Forecasting credit ratings of EU banks
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Plakandaras, Vasilios; Gkonkas, Periklēs; Papadimitriou, Theophilos; Doumpa, Efterpi; Stefanidou, Maria Article Forecasting credit ratings of EU banks International Journal of Financial Studies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Plakandaras, Vasilios; Gkonkas, Periklēs; Papadimitriou, Theophilos; Doumpa, Efterpi; Stefanidou, Maria (2020) : Forecasting credit ratings of EU banks, International Journal of Financial Studies, ISSN 2227-7072, MDPI, Basel, Vol. 8, Iss. 3, pp. 1-15, https://doi.org/10.3390/ijfs8030049 This Version is available at: https://hdl.handle.net/10419/257716 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
International Journal of Financial Studies Article Forecasting Credit Ratings of EU Banks Vasilios Plakandaras 1,* , Periklis Gogas 1, Theophilos Papadimitriou 1, Efterpi Doumpa 2and Maria Stefanidou 2 1Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece; [email protected] (P.G.); [email protected] (T.P.) 2School of Economics, Business Administration and Legal Studies, International Hellenic University, 57001 Thessaloniki, Greece; [email protected] (E.D.); [email protected] (M.S.) *Correspondence: [email protected] Received: 3 June 2020; Accepted: 3 August 2020; Published: 6 August 2020 Abstract: The aim of this study is to forecast credit ratings of E.U. banking institutions, as dictated by Credit Rating Agencies (CRAs). To do so, we developed alternative forecasting models that determine the non-disclosed criteria used in rating. We compiled a sample of 112 E.U. banking institutions, including their Fitch assigned ratings for 2017 and the publicly available information from their corresponding financial statements spanning the period 2013 to 2016, that lead to the corresponding ratings. Our assessment is based on identifying the financial variables that are relevant to forecasting the ratings and the rating methodology used. In the empirical section, we employed a vigorous variable selection scheme prior to training both Probit and Support Vector Machines (SVM) models, given that the latter originates from the area of machine learning and is gaining popularity among economists and CRAs. Our results show that the most accurate, in terms of in-sample forecasting, is an SVM model coupled with the nonlinear RBF kernel that identifies correctly 91.07% of the banks’ ratings, using only 8 explanatory variables. Our findings suggest that a forecasting model based solely on publicly available financial information can adhere closely to the official ratings produced by Fitch. This provides evidence that the actual assessment procedures of the Credit Rating Agencies can be fairly accurately proxied by forecasting models based on freely available data and information on undisclosed information is of lower importance. Keywords: credit ratings; machine learning; Support Vector Machines; banks 1. Introduction Credit Rating Agencies (CRAs) have been around for more than 150 years. Their role progressed from simple information collectors to quasi-official evaluators of credit risk throughout the modern global financial system. CRAs were originally paid by potential investors to compile financial information and data at a time when such a service was too difficult and costly. Nonetheless, after the 1929 big market crash CRAs started to play a more formal role in the financial system. The stricter rules that were imposed by regulators with the Glass–Steagal Act in the mid-1930s limited banking, insurance and other financial institutions to only invest in “investment grade” securities, assessed by the CRAs. Since then, we have seen a growing reliance on CRAs ratings as they are increasingly incorporated in private contracts, investment guidelines for pension funds, endowment funds and other private entities that all came to rely on these CRAs ratings. In the aftermath of the 2008 global financial crisis, the role of CRAs evolved to an increasingly important albeit a questionable one; they provide important financial information to market participants, mainly by issuing ratings on the probability of default for specific debt issuers. In recent years, there is an increased interest in the credit ratings process and specifically on the actual criteria used by the Int. J. Financial Stud. 2020,8, 49; doi:10.3390/ijfs8030049 www.mdpi.com/journal/ijfs
