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Problems of forecasting the bankruptcy of agricultural commodity producers: Russian legislation effectiveness assessment

Fedorova E.A. Financial University under Government of Russian Federation, Moscow, Russian Federation ( ecolena@mail.ru )

Khrustova L.E. Financial University under Government of Russian Federation, Moscow, Russian Federation ( khrustoval@yandex.ru )

Chekrizov D.V. ZAO GlobalTel, Moscow, Russian Federation ( CHEKrisovDV@mail.ru )

Journal: Economic Analysis: Theory and Practice, #12, 2017

Importance The article addresses the bankruptcy problem in the agricultural sector and analyzes relevant effective laws to reveal the flaws in the currently applied methodology to assess the financial condition of agricultural goods producers.
Objectives The purpose of the research is to formulate proposals to improve Russian laws in the sphere of agricultural goods producers' bankruptcy prediction.
Methods The research rests on the CART (Classification and Regression Tree) methodology whereby we specify financial ratios that are applied to evaluate agricultural companies. To estimate financial thresholds, we analyze the sampling consisting of 580 companies, including 273 bankrupts.
Results To enhance the Russian laws, we revised financial ratios enabling to determine bankrupt companies, offered a better classification of companies based on their financial ratios, considering the industry-specific features of agricultural producers. We included a new indicator with a high level of predictive capability into the existing methodology, and recommended to exclude the ratio of working capital to inventories as it has low forecasting power.
Conclusions and Relevance If implemented, the offered changes to the methodology will increase the efficiency of bankruptcy forecasting for agricultural goods producers and help take timely measures to prevent failures.


Forecasting the bankruptcy of enterprises: Evidence from the construction, manufacturing, transportation, agricultural and trade industries

Fedorova E.A. Financial University under Government of Russian Federation, Moscow, Russian Federation ( ecolena@mail.ru )

Fedorov F.Yu. Financial University under Government of Russian Federation, Moscow, Russian Federation ( fedorovfedor92@mail.ru )

Khrustova L.E. Financial University under Government of Russian Federation, Moscow, Russian Federation ( khrustoval@yandex.ru )

Journal: Finance and credit, #43, 2016

Subject The study considers bankruptcy evaluation parameters formalized in the Russian legislation.
Objectives The aim is to provide empirical support to the methodology for bankruptcy evaluation offered by the Russian legislation, and update the parameters of financial performance it establishes.
Methods The study rests on two hypotheses: financial indicators established by Russian laws correctly evaluate the probability of bankruptcy and are better adapted to the realities of modern Russian economy than similar western models; an adequate assessment of the probability of bankruptcy in the Russian legislation is provided not only through financial performance, but also through a set of standards for its evaluation. However, the current standards are obsolete and do not consider the industry specifics of analyzed companies.
Results We built a standard model of logistic regression that enables to assess the accuracy of financial indicators of bankruptcy approved by Russian laws, and to support their correctness empirically based on the data on enterprises operating in construction, manufacturing, transportation, agricultural and trade industries. Similarly, we proved the need for updating the criteria for financial performance evaluation, and offered a new system of standards, which takes into account the industry characteristics of the companies under analysis. Both hypotheses were confirmed and empirically proved.
Conclusions The findings substantiate the need for updating the legal standards, the importance of industry specifics, and show that our developed standards are correct and can be translated into practice for analysis purposes.


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