Information System Supporting Decision-making Processes for Forming of Securities Portfolio

: pp. 39 - 55
Lviv Polytechnic National University, Ukraine
Lviv Polytechnic National University
Lviv Polytechnic National University, Ukraine
Lviv National University of Veterinary Medicine and Biotechnology
Institute for Applied Problems of Mechanics and Mathematics

Due to large-scale changes in the economy in the world and in Ukraine in particular, there has recently been a significant increase in interest in the problems of investment theory. An example is the intensification in recent years of the purchase of shares of large international companies and cryptocurrencies and, according to the rapid growth of their values. It is known that as a special case, the theory of investment considers the task of optimizing investment portfolios.

It is established that the issue of decision-making on the formation and optimization of the investment portfolio is in the field of attention of both large investment companies and private investors, because choosing among possible alternatives for allocating investments within the financial assets market, the investor will get different results. It is accepted that the optimal distribution of the investment portfolio should provide the best return while maintaining the least risk, and the result should be understood as the amount of income received during the period of ownership of the investment portfolio.

An information system to support the decision-making of the securities portfolio has been developed, which allows potential investors to independently on assess the effectiveness of the investment portfolio by comparing the growth dynamics of shares available on the financial market. It is known that most of the information encountered by the investor is in tabular format, and according to the methodology of scientific knowledge, people are more receptive to visualized ways of presenting information. The newly created information system uses a visualization process that presents available tabulated information in a structured form of diagrams, graphs, charts.

  1.  H. Markowitz, Portfolio selection, Journal of Finance 7(1) (1952) 77-91,
  2. Kuzmin O., Alekseev I., Kolisnyk M. Problems of financial and credit regulation of innovative development of production and economic structures: monograph . Lviv Polytechnic National University Publishing House, 2007. - 152 p.
  3. J. Lu, D. RuanandG. Zhang (eds.), E-ServiceIntelligence: Methodologies, TechnologiesandApplications (Springer-Verlag, Berlin, Heidelberg, 2007),
  4. T. Stoilov, How to integrate complex optimal data processing in information services ininternet, in Proc. 20th Int. Conf. Computer Systems and Technologies, ACM DigitalLibrary, 2019, pp. 19-30,
  5. V. D. Ta, C. M. Liu and D. A. Tadesse, Portfolio optimization-based stock predictionusing long-short term memory network in quantitative trading, Applied Sciences 10(2020) 437,
  6. Kalnyi, S. V. and Vysotskyi, V. A. (2019), "Management formation of investment portfolio enterprises in Ukraine", Efektyvna ekonomika, [Online], vol. 3, available at:
  7. Medynska, Tetyana V., Rushchyshyn, Nadiia M., and Nikonenko, Uliana M. (2020) "Tax Regulation of Investment Activity of Ukrainian Banks.", Business Inform 11:316-324.
  8. M. García-Galicia, A. A. Carsteanu and J. B. Clempner, Continuous-time mean varianceportfolio with transaction costs: A proximal approach involving time penalization, International Journal of General Systems 48(2) (2019) 91-111, https://doi: 10.1080/03081079.2018. 1522306.
  9.  X. Huang and X. Wang, Portfolio investment with options based on uncertainty theory,International Journal of Information Technology & Decision Making 18 (2019) 929-952.
  10. E. Allaj, The Black-Litterman model and views from a reverse optimization procedure:An out-of-sample performance evaluation, Computational Management Science 17(2020) 465-492.
  11. A. Palczewski and J. Palczewski, Black-Litterman model for continuous distributions,European Journal of Operational Research 273(2) (2019) 708-720, https://www.sciencedirect.comscience/article/pii/S0377221718306933.
  12. A. Rutkowska and M. Bartkowiak, Exertion approach to vague information in portfolioselection problem with many views, 2019 Conf. Int. Fuzzy Systems Association and theEuropean Society for Fuzzy Logic and Technology (EUSFLAT 2019) (Atlantis Press,Paris, France, 2019), pp. 142- 49, https://www.atlantis-press.comproceedings/eus°at-19/125914792
  13. F. Wen, L. Xu, G. Ouyang and G. Kou, Retail investor attention and stock price crashrisk: Evidence from China, Journal of International Review of Financial Analysis 65(2019) 1-15,
  14. G. Kou, Ö. Akdeniz, H. Dinçer and S. Yüksel, Fintech investments in European banks: Ahybrid IT2 fuzzy multidimensional decision-making approach, Journal of Financial Innovation 7(39) (2021) 1-28,
  15. Wes McKinney. Python for Data Analysis / Wes McKinney, Julie Steele and Meghan Blanchette. - United States of America: O'Reilly, 2018. - 470 с.
  16. Jake VanderPlas. Python Data Science Handbook. Essential Tools for Working with Data / Jake VanderPlas. - United States of America: O'Reilly Media, Inc., 1005. Gravenstein Highway North, Sebastopol, CA 95472., 2017. - 548 с
  17. Yaroshko S., Manziy O. Financial mathematics. Part 1. Lviv, ZUKC Publishing House, 2021. - 209 p.
  18. Dickey, D. A.; Fuller, W. A. (1979).Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74 (366): 427-431. JSTOR2286348. https://doi:10.1080/01621459.1979.10482531