Int. J. Financial Stud. 2020,8, 49 2 of 15 CRAs to evaluate debt issuers. Focusing on banking institutions, rating agencies provide publicly available ratings associated with the ability of a banking institution to meet debt obligations on time. The supervisory framework of the Basel II and the Basel III accords expanded the role of credit rating agencies. As a result, now banks are required to calculate their risk weighted assets (RWA). This is done by either using the ratings provided by the CRAs or creating their own internal ratings approach. In either case, ratings are now in the epicenter of risk assessment and the resulting capital requirements for the banking institutions that are used by the supervising authorities. Nevertheless, the experience from the 2008 financial crisis dictates that CRAs may underreact to financial information or significantly delay in downgrading debt issuers. In many occasions, they only downgrade a debt issuer long after the markets do. This happens especially in the case of “too-big-to-fail” financial and banking institutions. Long before the 2008 crisis, their integrity was under scrutiny in other major corporate collapses as well, such as the Enron failure in the U.S., the Asian financial crisis and Parmalat scandal and the subsequent failure in Europe. All these corporations were assessed and assigned high ratings just a few days before their collapse. The same was true even in major sovereign debt crises like the debt crises of Greece, Spain, Portugal and Italy in the early 2010’s. The Securities and Exchange Commission (SEC) in a 2011 annual report on credit raters found “apparent failures” at each of the 10 credit rating agencies they examined. These included Standard & Poor’s (S&P), Moody’s, and Fitch, the “big three” credit rating agencies that hold 95% of the corresponding global ratings market. On top of these concerns, the fact that the criteria of the assessments are not fully disclosed and transparent, augments the mistrust on the actual quality of the ratings. Based on the above criticism, the regulatory authorities in the U.S. decided to take some regulatory action in 2008 and since require the public disclosure of the information a CRA uses to determine a rating on a structured product. In response to the inability of CRAs to properly appreciate the risks in complex financial instruments before 2008, the European Commission strengthened the regulatory and supervisory framework for CRAs in the E.U. The new E.U. rules were introduced in three consecutive rounds. The first round of rules, which came into force at the end of 2009, established a regulatory framework for CRAs and introduced a regulatory oversight regime, whereby CRAs had to be registered and supervised by national competent authorities. In addition, CRAs were required to avoid conflicts of interest, and to have sound rating methodologies and transparent rating activities. In 2011, these rules were amended to take into account the creation of the European Securities and Markets Authority (ESMA), which supervised CRAs registered in the E.U. A further amendment was made in 2013 to reinforce the rules and address weaknesses related to sovereign debt credit ratings. In the case of European banks, total bank debt issuance amounted to approximately € 881 billion in 2020. That included € 220 billion of corporate bonds, € 129 billion of medium-term notes, € 160 billion of short-term debt and €510 billion of covered bonds (European Central Bank (European Central Bank ECB). This study seeks to find the most important factors contributing to the ratings of European banking institutions. This was done using only publicly available information from the published financial statements of all banks. In doing so, we compiled a dataset of 112 E.U. banking institutions and attempted to fit a Probit and a Support Vector Machines (SVM) model in order to pinpoint the variables used in the true rating procedure. Thus, our focus was to use the publicly available data in order to accurately forecast the assigned credit ratings from the CRAs. To the best of our knowledge, our paper is the first that attempts to pinpoint the criteria used by rating agencies to assess the resilience of E.U. banks. The remainder of the paper is organized as follows. Section 2reviews the literature. In Section 3 we describe the data and the methodology, while the empirical findings are presented in Section 4. Section 5concludes the paper.
