Везде
A study of the correlation between macroeconomic indicators and the probability of enterprise bankruptcy in the construction industry
Table of contents
Annotation Estimate Publication content
References Comments
Share
QR
Metrics
A study of the correlation between macroeconomic indicators and the probability of enterprise bankruptcy in the construction industry
Annotation
PII
S020736760002279-5-1
DOI
10.31857/S020736760002279-5
Publication type
Article
Status
Published
Authors
  Denis Gilev
Occupation: Senior Lecturer
Affiliation: Ural Federal University named after the first President of Russia B.N. Yeltsin
Edition
Issue 10
Pages
69-78
Abstract

In the article two models of predicting bankruptcy in the construction industry are being compared: the one based on financial coefficients only, and the other comprising both macroeconomic and financial coefficients. The results obtained support the hypothesis that macroeconomic ratios included in the bankruptcy predicting model make it more accurate and increase its predictive power.

Keywords
probability of bankruptcy, financial coefficients, macroeconomic indices, forecasting, logit-model
Received
30.11.2018
Date of publication
11.12.2018
Number of purchasers
10
Views
1225
Readers community rating
0.0 (0 votes)
Cite   Download pdf Download JATS

References

1. E.A. Federova, E.V. Gilenko, S.E. Dovzhenko. Models for Bunkruptcy Forecasting: Case Study of Russian Enterprises // Studies on Russian Enterprises Development. 2013. № 24 (2). R. 159–164.

2. Tsentr makroehkonomicheskogo analiza i kratkosrochnogo prognozirovaniya. [Ehlektronnyj resurs]. 2017. URL: http://www.forecast.ru.

3. Sami Ben Jabeur, Youssef Fahmi. Forecasting financial distress for French firms: a comparative study // Empirical Economics. 1968. Published online: 29 March 2017.

4. E.I. Altman. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy // Journal of Finance.1968. № 23 (4).

5. Yang Hwae Huo. Bankruptcy Situation Model in Small Business: The Case of Restaurant Firms // Hospitality Review, 2006. № 24 (2). R. 49–58.

6. Jeroen Oude Avenhuis. Testing the generalizability of the bankruptcy prediction models of Altman, Ohlson and Zmijewski for Dutch listed and large non-listed firms // The School of Management and Governance, University of Twente, Enschede, the Netherlands. 2013.

7. Philippe du Jardin, David Veganzones, Eric Séverin. Forecasting Corporate Bankruptcy Using Accrual-Based Models // Computational Economics. 2017.

8. Eduardo Acosta-González, Fernando Fernández-Rodríguez, Hicham Ganga. Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data // Computational Economics. 2017.

9. Honjo Y. Business Failure of new firms: an empirical analysis using a multiplicative hazard model. // Int J Indust Org. 2016. № 18 (4). R. 557–574.

10. E. A. Federova, S. E. Dozhenko, F. Yu. Federov. Bankruptcy-Prediction Models for Russian Enterprises: Specific Sector-Related Characteristics // Studies on Russian Economic Development. 2016. № 27 (2), R. 254–261.

11. E. A. Federova, S. E. Dozhenko, E. V. Gilenko. Bankruptcy-Prediction for Russian Companies: Application of Combined Classifiers // Expert Systems with Applications. 2013. № 40. R. 7285–7293.

12. SPARK Database (2017, December 15). Retrieved from http://www.spark-interfax.ru.

13. Lili Sun. A re-evaluation of auditor’s opinions versus statistical models in bankruptcy prediction // Quantitive Financial Accounting. 2007. № 28. R. 55–78.

14. Ofitsial'nyj sajt Tsentral'nogo Banka. [Ehlektronnyj resurs]. 2017. URL: http://www.cbr.ru.

15. Federal'naya sluzhba gosudarstvennoj statistiki. [Ehlektronnyj resurs]. 2017. URL: http://www.gks.ru.

Comments

No posts found

Write a review
Translate