. Warsaw: CeDeWu.Google Scholar
  11. 11.
    Narasimhan, J., Kim, J., Krische, S., & Lee, Ch. (2004). Analyzing the analysts: When do recommendations add value? Journal of Finance, 59, 1083–1124.CrossRefGoogle Scholar
  12. 12.
    Carnot, N., Koen, V., & Tissot, B. (2011). Economic forecasting and policy (2nd ed.). Basingstoke: Palgrave Macmillan.CrossRefGoogle Scholar
  13. 13.
    Arias, M., Arratia, A, & Xuriguera, R. (2013). Forecasting with twitter data. ACM Transactions on Intelligent Systems and Technology, 5(1), 1–24 (2013).Google Scholar
  14. 14.
    Miasato, V. A., Gonçalves, B., Costa, B. R., & De Carvalho, Silva J. E. (2017). Distributed averaged perceptron for Brazilian Portuguese part-of-speech tagging. In A. Paradisi, A. Godoy Souza Mello, F. F. Lira, & F. R. Carvalho (Eds.), Cognitive technologies. Telecommunications and information technology. Cham: Springer.Google Scholar
  15. 15.
    Pedrycz, W., & Homenda, W. (2012). From fuzzy cognitive maps to granular cognitive maps. In N. T. Nguyen, K. Hoang, & P. Jȩdrzejowicz (Eds.), Lecture Notes in Computer Science: Computational Collective Intelligence. Technologies and Applications. ICCCI 2012 (Vol. 7653). Berlin, Heidelberg: Springer.Google Scholar
  16. 16.
    Jutte, B., & van der Wal, C. N. (2016). Cognitive modelling of emotion contagion in a crowd of soccer supporter agents. In N. T. Nguyen, L. Iliadis, Y. Manolopoulos, & B. Trawiński (Eds.), Lecture Notes in Computer Science: Computational Collective Intelligence. ICCCI 2016 (Vol. 9875). Springer, Cham.Google Scholar
  17. 17.
  18. 18.
    Pilipczuk, O., & Eidenzon, D. (2013). The application of cognitive computer graphics to economic data exploration. Journal of Automation Mobile Robotics and Intelligent Systems, 7.Google Scholar
  19. 19.
    Owoc, M., Hauke, K., & Weichbroth, P. (2015). Knowledge-grid modelling for academic purposes. In IFIP International Workshop on Artificial Intelligence for Knowledge Management (1–14). Cham: Springer.Google Scholar
  20. 20.
    Hernes, M. (2014). A cognitive integrated management support system for enterprises. In D. Hwang, J. J. Jung, & N. T. Nguyen (Eds.), Lecture Notes in Computer Science: Computational Collective Intelligence. Technologies and Applications. ICCCI 2014 (Vol. 8733). Cham: Springer.Google Scholar
  21. 21.
    Franklin, S., Madlb, T., & Strain, S. (2016) A LIDA cognitive model tutorial, W: Biologically Inspired Cognitive Architectures, Biologically Inspired Cognitive Architectures.Google Scholar
  22. 22.
    Hernes, M. (2015). Information Extraction methods for text documents in a cognitive integrated management information system. In 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF) (pp. 287–292). IEEE.Google Scholar
  23. 23.
    Hernes, M., & Bytniewski, A. (2017). Knowledge integration in a manufacturing planning module of a cognitive integrated management information system. In N. Nguyen, G. Papadopoulos, P. Jędrzejowicz, B. Trawiński, & G. Vossen (Eds.), Lecture Notes in Computer Science: Computational Collective Intelligence. ICCCI (Vol. 10448). Cham: Springer.Google Scholar
  24. 24.
    Hernes, M., Maleszka, M., Nguyen, N. T., & Bytniewski, A. (2015, September). The automatic summarization of text documents in the Cognitive Integrated Management Information System. In 2015 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 1387–1396). IEEE.Google Scholar
  25. 25.
    Bytniewski, A., Chojnacka-Komorowska, A., Hernes, M., & Matouk, K. (2015). The implementation of the perceptual memory of cognitive agents in integrated management information system. In New Trends in Intelligent Information and Database Systems (pp. 281–290). Cham: Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Piotr Oleksyk
    • 1
  • Marcin Hernes
    • 1
    Email author
  • Bartłomiej Nita
    • 1
  • Helena Dudycz
    • 1
  • Agata Kozina
    • 1
  • Jakub Janus
    • 1
  1. 1.Wroclaw University of Economics and BusinessWrocławPoland

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