Int. J. Financial Stud. 2020,8, 49 3 of 15 2. Literature Review While bank ratings are used extensively as explanatory variables in the economic literature, the nature of ratings per se remains largely ill-examined. A paper closely related to this study is Gogas et al. (2014) who examined the ratings of the Fitch Rating Agency for the case of 92 U.S. banks. The authors used ordered logit models to forecast bank credit ratings based on publicly available financial statements of the banks. Their empirical findings suggested that almost 84% of the actual ratings can be matched based on publicly available information. Bissoondoyal-Bheenick and Treepongkaruna (2011) analyzed the quantitative determinants of bank ratings, provided by Standard & Poor’s, Moody’s, and Fitch for U.K. and Australian banks. They instead based their analysis on an ordered probit model and found that accounting variables from the financial statements of banking institutions had more explaining power in identifying banks’ ratings than macroeconomic ones. Pagratis and Stringa (2007) conducted an ordered probit analysis in order to evaluate the potential linkage between Moody’s bank ratings and bank characteristics such as provisions, profitability, cost efficiency, liquidity, short-term interest rates and bank-size. From a different perspective, Papadimitriou (2012) explored the clustering properties of 90 financial institutions using a correspondence analysis map. The goal was to correspond clustering groups with ratings from Fitch. The empirical findings support a correspondence between clusters and ratings, though the regions corresponding to the ratings are highly overlapped. Credit ratings have also been explored with the use of machine learning methods. Ravi et al. (2008) argued that almost 83.5% of bank failures can be foreseen based on a Support Vector Machine (SVM) model that utilized the information of 54 financial variables on a sample of 1000 banks, over the period 2005–2008. Although the issue of identifying the exact structure of credit ratings for banks has not been studied to a large effect by the relevant literature, significant relevant papers can be found in the area of predicting bond ratings. Ederington (1985); Pinches and Mingo (1973); Belkaoui (1980) used statistical methods such us logistic regression and multivariable discriminant analysis (MDA) to predict bond ratings. Based on alternative sets of variables the prediction results vary in accuracy between 50% and 70%. Many studies on bond credit rating prediction build forecasting neural networks models (Dutta and Shekhar 1988;Surkan and Singleton 1990;Kim et al. 1993) that are more accurate than typical statistical methods. Moody and Utans (1995) used neural networks to forecast corporate bond ratings based on the ratings of S&P. Using 10 input variables they correctly forecasted 85.2% of the actual ratings. Maher and Sen (1997) compared neural networks to logistic regression models in forecasting bond ratings for the period 1990–1992. The most accurate model achieved 70% on a holdout sample. Kwon et al. (1997) compared ordinal pairwise partitioning (OPP) with back propagation and conventional neural networks for bond ratings of Korean firms. Using 126 financial variables for the period 1991–1993 they achieved 71–73% via neural networks with OPP and 66–67% via conventional neural networks. Huang et al. (2004) compared back propagation neural networks (BPNN) to SVMs in forecasting corporate credit ratings for the U.S. and Taiwan. The most accurate model was a linear SVM model achieving an 80% of correct bond classification. He et al. (2012) examined the relationship between ratings and the business cycle on mortgage-backed securities (MBS) spanning the period from 2000 to 2006 and their respective ratings from Moody’s, S&P and Fitch. The idea was that large financial institutions will persuade CRAs to issue a higher rating than the one dictated by the rating methodology. This discrepancy should be visible when the price of securities sold by big issuers drops more than the price of securities sold by small issuers (keeping everything else fixed). The empirical findings provided evidence in favor of a favorable rating for larger issuers in comparison to small ones, especially during the market boom period of 2004–2006. Hau et al. (2013) extended the previous setting to the banking sector, using a cross-sectional sample of 39,000 banking institution ratings for the period 1990–2011 from Moody’s, S&P, and Fitch. The authors concluded that large banks systematically received higher ratings than they should have actually received. An important factor in this favorable rating scheme is the provision of large securitization to CRAs that affects the final outcome of the rating. This phenomenon is more
