Managing investment attractiveness of a company during a period of high market volatility based on forecasting expectations

Evaluation of efficiency of economic and financial activities is primarily based on the financial performance of a company. In the context of the joint-stock companies’ activities, special importance belongs to timely or premature assessment of financial prospects of the activities for increasing the profit and potential of the company. To achieve the goals, it is extremely important to objectively evaluate the elements of innovative strategy of the company considering both internal and external influence as well as the company’s unique circumstances. While assessing a specific situation it is essential to take into account both innovative environment and position and its innovative potential. Financial coefficients which allow analysis of performance are used as the assessment criteria for financial condition. The study in hand suggests creation of a high-precision model based on the variability of statistical forecasting techniques followed by a thorough assessment to identify factors that objectively influence the company’s investment attractiveness. At the initial stage the authors calculated 40 financial indicators of the company quarterly over a seven-year period, and 10 factors of the external environment which were used later for conducting the multicorrelation analysis to select the most correlating with the leading one. This was the share price represented by binary code, where 0 indicates a decrease and 1 indicates an increase. A combination of approaches such as regression analysis, Gaussian processes, cumulative perspective theory and the method of constructing vector measures allowed increasing the accuracy of the model from 89 to 96.7% and identify the basic indicators which could be useful in forecasting the investment attractiveness of the company such as the share of net working capital in assets, the level of real income of the population and return on capital employed.

1. Antunes F., Ribeiro B., Pereira F. Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing. 2017;60:831–843. https://doi.org/10.1016/j.asoc.2017.06.043

2. Daemi A., Kodamana H., Huanga B. Gaussian process modelling with Gaussian mixture likelihood. Journal of Process Control. 2019;81(С):209–220. https://doi.org/10.1016/j.jprocont.2019.06.007

3. Dierkes M., Erner C., Zeisberger S. Investment horizon and the attractiveness of investment strategies: A behavioral approach. Journal of Banking & Finance. 2010:34(5):1032–1046. https://doi.org/10.1016/j.jbankfin.2009.11.003

4. Marchewka A. Applying vector measure construction method (VMCM) to analyze change dynamics of investment attractiveness of the region. Procedia Computer Science. 2021;192(8):3252–3261. https://doi.org/10.1016/j.procs.2021.09.098

5. Godlewska-Majkowska H., Komor A., Turek D., Zarębski P., Czernecki M., Typa M. Regional Investment Attractiveness 2016. Report prepared for the Polish Information and Foreign Investment Agency at the Institute of Enterprise, Warsaw School of Economics, Warsaw. December 2016. URL: https://readgur.com/doc/3053257/regional-investment-attractiveness-2016-opole (дата обращения: 15.12.2023).

6. Nermend K. Metody analizy wielokryterialnej i wielowymiarowej we wspomaganiu decyzji. Warszawa: Wydawnictwo Naukowe. PWN; 2017. 365 p.

7. Marchewka A. Applying TOPSIS to evaluate labour market resources as a factor of regional investment attractiveness. Procedia Computer Science. 2022;207:3122–3129. https://doi.org/10.1016/j.procs.2022.09.370

8. Pisică D., Dammers R., Boersma E., Volovici V. Tenets of good practice in regression analysis. A Brief Tutorial. World Neurosurgery. 2022;161:230–239.e6. https://doi.org/10.1016/j.wneu.2022.02.112

9. Rolik Y.A. A complex approach to evaluating the innovation strategy of a company to determine its investment attractiveness. Procedia – Social and Behavioral Sciences. 2013;99:562–571. https://doi.org/10.1016/j.sbspro.2013.10.526

10. Пахомов В.А. Факторы инвестиционной привлекательности предприятий – исполнителей контрактов. В сб.: Проблемы предпринимательства в экономике России: Межвуз. сб. науч. трудов. М.: ООО «Технологии стратегического менеджмента»; 2005. Вып. 8. С. 116–125.

11. Opricovic S., Tzeng G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 2004;156(2):445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

12. Shen K.-Y., Tzeng G.-H. Advances in multiple criteria decision making for sustainability: Modeling and applications (editorial). Sustainability. 2018;10(5):1600. https://doi.org/10.3390/su10051600

13. Polkovnichenko V. Household portfolio diversification: A case for rankdependent preferences. Review of Financial Studies. 2005;18(4):1467–1502. https://doi.org/10.2139/ssrn.293831

14. Armstrong M. A Handbook of Human Resource Management Practice. 10th ed. London: Kogan Page; 2006. 977 p.

15. Zakamouline V., Koekebakker S. Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance. Journal of Banking and Finance. 2009;33:1242–1254. https://doi.org/10.2139/ssrn.1028715

16. Hwang C.-L., Yoon K. Multiple attribute decision making: Methods and applications: A state-of-the-art survey. New York, USA: Springer; 1981. 259 p. (Lecture Notes in Economics and Mathematical Systems. Vol. 186).

17. Balcerzak A., Pietrzak M. Application of TOPSIS method for analysis of sustainable development in European Union countries. In: Loster T., Pavelka T. (eds.). Proceed. of the 10th Inter. days of statistics and economics. Prague, Czech Republic. September 8–10, 2016. Prague: Libuse Macakova, Melandrium; 2016. P. 82–92.

18. Shinno H., Yoshioka S., Marpaung S., Hachiga S. Qualitative SWOT analysis on the global competiveness of machine tool industry. Journal of Engineering Design. 2006;17(3):251–258. https://doi.org/10.1080/09544820500275180

19. Barberis N. Investing for the long run when returns are predictable. Journal of Finance. 2000;55(1):225–264. https://doi.org/10.1111/0022-1082.00205