Early warning models of financial distress. Case study of the Romanian firms listed on RASDAQ

Transcription

1 Theoretcal and Appled Economcs Volume XX (2013), No. 5(582), pp Early warnng models of fnancal dstress. Case study of the Romanan frms lsted on RASDAQ Mădălna Ecaterna ANDREICA The Bucharest Unversty of Economc Studes Abstract. In ths paper we desgn an early warnng model for Romanan dstressed frms. The logt model was bult based on fnancal ratos of 66 Romanan frms lsted on RASDAQ that were facng fnancal dffcultes n In addton, we dentfed the man prncpal components obtaned wth mnmum loss of nformaton after applyng the prncpal component analyss and proposed a new estmaton of the logt model by replacng the ntal set of nput data matrx wth the man prncpal components of the fnancal observatons. The results ndcated an ncrease wth 12 percentage ponts n the performance of the one year ahead predcton of fnancal dstress of the new warnng model. Keywords: dstress predcton; fnancal crss; logt model; prncpal component analyss. JEL Codes: C53, G32. REL Codes: 11B, 17B.

2 8 Mădălna Ecaterna Andreca 1. Introducton The early warnng models of unproductve frms that are confronted wth fnancal dffcultes and hgher rsk of bankruptcy brng a true decson support n the context of the current fnancal crss, not only by offerng warnng sgns of fnancal dstress, but also by helpng mnmzng the computatonal tme for determnng a frms rsk of nsolvency when requestng fundng to a bank. In ths paper we amed to buld an effcent one-year-ahead predcton model of Romanan unproductve frms and to establsh the man fnancal ratos that contrbute to rapd screenng of the frms wth hgh fnancal dstress rsk. In order to do that we collected fnancal ratos of 66 Romanan frms lsted on RASDAQ durng , out of whch half were facng penaltes for late payments n 2011 and had an unproductve behavor. In addton, we dentfed the man components obtaned after applyng the prncpal component analyss wth mnmal loss of nformaton and proposed a new estmaton of a logt model by replacng the ntal set of nput data wth ther man prncpal components and reached better predcton results. The ssue concernng the early warnng models of fnancal dstress or bankruptcy ganed great nterest snce the late 1960s. The frst step n ths drecton was made by Beaver (1966), who proposed a method to classfy frms nto two groups by usng a t-test statstc. Beaver's study was contnued by Altman (1968), who proposed a model of multvarate dscrmnant analyss, whle Esenbes (1977), Ohlson (1980) and Jones (1987) dentfed some nconsstences n the method and proposed new methods of fnancal dstress predcton. For nstance, n 1980 Ohlson proposed a logt model for fnancal dstress predcton and hs results showed that sze, fnancal structure, performance and current lqudty were mportant determnants of bankruptcy. Then n 1984 Zmjewsk (1984) proposed the probt model n order to predct the bankruptcy rsk of a frm, but the model had less applcablty n the lterature. Instead Shumway (2001) proposed the hazard model for predctng bankruptcy, that was defned as a mult-perod logt model. The man partculartes of the hazard model consst n the fact that the explanatory varables vary durng several tme perods, leadng to more effcent estmators. Moreover, the duraton model uses a baselne hazard functon that can be estmated drectly through macroeconomc varables n order to reflect the economc changes (Nam, Km, Park, Lee, 2008). In recent years many types of heurstc algorthms such as neural networks and decson trees have also been appled to the bankruptcy predcton problem and several mprovements n the fnancal dstress predcton were notced. For nstance, the studes made by Tam and Kang (1992), Salchenberger et al. (1992) and Jan and Nag (1998) concluded that neural networks outperform conventonal statstcal models of fnancal dstress

