Prediction of Insolvency of Hungarian Micro Enterprises
Ključne reči:
bankruptcy, market, forecastingApstrakt
The aim of the study is to establish insolvency forecast model with the usage of different statistical methods and compare their efficiency. Besides this the relation and direction between indebtedness and financial distress is also part of the examination. With different approaches we nearly reached the same efficiency, the main focus was on the independent testing sample where we did not apply any modification on the dataset supposing realistic circumstances for predicting the probability of default. The research is focusing on small companies, since their number in the economy is considered high, but for this segment such insolvency forecasts are very rare.
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Reference
Altman, E. I. (1968) “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, The Journal of Finance, Vol. 23, No. 4, pp. 589-609.
Arutyunjan, A. (2002) “A mezőgazdaságivállalatokfizetésképtelenségénekelőrejelzése, Gödöllő: SzentIstván Egyetem, Közgazdaságtudományi DoktoriIskola (Ph.D. thesis).
Beaver, W. (1966) “Financial Ratios as Predictors of Failure, Empirical Research in Accounting: Selected Studies”, Journal of Accounting Research, Supplement to Vol. 5, pp. 1-111.
Brealey, R. A., Myers, S. C. (1999) “Modern vállalatipénzügyek”, Panem Kft., Budapest
Fazekas, B. (2007) “Vállalativáltoztatásésválság menedzsmentésal kalmazottpénzügyi módszerek”, Thesis, Gödöllő.
My-X.hu, available at: http://miau.gau.hu/myx-free/ (01/05/2016)
Odom, M. D., Sharda, R. (1990) “A Neural Network Model for Bankruptcy Prediction”, In: Proceeding of the International Joint Conference on Neural Networks, San Diego, 17–21 June 1990, Volume II. IEEE Neural Networks Council, Ann Arbor, pp. 163-171.
Ohlson, J. (1980) “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research, Vol. 18, No. 1, pp. 109-131.
Pacelli, V., Azzollini, M. (2011) “An Artificial Neural Network Approach for Credit Risk Management”, Journal of Intelligent Learning Systems and Applications, pp. 103–112.
Ross, Stephen A., (1977) “The Determination of Financial Structure: The Incentive Signaling Approach”, Bell Journal of Economics 8, 23-40.
Sajtos L., Mitev A. (2007), “SPSS kutatásiésadatelemzésikézikönyv”, Alenia Kiadó, Budapest.
Székelyi M., Barna I. (2002) “Túlélőkészletaz SPSS-hez”, Typotex Kiadó, Budapest.
Virág M., Kristóf T., Fiáth A., Varsányi J. (2013) “Pénzügyielemzés, csődelőrejelzés”, Kossuth Kiadó, Budapest.