Insolvency Problem of Agricultural Enterprises and Forecasting of Their Bankruptcy

Tyumen State University Herald. Social, Economic, and Law Research


2018, Vol. 4. №3

Insolvency Problem of Agricultural Enterprises and Forecasting of Their Bankruptcy

For citation: Ilina A. D., Spitsina D. V., Orusova O. V. 2018. “Insolvency Problem of Agricultural Enterprises and Forecasting of Their Bankruptcy”. Tyumen State University Herald. Social, Economic, and Law Research, vol. 4, no 3, pp. 201-212. DOI: 10.21684/2411-7897-2018-4-3-201-212

About the authors:

Anastasia D. Ilina, Undergraduate Student, Faculty of International Economic Relations, Financial University under the Government of the Russian Federation (Moscow);

Daria V. Spitsina, Undergraduate Student, Faculty of International Economic Relations, Financial University under the Government of the Russian Federation (Moscow);

Olga V. Orusova, Cand. Sci. (Econ.), Associate Professor, Department of Economic Theory, Financial University under the Government of the Russian Federation (Moscow);


This article studies the problem of diagnosing an enterprise’s insolvency in the agro-industrial complex. The authors estimate the probability of a company’s bankruptcy relying on the coefficient analysis and the nonlinear forecast model.

The study’s importance lies in the following factors: lack of significant studies in this field, interdisciplinarity, and conformity with the world agenda of economic research.

The objectives of scientific research include a) substantiating the insolvency factors of agricultural enterprises and b) considering the possibility of forecasting the bankruptcy of an enterprise. For these purposes, the authors have studied the trends and financial condition of the agro-industrial complex.

The study allowed:

  • identifying common features between the crisis state of the economy and the company as a separate economic agent;
  • determining possible obstacles to the effective operation of the enterprise;
  • forecasting insolvency on the example of small and medium-sized enterprises of the Russian agro-industrial complex.

To achieve these goals, the authors have used the toolkit of financial, statistical and comparative retrospective analysis, programming methods, and data visualization in the language Python 3.0 (Python 3.0) in the service “Jupiter”.

This study relies on the data on the financial reporting of 119 Russian small mixed agricultural enterprises, which were selected in 2012-2016 with the help of the counterparty verification system “SPARK”.

The authors analyze the interrelationships between crisis phenomena in the economy and their impact on a particular company, and determine the range of internal factors of an enterprise’s inefficiency. They also consider the specifics of the Russian legal and regulatory framework, as well as the necessity of an analysis using nonlinear models on the example of the machine learning algorithm “random forest’, accounting for the specifics of Russian mixed agricultural enterprises.


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