3 Early warnng models of fnancal dstress. Case study of the Romanan frms lsted on RASDAQ 9 predcton. Soon after that, hybrd Artfcal Neural Network methods were proposed n some fnancal dstress predcton studes such as Ym and Mtchell (2005) and Andreca (2009). Instead, Zheng and Yanhu (2007) and Andreca (2012) used decson tree models for corporate fnancal dstress predcton and presented the advantages of usng CHAID decson trees n comparson to a neural network model, whch s complcated to buld up and to nterpret or to a statstc model such as multvarate dscrmnate regresson and logstc regresson, where the patterns need to be lnearly separable and samples are assumed to follow a multvarate normal dstrbuton. In ther study, Zheng and Yanhu (2007) notced that t s not approprate to use fnancal nformaton to predct fnancal dstress ahead of four years and the results showed that decson trees are a vald model to predct lsted frms fnancal dstress n Chna, wth a 80% probablty of correct predcton. As notcng from the lterature revew, the early warnng models for dstress companes have been ntensvely studed startng wth 1960 and stll play a huge role especally durng fnancal crss. In ths context, early warnng models could be of great help to prevent fnancal dffcultes or bankruptcy of a frm and to protect the companes that are offerng fnancng to any potental fnancal dstressed frm. 2. Metodologes In order to buld predctve models of fnancal dstress n Romana, some logt econometrc models were estmated and then a prncpal component analyss was appled. Accordng to Shumway (2001) the logt model s a sngle-perod classfycaton model whch uses maxmum lkelhood estmaton to provde the condtonal probablty of a frm belongng to a certan category gven the values of the ndependent varables for that frm. The logt model descrbes the relatonshp between a dchotomous varable Y, that takes values 1 or 0 for dstress and non-dstress, respectvely, and k explanatory varables x 1, x 2,..., x k, representng seven fnancal ratos. The logt regresson model s defned as follows: p 1 e ( 1 k 0 j j x 1, j ) When applyng the logt-transformaton to the above equaton, we get a lnear relatonshp between logt(p ) and the explanatory varables: p logt (p ) log 1 - p (1) k x 0 j (2) 1, j 9

4 10 Mădălna Ecaterna Andreca Ths equaton s also called the logt form of the model, where logt(p ) s the log odds of dstress for the gven values x,1, x,2,, x,k of the explanatory varables. Regardng the threshold value to whch we compare the predcted probabltes of a frm to become productve or unproductve we chose the level of 0.5. Thus, f the estmated probablty s less than 0.5, the observaton s classfed as a productve frm, whle the probablty values above 0.5 ndcate an unproductve frm. The Prncpal component analyss (PCA) s a way of dentfyng patterns n data where graphcal representaton s not avalable, by reducng the number of dmensons, wthout much loss of nformaton. PCA nvolves a mathematcal procedure that reduces the dmensonalty of the ntal data space by transformng a number of possbly correlated varables nto a smaller number of uncorrelated varables called prncpal components. These components are synthetc varables of maxmum varance, computed as a lnear combnaton of the orgnal varables. The frst prncpal component accounts for as much of the varablty n the data as possble, and each succeedng component accounts for as much of the remanng varablty as possble. 3. Buldng the early warnng model For ths study, publc fnancal nformaton for the years was collected for 66 Romanan lsted companes on RASDAQ, correspondng to the same III-R market category wth the purpose to predct fnancal dstressed frms one year ahead. In order to have two equal sample groups of both fnancal dstressed and non-dstressed frms n the year 2011, 33 dstressed frms wth losses and outstandng payments were selected n the study together wth 33 nondstressed companes, that were matched by assets sze and actvty feld. The selecton of the man set of fnancal ratos for each company was condtoned by those varables that appeared n most emprcal work, but also restrcted to the avalablty of the fnancal data provded by the Bucharest Stock Exchange Market. Seven fnancal ratos were calculated for ths study n order to reflect the company s proftablty, asset utlzaton and growth ablty. They are presented n Table 1. Table 1 Fnancal ndcators CATEGORY CODE FINANCIAL RATIOS DEFINITION I1 Proft Margn Net Proft or Loss / Turnover 100 Proftablty I2 Return on Assets Net Proft or Loss / Total Assets 100 I3 Proft per employee Net Proft or Loss / number of employees I4 Operatng Revenue per employee Ln(Operatng revenue / number of employees) Asset utlzaton I5 Workng captal per employee Workng captal / number of employees I6 Total Assets per employee Ln(Total Assets / number employees) Growth ablty I7 Growth rate on total assets (Total Assets1 Total Assets0) / Total Assets0

