Industrija 2/2011 - Ekonomski institut

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V. P a v l o v i ć , S . M u m i n o v i ć , J . M . C v i j a n o v i ć A p p l i c a t i o n o f S a n d i n & . . . 1. INTRODUCTION T he ability to provide powerful predictions of corporate insolvencies is of great importance both from an individual and a collective point of view. From an individual perspective, alongside owners, employees, customers, suppliers, certified accountants, and other counterparties of companies, in particular banks are interested in accurate insolvency predictions, as in case of bankruptcies of their costumers they have to reckon considerable losses. From a national economy perspective, the employment of powerful insolvency prediction models by creditors is an important precondition for guaranteeing the soundness and stability of the banking system and for the implementation of risk-sensitive loan terms which provide a motivation for incentive compatible, risk sensitive behavior of debtors. ([4], p.3) The prediction of company failure has been well researched using developed country data. A variety of models have been developed in the academic literature using techniques, such as multiple discriminant analysis (MDA), logit, probit, recursive partitioning, hazard models, and neural networks. Summaries of the literature are provided in Zavgren (1983), Jones (1987), O‟leary (1998), Boritz, Kennedy and Sun (2007) and Agarwal and Taffler (2007). ([11], p.76) Table 1: Systematization of Statistical Models Used for Predicting Business Bankruptcy Model Type Discriminant analysis Logit model Logit model Author Model Form [ (F(1) – function of the model for 1 year before; F(2) – function of the model for 2 years before; in other cases there is no differentiation or lack of data] Altman (1996) F = 1,2x(AC-SL)/AT + 1,4xNP/AT + 3,3xEBIT/AT + 0,6xmarket_EQ/TL + 0,999xTR/AT Altman, Sabato (2007) Pang-Tien, Ching- Wen, Hui-Fun (2008) F = 4.28 + 0,18xEBIT/AT – 0,01xSL/EQ + 0,08xNP/AT + 0,02xcash/AT + 0,19xEBITxINT F(1) = -4,44 + 0,08xTL/AT – 0,042xEBIT/INT 0,021xOP/INT F(2) = -7,03 + 0,067xTL/AT + 0,044xOC/TR – 0,00000042xNP/TR + 0,027xGP/NP Effectiveness (1 year / 2 years) Number of Firms Used, Country & Year 95.45% / NA 66; USA; 1966- 1968 75.43% / NA 432; USA; 2003- 2004 92.9% / 94.9% 116; Taiwan; 2002-2007 Disrciminant analysis Logit model Logit model Discriminant analysis Sandin, Porporato (2007) Lin, Piesse (2004) Joo-Ha, Taehong (2000) Maczynska (2004) F = 15,06x OP/TR + 16,11xEQ/AT – 4,14 F(1) = 0,2 – 0,33xNP/AT – 0,17xcash/TL – 0,95x(AC-SL)/AT F(2) = 1,73 – 1,78xNP/AT – 0,7x(AC- SL)/OC F = 0,1062xINT/TR – 0,00682xEBIT/TL – 0,1139xTR/REC F = 9,498xOP/AT + 3,566xEQ/AT + 2,903x(NP+DEP)/TL +0,452xAC/SL – 1,498 70% (average for 2 years) 80.52% / 87% 22; Argentina; 1991-1998 77; Great Britain; 1985-1995 80.4% / 76.1% 46; South Korea; 1997- 1998 88.4% 128; Poland; 2001 2
I n d u s t r i j a 2 / 2 0 1 1 . Model Type Author Model Form [ (F(1) – function of the model for 1 year before; F(2) – function of the model for 2 years before; in other cases there is no differentiation or lack of data] Effectiveness (1 year / 2 years) Number of Firms Used, Country & Year Discriminant analysis Gajdka, Stos (1996) F=0,44 + 0,20098xTR/AT + 0,001302xSL/OCx365 + 0,76097xNP/AT + 0,96596xGP/TR – 0,34109xTL/AT 82.5% / NA 40; Poland; Logit model Gruszczynski (2003) F = 1,3508 + 7,5153xOP/AT – 6,1903xTL/AT 86.96% 46; Poland; 1995 Discriminant analysis Prusak (2005) F = 1,4383xNP/TL + 0,1878xOC/SL + 5,0229xSP/AT – 1,8713 97.40% / 93.67% 78; Poland; 1998-2003 Source: Korol, Tomasz. (<strong>2011</strong>) Multi-Criteria Early Warning System Against Enterprise Bankruptcy Risk, International Research Journal of Finance and Economics, Issue 61.p. 143-144 Table 2: Systematization of Models of Soft Computing Techniques Used for Predicting Business Bankruptcy Type of the model Artificial neural network Artificial neural network Genetic algorithm Artificial neural network Artificial neural network Artificial neural network Author Neves, Vieira (2006) Shah, Murtaza (2000) Sikora, Shaw (1994) Serrano-Cinca (1997) Sen, Stivason (2004) Witkowska (2002) Form of the model ( n – k – o n – number of neurons in entry layer, k – number of neurons in hidden layer, o – number of neurons in output layer) 20 – 15 – 1 (20 financial ratios) (multilayer perceptron) 8 – 3 – 2 (AC/SL; TR/AC; TR/REC; EBIT/INT; TL/AT; AT/EQ; NP/TR; NP/EQ) (multilayer perceptron) NP/AT; TL/AT; NP/TR; LL/AT; AC/SL; AC/TR; AC/SL; (AC- SL)/TR; EQ/TL; trends: TR, NP, (ACINV)/SL (the structure of model was not given) 9 – 2 – 1 [AC/AT; (AC-cash)/AT; AC/TL; RES/TL; NP/AT; NP/EQ; NP/TL; OC/TR; cash/TL) (multilayer perceptron) 8 – 17 – 2 (LL/AT; AC/AT; AC/SL; cash/AT; Log AT; AC/TR; TR/AF; REC/INV) (multilayer perceptron) 13 – 4 – 5 (AC/SL; cash/SL; NP/TR; NP/EQ; NP/AT; REC/TRx365; INV/TRx365; TR/AT; EBIT/INT; TL/AT; qualitative variables: market share, chances of growth, qualifications of managers) (multilayer perceptron) Effectiveness (1 year / 2 years) 84,1% / 76,5% Number of companies, country & year 2800; France; 1998-2000 73% / NA 60; USA; 19921994 66,8% 104; USA; 1991 93,94% 66; Spain; 50,6 % 150; USA; 19701990 90% 250; Poland 3
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