SEARCH
 

Search

 

Результаты поиска 1 - 3 из 3
Начало | Пред. | 1 | След. | Конец


The specifics of predicting the bankruptcy of State-owned organizations as per the Russian laws

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 )

Journal: Finance and Credit, #6, 2019

Subject The article discusses legislative principles for predicting the bankruptcy of State-owned entities in Russia.
Objectives The study is aimed to refine the methodology that the Russian regulations prescribe to predict the bankruptcy of State-owned entities in line with their specifics. It specifies the applicable financial indicators and economic guidance for their evaluation. As per Hypothesis 1, financial indicators set out in the effective laws are outdated and fail to accommodate the specifics of State-owned corporations. As per Hypothesis 2, although being overlooked in the effective laws, some ratios have the high predicting potential (over 70 percent) and can supplement the methodology.
Methods The study is based on the methods approved by the Federal Agency for State Property Management. Applying the CART technique to financial metrics, we managed to specify their statutory values in line with the specifics of State-owned corporations.
Results Having verified the outcome through a sample of 692 companies, we found an increase in the predictive potential of the above metrics when statutory values change. We proved the high accuracy of some ratios concerning State-owned corporations.
Conclusions and Relevance The findings will help supplement the methodology by the Federal Agency for State Property Management and contribute to the efficiency of bankruptcy forecasts concerning State-owned corporations.


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.


Результаты поиска 1 - 3 из 3
Начало | Пред. | 1 | След. | Конец


Отсортировано по релевантности | Сортировать по дате