5 Early warnng models of fnancal dstress. Case study of the Romanan frms lsted on RASDAQ 11 The ntal sample of 66 frms was dvded nto two groups: 76% of the ntal observatons were used n the learnng process (n-sample), whle the rest of 24% were used for predcton (out-of-sample). The predcton effcency of the model was based on the 24% of the observatons used for predcton and was calculated by comparng the forecastng results wth the actual values. After estmatng the econometrc model based on the fnancal ndcators regstered n 2010, t resulted the followng unfactoral model, wth the proft margn ndcator (I1) as the explanatory varable and the followng logt equaton: p P( y 1) 1 e 1 ( 0,3720,0648 I1) Although the McFadden R-squared value s only 32.5%, the result of the Expectaton Predcted Test suggests that the model brngs a total gan of 34% n comparson to the smple constant model and the level of 0.2 of the goodness of ft Test (H-L Statstcs) confrms the valdty of the model. Moreover, the Akake and Schwartz nformaton crteron values are small and the coeffcent sgn of varable I1 s accordng to the economc theory, statng that a reduced level of the proft margn mples hgher rsks of fnancal dstress. The out-of-sample forecastng performance of the logt model bult based on fnancal ratos of the year 2010 n order to predct fnancal dstress of the Romanan frms one year ahead s only 69%, accordng to Table 2. Table 2 The forecastng performance of the logt model IN SAMPLE OUT-OF-SAMPLE Healthy Unhealthy TOTAL Unhealthy Healthy TOTAL Total ncorect corect % ncorect % corect Improvng the effcency of the early warnng model A prncpal component analyss was then performed n order to reduce the dmensonalty of the orgnal data space wth mnmum loss of nformaton and n order to see to what extent are the varables used n the analyss relevant for obtanng classfcaton rules for the two types of frms. The egenvalues obtaned from PCA applcaton are shown n Table 3. 11

6 12 Mădălna Ecaterna Andreca Table 3 Total Varance Explaned Component Intal Egenvalues Extracton Sums of Squared Loadngs Rotaton Sums of Squared Loadngs Total % of Varance Cumulatve % Total % of Varance Cumulatve % Total % of Varance Cumulatve % Extracton Method: Prncpal Component Analyss. The frst three prncpal components that have values hgher than 1 are the followng: λ 1 = 2.55, λ 2 = 1.64 and λ 3 = 1.31, wth a mnmum loss of nformaton of approxmately 21.5%. The frst prncpal component s hghly correlated to I1 and I7, beng a proftablty ndcator. The second prncpal component s mostly correlated to I2, I3 and I5 and offers nformaton about frms assets and workng captal, whle the thrd prncpal component descrbes the economc actvty of a frm accordng to ts employees, beng strongly correlated to I4 and I6 (Table 4). Table 4 Rotated component matrx(a) Component I I I I I I I Extracton Method: Prncpal Component Analyss. Rotaton Method: Varmax wth Kaser Normalzaton. a Rotaton converged n 5 teratons. The graphcal representaton of the 66 frms on a three-dmensonal prncpal component space s shown n Fgure 1, where one can notce that, n general, the two types of frms tend to form two dstnct groups.

