• Ruslan Sagitov Kazan Federal University
  • Alexey Kirpikov Kazan Federal University



financial stability, simulation modeling, cash flows, VaR technique.


Most modern methods of retrospective assessment of financial stability of companies have significant internal contradictions that hinder their productive use in the process of scientific research and practical activities of economic entities. A coefficient analysis of the financial condition is accompanied by difficulty in determining the normative values of indicators that would take into account the industry and individual characteristics of the organization. The use of regression analysis requires a significant array of historical data and the selection of key indicators that do not have a high level of correlation. According to the authors, the forecasting of cash flows using simulation methods has a significant potential for predicting financial stability. The empirical basis of the study was formed by indicators of cash flow budgets, information presented in annual reports, and also published in the media by one of the largest petrochemical companies in the Russian Federation in retrospect from 2011 to 2018. The tools of simulation modeling assumed the use of a uniform distribution law of a random variable, the justification of the boundaries of the change in the initial variables with a confidence level of 95% was based on the provisions of the VaR method. The conceptual basis of the simulation model was determined by an algorithm for formalizing the dependence of the components of cash flows on current financial and investment activities with a resulting indicator, which was played by the free cash balance at the end of the forecast period. According to the authors, improving the quality of the meaningful interpretation of the results implies an independent statistical evaluation and visual presentation of the results of experiments that demonstrated a positive and negative level of effectiveness of financial and economic activities


Não há dados estatísticos.


Abad, P., Benito, S., López, C. (2014) A comprehensive review of Value at Risk methodologies. Spanish Review of Financial Economics. 12(1), pp. 15-32.

Chernavsky K. 2010 Events and trends control in diagnostics of financial capacity of transportation companies. The Journal of Alternative Investments. 13 (1), pp. 41-54.

Barth, ME, Cram, DP, Nelson, KK (2001). Accruals and the prediction of future cash flows. Accounting review. 76(1) pp. 27-58
Basak, S., Shapiro, A. 2001 Value-at-risk-based risk management: Optimal policies and asset prices. Review of Financial Studies. 14. pp. 371-405.

Eppen, Gary D., Matin, R. Kipp, Schrage, Linus (1989) Scenario approach to capacity planning. Operations Research. 37(4). pp. 517-527.

Harrison, J.R., Lin, Z., Carroll, G.R., Carley, K.M. (2007) Simulation modeling in organizational and management research. Academy of Management Review. 32(4), pp. 1229-1245.

Hendry, D.F. (1984) Monte carlo experimentation in econometrics. Handbook of Econometrics 2, pp. 937-976.

Huss, W.R., Honton E. J. (1987) Scenario planning -What style should you use? Long Range Planning. 20(4). pp. 21-29.

SPARK [Electronic resource]: A professional market and company analysis system. Access mode:
Interfax: a corporate information disclosure center. [Electronic resource]. Access mode:



Como Citar

SAGITOV, R. .; KIRPIKOV, A. . ANALYSIS OF FINANCIAL STABILITY OF COMPANIES WITH THE USE OF IMITATION MODELING OF CASH FLOWS. Gênero & Direito, [S. l.], v. 8, n. 6, 2019. DOI: 10.22478/ufpb.2179-7137.2019v8n6.49186. Disponível em: Acesso em: 18 abr. 2024.



Seção Livre