DOI: https://doi.org/10.18371/fcaptp.v3i26.144280

MODELS OF FORECASTING IN THE MECHANISM OF EARLY INFORMING AND PREVENTION OF FINANCIAL CRISES IN CORPORATE SYSTEMS

L. S. Guryanova, V. S. Gvozdytskyi, O. V. Dymchenko, O. A. Rudachenko

Abstract


The paper is devoted to the problem of preventing financial crises in corporate systems, whose activities are becoming more and more complex in the context of globalization. The mechanism of early informing and crisis prevention in corporate systems is proposed, and includes five main modules: an analysis of the financial condition of the corporation, an analysis of the financial condition of subsidiaries, an evaluation of the impact of the financial crisis on a subsidiary on the threat of bankruptcy of the corporation as a whole, forecasting the financial condition of subsidiaries and corporation as a whole, anti-crisis management. The first four modules of the mechanism are the modules of implementation of proactive crisis management in the corporation, aimed at preventing the emergence of a crisis state, both in individual elements and in the corporate system as a whole. The fifth module is used in conditions of the current negative estimation of the financial condition of the corporation, and it is a "response" to existing crisis processes and phenomena in the corporation. After its implementation during the process of monitoring of the financial condition, proactive control modules are started to be used to allow early diagnosis and to prevent a crisis state. Particular attention is paid to such modules of proactive management as the evaluation of the impact of financial crises of subsidiaries on bankruptcy of the corporations as a whole and forecasting financial crises. A model basis for these two modules was developed. Neural networks, the mathematical apparatus of fuzzy logic, and the Caterpillar method were used for developing the models of estimation of the crisis threat in the corporate system. The developed set of models allowed to estimate the threat of financial crises in the parent enterprise and in the subsidiaries of the corporation, not only in the current but also in the perspective periods. The obtained results indicate that the financial condition of the investigated corporation is characterized by low level of the bankruptcy threat. Along with this, there is an increase in the threat of bankruptcy in a number of subsidiaries in the perspective period and the strong impact of local crises on the financial position of the corporation as a whole. The latter leads to the need of implementation of the anti-crisis measures in the corporate structure. An adequate tool for choosing anti-crisis measures and developing scenarios for the implementation of the anti-crisis management strategy is simulation modelling based on the concept of system dynamics.

Keywords


corporate system; financial crisis; prevention; forecasting; neuro-fuzzy models; «caterpillar» method.

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References


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GOST Style Citations


Javier De A. Bankruptcy forecasting : A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS) / Javier De Andres, Pedro Lorca, Francisco Javier de Cos Juez, Fernando Sánchez-Lasheras // Expert Systems with Applications. - 2011. - Vol. 38. - Is. 3. - March. - P. 1866-1875.

Ning Ch. Clustering and visualization of bankuptcy trajectory using self-organizing map / Ning Chen, Bernardete Ribeiro, Armando Vieira, An Chen // Expert Systems with Applications. — 2013. - Vol. 40. — Is. 1. - January. - P. 385-393

Yu-Chien Ko. An evidential analysis of Altman Z-score for financial predictions; Case study on solar energy companies / Yu-Chien Ko, Hamido Fujita, Tianrui Li // Applied Soft Computing. - 2017. - Vol. 52. - P. 748-759.

Matviychuk A. V. Bankruptcy Prediction in Transformational Economy: Discriminant and Fuzzy Logic Approaches / A. V. Matviychuk // Fuzzy Economic Review. - 2010. - Vol. 15. - Is. 1. - P. 21-38.

Li S. A financial early warning logit model and its efficiency verification / S. Li, S. Wang // Knowledge-Based Systems. - 2014. - Vol. 70. - P. 78-87.

Brezigar-Masten A. CART-based selection of bankruptcy predictors for the logit model / Arjana Brezigar-Masten, Igor Masten // Expert Systems with Applications. - 2012. - Vol. 39. - Is. 11. - 1 September. - P. 10153-10159.

Zarei M. Applying adaptive neuro fuzzy model for bankruptcy prediction / M. Zarei, M. Rabiee, T. Zanganeh // International Journal of Computer Applications. - 2011. - № 20 (3). - Р. 15-21.

Bahia I. Using Artificial Neural Network Modeling in Forecasting Revenue: Case Study in National Insurance Company International / I. Bahia // International Journal of Intelligence Science. - 2013. - Vol. 3. - № 3. - P. 136-143.

Zelenkov Y. Two-step classification method based on genetic algorithm for bankruptcy forecasting / Yuri Zelenkov, Elena Fedorova, Dmitry Chekrizov // Expert Systems with Applications. - 2017. - Vol. 88. - 1 December. - P. 393-401.

Gordini N. A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy / Niccolò Gordini // Expert Systems with Applications. - 2014. - Vol. 41. - Is. 14. - 15 October. - P. 6433-6445.

Klebanova Т. S. Modeling cash flows of the enterprise in terms of uncertainty / T. S. Klebanova, L. S Guryanova, O. J. Kononov. - Kharkiv : PH «ІNZHEK», 2006.

Barannikov V. V. Synthesis of composite simulation and optimization models of current assets circuit (synergisticeffect) / V. V. Barannikov // Donetsk National University Bulletin, 2 (B) - Economics and Law. - 2008. - Р. 347-350.

Guryanova L. S. Forecasting as a basic element of the corporations management system [Electronic resource] / L. S. Guryanova, T. S. Klebanova, V. S. Gvozdytskiy, S. V. Milevskyi // Financial and credit activity: problems of theory and practice. - 2017. - № 2 (23). - Available at : http://fkd.org.ua.

The website for State Statistics Service of Ukraine [Electronic resource]. - Available at : http://www.ukrstat.gov.ua.





Copyright (c) 2018 L. S. Guryanova, V. S. Gvozdytskyi, O. V. Dymchenko, O. A. Rudachenko

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ISSN (print) 2306-4994, ISSN (on-line) 2310-8770