7 Early warnng models of fnancal dstress. Case study of the Romanan frms lsted on RASDAQ 13 Fgure 1. The frms on a three-dmensonal space The PCA allowed us to reduce the dmensonalty of the ntal data space wth mnmum loss of nformaton so that the two types of frms could be easly dentfed. In addton to that, we found that when replacng the fnancal ndcators of the predcton model wth the man prncpal components of the ntal data matrx, the predcton performance of the new logt model bult wth the frst two prncpal components mproved and reached an accuracy of predcton of 81.3%. The new econometrc model s descrbed by the equaton: p P( y 1) 1 e 1 ( 0,3543,043 fact1 1,394 fact2 ) The R 2 McFadden value reaches 39.7%. Moreover, the new model brngs a total gan of 28% n comparson to the smple constant model and the level of 0.21 of the goodness of ft Test (H-L Statstcs) confrms the valdty of the model The out-of-sample forecastng performance of the new logt model bult based on the frst two prncpal components of the ntal fnancal data of the year 2010 n order to predct fnancal dstress of the Romanan frms one year ahead s mprovng by reachng 81.3%. Table 5 The forecastng performance of the new logt model bult on prncpal components IN SAMPLE OUT-OF-SAMPLE Healthy Unhealthy TOTAL Unhealthy Healthy TOTAL Total ncorect corect % ncorect % corect

8 14 Mădălna Ecaterna Andreca 5. Conclusons In ths paper we desgn an early warnng model for Romanan dstressed frms. The logt model was bult based on fnancal ratos of 66 Romanan frms lsted on RASDAQ that were facng fnancal dffcultes n In addton, we dentfed the man prncpal components obtaned wth mnmum loss of nformaton after applyng the prncpal component analyss and proposed a new estmaton of the logt model by replacng the ntal set of nput data matrx wth the man prncpal components of the fnancal observatons. The results ndcated an ncrease wth 12 percentage ponts n the performance of the one year ahead predcton of fnancal dstress of the new warnng model. References Altman, E.I. (1968). Fnancal Ratos, Dscrmnant Analyss and the Predcton of Corporate Bankruptcy, Journal of Fnance, 23, pp Andreca, M.E. (2012). Fnancal Dstress Predcton of the Romanan Companes Usng CHAID Models, Metalurga Internatonal, nr. 12, pp , ISSN Andreca, M.E. (2009). Early warnng models for fnancal dstress: The case of the Romanan companes, Metalurga Internatonal, vol. 14, specal Issue nr. 11, pp Andreca, M.E. (2009). Predctng Romanan Fnancal Dstressed Companes, Advances n Economc and Fnancal Research - DOFIN Workng Paper Seres 37, Bucharest Unversty of Economcs, Center for Advanced Research n Fnance and Bankng - CARFIB, Beaver, W. (1966). Fnancal ratos as predctors of falure, Journal of Accountng Research (Supplement), 4, pp Esenbes, R. (1977). Ptfalls n the applcaton of dscrmnant analyss n busness, fnance and economcs, Journal of Fnance, 32, pp Jan, B.A., Nag, B.N. (1998). A neural network model to predct long-run operatng performance of new ventures, Annals of Operatons Research, 78, pp Jones, F.L. (1987). Current technques n bankruptcy predcton, Journal of Accountng Lterature, 6, pp Nam, C.W., Km, T.S., Park, N.J., Lee, H.K. (2008). Bankruptcy predcton usng a Dscrete- Tme Duraton Model Incoproratng Temporal and Macroeconomc dependences, Journal of Forecastng, 27, pp Ohlson, J.A. (1980). Fnancal ratos and the probablstc predcton of bankruptcy, Journal of Accountng Research, 18, pp Salchenberger, L.M., Cnar, E.M., Lash, N.A. (1992). Neural networks: A new tool for predctng thrft falures, Decson Scences, 23, pp Shumway, T. (2001). Forecastng bankruptcy more accurately: A smple hazard model, Journal of Busness, 74 (1), pp Ym, J., Mtchell, H. (2005). A comparson of corporate dstress predcton models n Brazl: hybrd neural networks, logt models and dscrmnant analyss, Nova Economa Belo Horzonte, 15, Zheng, Q., Yanhu, J. (2007). Fnancal Dstress Predcton on Decson Tree Models, IEEE