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Using Bayesian Networks to Estimate the Effectiveness of Innovative Projects

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2021)

Abstract

The paper proposes an application of Bayesian methodology to analyze the attachment effectiveness in the national economy. The methods for creation the BNs structure, their parametric learning, validation, and scenario analysis are examined. The research results show that at the highest level of capital investments, the financial activity result will be 48% more active, while the net profit indicator will rise by 8%. To make the profitability equal 100%, it is desirable for us to decrease the payback term by 15% and increase the level of net profit by 9%. This study can be useful for investors, managers, economists, financiers and other investment professionals.

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References

  1. Ukraine 2019–2020: broad opportunities, contradictory results (2020). https://razumkov.org.ua/uploads/other/2020-PIDSUMKI-ENG.pdf

  2. Ukraine: Investment guide. dlf attorneys-at-law (2021). https://dlf.ua/en/ukraine-investment-guide/

  3. de Andrade, B.B., Souza, G.S.: The EM algorithm for standard stochastic frontier models. Pesquisa Operacional 39(3) (2019). https://doi.org/10.1590/0101-7438.2019.039.03.0361

  4. Bonello, A., Grima, S., Spiteri, J.: Understanding the investor: A maltese study of risk and behavior in financial investment decisions (vol. first edition). bingley, uk: Emerald publishing limited (2019). http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993147

  5. Cakici, N., Zaremba, F.: Size, value, profitability, and investment effects in international stock returns: are they really there? Jo. Investing Apr (1) (2021). https://doi.org/10.3905/joi.2021.1.176

  6. Cornwall, J.R., Vang, D.O., Hartman, J.M.: Entrepreneurial financial management: an applied approach (2019). http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2237944

  7. Dimitras, A.I., Papadakis, S., Garefalakis, A.: Evaluation of empirical attributes for credit risk forecasting from numerical data. Invest. Manage. Financ. Innov. 14(1), 9–18 (2017). https://doi.org/10.21511/imfi.14(1).2017.01

  8. Dogru, T., Upneja, A.: The implications of investment-cash flow sensitivities for franchising firms: theory and evidence from the restaurant industry. Cornell Hospitality Q. 60(1), 77–91 (2019). https://doi.org/10.1177/1938965518783167

    Article  Google Scholar 

  9. Dugar, A., Pozharny, J.: Equity investing in the age of intangibles. Financ. Anal. J. 77(2), 21–42 (2021). https://doi.org/10.1080/0015198X.2021.1874726

    Article  Google Scholar 

  10. Garde, A., Zrilic, J.: International investment law and non-communicable diseases prevention. J. World Investment Trade 21(5), 649–673 (2020). https://doi.org/10.1163/22119000-12340190

    Article  Google Scholar 

  11. Gilbert, E., Meiklejohne, L.: A comparative analysis of risk measures: a portfolio optimisation approach. Invest. Anal. J. 48(3), 223–239 (2019). https://doi.org/10.1080/10293523.2019.16431282

    Article  Google Scholar 

  12. Harford, J., Kecskes, A., Mansi, S.: Do long-term investors improve corporate decision making? J. Corp. Finan. 50, 424–452 (2017). https://doi.org/10.1016/j.jcorpfin.2017.09.022

    Article  Google Scholar 

  13. Jayaraman, S., Shuang, Wu., J. : Should i stay or should i grow? using voluntary disclosure to elicit market feedback. Rev. Financ. Studi. 33(8), 3854–3888 (2020). https://doi.org/10.1093/rfs/hhz132

  14. Kyzym, M.O., Doronina, M.: Economic Science in Ukraine: challenges, problems and ways of their solving. Problems Econ. (3), 156–163 (2019). https://doi.org/10.32983/2222-0712-2019-3-156-163

  15. Lekar, S., Shumeiko, D., Lagodiienko, V. Andi Nemchenko, V.: Construction of bayesian networks in public administration of the economy. Int. J. Civil Eng. Technol. 10(3), 2537–2542 (2019). http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=03

  16. Niloy, N., Navid, M.: Naïve bayesian classifier and classification trees for the predictive accuracy of probability of default credit card clients. Am. J. Data Mining Knowl. Discov. 3(1), 1–12 (2018). https://doi.org/10.11648/j.ajdmkd.20180301.11

  17. Park, S.Y., Schrand, C.M., Zhou, F.: Management Forecasts and Competition for Limited Investor Resources (2019). https://ssrn.com/abstract=3357603

  18. Paskaramoorthy, A.B., Gebbie, T.J., van Zyl, T.L.: A framework for online investment decisions. Invest. Anal. J. 49(3), 215–231 (2020). https://doi.org/10.1080/10293523.2020.1806460

    Article  Google Scholar 

  19. Poonam, M., Harpreet, A.: Analytical study of capital budgeting techniques (Only automobiles companies). Asian J. Multidimension. Res. 8(6), 150–162 (2019). https://doi.org/10.5958/2278-4853.2019.00226.X

    Article  Google Scholar 

  20. Lopez de Prado, M., Vince, R., Zhu, Q.: Optimal risk budgeting under a finite investment horizon. Risks 7(3) (2019). https://doi.org/10.3390/risks7030086

  21. Pärssinen, M., Wahlroos, M., Manner, J., Syri, S.: Waste heat from data centers: an investment analysis (2019). http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.73BBA477

  22. Rösch, D.M., Subrahmanyam, A., van Dijk, M.A.: Investor short-termism and real investment. J. Financ. Markets (2021). https://doi.org/10.1016/j.finmar.2021.100645

    Article  Google Scholar 

  23. Ryu, D., Ryu, D., Yang, H.: Investor sentiment, market competition, and financial crisis: evidence from the korean stock market. Emerg. Mark. Financ. Trade 58(81), 1804–1816 (2020)

    Article  Google Scholar 

  24. Shah, A.: Uncertain risk parity. J. Investment Strat. 10(3) (2021). http://doi.org/10.21314/JOIS.2021.009

  25. Shi, Y., Liu, H.: EM-detwin: a program for resolving indexing ambiguity in serial crystallography using the expectation-maximization algorithm. Crystals 10(7) (2020). https://doi.org/10.3390/cryst10070588

  26. de Souza, P., Rogerio, M., Lunkes, R., Bornia, C.: Capital budgeting: a systematic review of the literature (2020) https://doi.org/10.1590/0103-6513.20190020

  27. Sven, O.S., Michniuk, A., Heupel, T.: Beyond budgeting - a fair alternative for management control? - examining the relationships between beyond budgeting and organizational justice perceptions. Stud. Bus. Econ. 2(160) (2019). https://doi.org/10.2478/sbe-2019-0032

  28. Tian, G.L., Ju, D., Chuen, Y.K., C., Z.: New expectation-maximization-type algorithms via stochastic representation for the analysis of truncated normal data with applications in biomedicine. Stat. Methods Med. Res. 27(8), 2459–2477 (2018). https://doi.org/10.1177/0962280216681598

  29. Yang, Y., Mémin, E.: Estimation of physical parameters under location uncertainty using an ensemble-expectation-maximization algorithms. Q. J. R. Meteorol. Soc. 145, 418–433 (2019). https://doi.org/10.1590/0101-7438.2019.039.03.0361

    Article  Google Scholar 

  30. Ye, M., Zheng, M., Zhu, W.: Price Discreteness and Investment to Price Sensitivity. Available at SSRN (2019)

    Google Scholar 

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Correspondence to Mariia Voronenko .

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Naumov, O. et al. (2022). Using Bayesian Networks to Estimate the Effectiveness of Innovative Projects. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_50

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