SHS Web Conf.
Volume 62, 201917th International Scientific Conference “Problems of Enterprise Development: Theory and Practice” 2018
|Number of page(s)||4|
|Section||Improvement of Accounting and Analytical Support of Sustainable Development of Social and Economic Systems|
|Published online||15 March 2019|
- G.I. Khaustova, E.B. Panina, T.A. Stepanova, Evaluation of factors effect on financial stability of livestock organi-zations. Economics: Yesterday, Today and Tomorrow, 6(10A), 117-129 (2016). [in Rus.] [Google Scholar]
- B. Hirtle, A. Kovner, J. Vickery, Assessing financial stability: The capital and loss assessment under stress scenarios (CLASS) model. Journal of Banking & Finance, 69, 35-55. DOI: 10.1016/j.jbankfin.2015.09.021(2016). [CrossRef] [Google Scholar]
- S.P. Kyurdzhiev, A.A. Mambetova, E.P. Peshkova, An integral evaluation of the financial state of the regional en-terprises. Economica Regiona – Economy of Region, 12(2), 586-601. DOI: 10.17059/2016-2-22 (2016). [CrossRef] [Google Scholar]
- E.I. Sukhanova, S.Y. Shirnaeva, Different approaches to macroeconomic processes simulation and forecasting. Fun-damental Research, 12, 406-411 (2015). [in Rus.]. [Google Scholar]
- E.I. Sukhanova, S.Y. Shirnaeva, A.G. Mokronosov, Econometric models for forecasting of macroeconomic indices. International Journal of Environmental and Science Education, 11(16), 9191-9205 (2016). [Google Scholar]
- G.A. Gadelshina, A.V. Aksyanova, Forecasting enterprise profits using a multi-trend model. Bulletin of Kazan Technological University, 16(1), 277-281 (2013). [in Rus.]. [Google Scholar]
- O.Y. Patlasov, N.V. Vasina,) Logit-regression technique for modeling the credit rating of legal entities agricultural organizations (based on the regulations of Sberbank of Russia). Human Science: Humanitarian Studies, 2(10), 85-95 (2012. [in Rus.]. [Google Scholar]
- D.S. Bidzhoyan, Model for assessing the probability of revocation of a license from the Russian bank. Finance: Theory and Practice, 22(2), 26-37 (2018). [in Rus.]. [CrossRef] [Google Scholar]
- M. Irfan, S. Saha, S.K. Singh, A random effects multinomial logit model for the determinants of exit modes: Evi-dence from a panel of US manufacturing firms. Journal of Economic Studies, 45(4), 791-809. https://doi.org/10.1108/JES-03-2017-0075 (2018). [CrossRef] [Google Scholar]
- V. Lapo, Efficiency of investment stimulation methods in a timber industry complex: An econometric research. Ap-plied Econometrics, 1(33), 30-50 (2014). [in Rus.]. [Google Scholar]
- J.M. Pereira, M. Basto, A.F. das Silva, Comparing logit model with discriminant analysis for predicting bankruptcy in Portuguese hospitality sector. European Journal of Tourism Research, 16, 276-280 (2017). [Google Scholar]
- D.A. Hensher, S. Jones, Forecasting corporate bankruptcy: Optimizing the performance of the mixed logit model. ABACUS-A Journal of Accounting Finance and Business Studies, 43(3), 241-264. DOI: 10.1111/j.1467-6281.2007.00228.x (2007). [Google Scholar]
- V. Boguslauskas, R. Mileris, Estimation of credit risk by artificial neural networks models. Inzinerine Ekonomika – Engineering Economics, 4, 7-14 (2009). [Google Scholar]
- Y.Q. Wei, B. Liu, X.M. Liu, Entry modes of foreign direct investment in China: a multinomial logit approach. Jour-nal of Business Research, 58(11), 1495-1505. DOI: 10.1016/j.jbusres.2004.10.002 (2005). [CrossRef] [Google Scholar]
- E.A. Fedorova, E.V. Gilenko, The use of binary choice models to predict bank failures. Economics and Mathemati-cal Methods, 49(1), 106-118 (2013). [in Rus.]. [Google Scholar]
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