Possibilities to apply classical bankruptcy prediction models in the construction sector in Lithuania
LTReikšminiai žodžiai: Bankruptcy; Bankruptcy prediction; Bankruptcy prediction models.
ENThe paper presents the results of a research of the application of bankruptcy prediction models in the construction sector in Lithuania. During the financial crisis, many companies in the construction sector went bankrupt. Therefore, the research aims to find out whether conventional bankruptcy prediction models are applicable in this sector. Moreover, Lithuanian researchers have contradictory opinions about the possibilities to apply bankruptcy prediction models. Empirical research studies provide conflicting results as well. It should also be noted that earlier Lithuanian research studies (1999-2013) featured a small sample of companies, which could have had an impact on great errors of the research results. The above mentioned reasons encourage evaluating the accuracy of bankruptcy prediction models by examining a large sample of companies and evaluating real benefits obtained from the acquired information. The present study is distinguished by its large sample of companies that was compiled for the first time (433 companies in the construction sector that were filed for bankruptcy in 2009-2013 were examined). To achieve the aim of the research, i.e., to evaluate the applicability of bankruptcy prediction models in Lithuanian companies in the construction sector, 5 classical statistical bankruptcy prediction models were chosen: 3 linear discriminant analytical models (Altman, Springate, Taffler) and 2 logistic regression models (Chesser, Zavgren). From the Altman’s models, the Altman’s model for companies whose shares are not quoted in the stock exchange markets, Altman’s Z"-Score Model for the service companies and Altman’s Z"-Score Model for emerging countries were investigated. From Taffler models, Taffler (1973) and Taffler & Tisshaw (1977) models were analysed.Having carried out the research, it is possible to come to the conclusion that the most accurate bankruptcy prediction models with the highest bankruptcy probability are the following: the logistic regression adapted Chesser and Zavgren models; the accuracy of the linear discriminant Springate models is also high. The research proved that the Taffler and Altman’s Z" Score Model for emerging countries models are least accurate. The results of the research might be useful for both the executive managers of companies in the construction sector and investors who analyse the problems of the operation continuity. [From the publication]
- Analysis of the accuracy of bankruptcy prediction models: the case of Lithuanian companies / Jurgita Stankevičienė, Gabija Prazdeckaitė. Apskaitos ir finansų mokslas ir studijos: problemos ir perspektyvos 2021, Nr. 1 (15), p. 44-53.
- Assessment of the practical application of corporate bankruptcy prediction models / Deimena Kiyak, Daiva Labanauskaitė. Ekonomika ir vadyba. 2012, Vol. 17, no. 3, p. 895-905.
- Bankroto prognozavimo modelių taikymo Lietuvos transporto ir saugojimo sektoriuje tyrimas / Rasa Kanapickienė. Ekonomika ir vadyba: aktualijos ir perspektyvos. 2014, Nr. 3 (35) p. 35-43.
- Bankroto tikimybės vertinimo modelis skirtingo mokumo ir pelningumo įmonėms / Dovilė Vitonytė, Alma Mačiulytė-Šniukienė. 22-oji Lietuvos jaunųjų mokslininkų konferencija „Mokslas – Lietuvos ateitis. Ekonomika ir vadyba", 2019 m. vasario 13-14 d. Vilnius. Vilnius : Technika, 2019. 1 pdf (9 p.).
- Bankruptcy prediction models: case of the construction and transport & storage sector in Lithuania / Rasa Kanapickiene, Renatas Spicas. New challenges of economic and business development - 2016: society, innovations and collaborative economy : international conference : May 12-14, 2016, Riga, University of Latvia : proceedings. Riga: University of Latvia, 2016. P. 344-356.
- Evaluation of bankruptcy prediction models for Lithuanian companies / Evelina Bercevič, Daiva Jurevičienė. Trendy ekonomiky a managementu. 2013, 7, 13, p. 43-55.
- Financial ratios for detection of company’s insolvency and bankruptcy fraud: similarities and differences / Julija Liodorova, Irina Voronova. Sociālo Zinātņu Vēstnesis 2020, 1 (30), p. 7-29.
- Research of possibility of bankruptcy diagnostics applying neural network / Ojaras Purvinis, Povilas Šukys, Rūta Virbickaitė. Inžinerinė ekonomika. 2005, Nr.1 (41), p. 16-22.