Int. J. Financial Stud. 2020,8, 49 4 of 15 prevalent during economic booms, when the risk of reputational loss is lower. The erosion of the rating system due to the aforementioned practice leads to the adverse phenomenon where the upper investment grade range does not reflect expected default probabilities, i.e., a higher rating does not necessarily correspond to a lower risk of default. From a different perspective, Kraft (2015) compared ratings to issuers with rating-based performance-priced loan contracts to issuers with contracts based on accounting ratios and other loan agreements. The study examined adjustments to ratings, i.e., the difference between actual rating and the hypothetical rating implied by reported financials. The study found that, after an adverse economic shock, the adjustments made for firms with rating-based contracts are more favorable than for firms with other types of contracts. This finding is consistent with the hypothesis of rating catering and suggests that reputational concerns are not sufficient to fully eliminate this phenomenon. 3. Data and Methodology 3.1. The Data For our analysis we used a cross-section of 112 European banking institutions over the period 2013–2017. In order to train our forecasting models, we compiled observations for 34 variables from the Bank-Focus/Orbis 14 database that originate from the banks’ financial statements up to 4 years prior to the 2017 actual rating grade. Thus, counting the lags of the 34 independent variables, we compiled a total of 136 explanatory variables considered as possible forecasters of bank ratings, where each lag was treated as an independent variable. The motivation for selecting up to 4 years of data prior to the 2017 Fitch rating stemmed from the fact that, as discussed in the introduction section, CRAs often react to the information reflected in financial statements with a delay. We obtained and used the ratings from Fitch for the year 2017 and the financial statements for the period 2013–2017, due to data availability issues. The independent variables can be classified into four general categories: Assets, Liabilities, Income statement and Financial Ratios. In Table 1we report the compiled financial variables used as independent variables (or features in the machine learning terminology) to our models. Table 1. Financial Variables. No Abbreviation Description Panel A: Assets 1 TASSET Total Assets 2 LO Loans 3 GRLO Gross loans 4 CBCB Cash& Balances at Central Bank 5 LASSET Liquid assets Panel B: Liabilities 6 DSF Deposits and Short-term funding 7 EQ Equity 8 TCDE Total customer deposits 9 OIBL Other interest-bearing liabilities 10 BDE Bank deposits
Int. J. Financial Stud. 2020,8, 49 5 of 15 Table 1. Cont. No Abbreviation Description Panel C: Income and Expenses 11 NI Net Income 12 NIM Net interest margin 13 NIR Net interest revenue 14 PBT Profit before tax 15 OPIN Operating income 16 ITEX Income tax expense 17 OPPR Operating profit 18 TOE Total operating expenses 19 NOR Net operating revenues 20 TIP Total interest paid 21 TIR Total interest received Panel D: Financial Ratios 22 NLTA Net loans/Total assets 23 NLDSF Net loans/Deposits and Short-Term funding 24 NLTDB Liquid assets/Total deposits and borrowed 25 LADSF Liquid assets/Deposits and Short-Term funding 26 LATDB Liquid assets/Total deposits and borrowed 27 NIRAA Net interest revenues/Average assets 28 OOPIAA Other operating income/Average assets 29 NOEAA Non-interest expenses/Average assets 30 ROAE Return On Average Equity (ROAE) 31 ROAA Return On Average Assets (ROAA) 32 ETA Equity/Total assets 33 ENL Equity/Net loans 34 EL Equity/Liabilities The dependent variable is ordinal and it is grouped in our case in four classes. These are assigned integer values from 0 to 3, such that lower values indicate a lower rating. The groupings of the four classes are depicted in Table 2. Table 2. Grouping of Bank ratings in classes. Class Identification Rating Category Number of Banks 3 AAA AA– AA A+24 2 A– A BBB+34 1 BBB– BBB 32 0 BB+BB– BB B+B– 22 Total 112 The grouping is performed is such a way so that the four identified classes contain a quasibalanced number of banking institutions, forming a balanced dataset that avoids micronumerosity issues.
Int. J. Financial Stud. 2020,8, 49 6 of 15 3.2. Support Vector Machines Support Vector Machines is a supervised machine learning method used in data classification. The basic concept of an SVM is to select a small set of data points from the initial dataset, called Support Vectors (SV), that defines a linear boundary separating the data points in two classes. In what follows we describe briefly the mathematical derivations of the SVM theory. We consider a dataset of vectors xi∈R2(i=1, 2, . . . ,n) belonging to 2 classes (targets 1 ) yi∈ {−1, +1}. If the two classes are linearly separable, we define a boundary as: f(xi)=wTxi−b=0, yif(xi)>0∀i(1) where wis the weight vector and bis the bias. This optimal hyperplane is defined as the decision boundary that classifies each data vector to the correct class and has the maximum distance from each class. This distance is often called a “margin”. In Figure 1, the SVs are represented with a contour circle, the margin lines (defining the distance of the hyperplane from each class) are represented by solid lines and the hyperplane is represented in the center. Int.J.FinancialStud.2020,8,xFORPEERREVIEW6of14 “margin”.InFigure1,theSVsarerepresentedwithacontourcircle,themarginlines(definingthe distanceofthehyperplanefromeachclass)arerepresentedbysolidlinesandthehyperplaneis representedinthecenter. Figure1.Hyperplaneselectionandsupportvectors.TheSVsareindicatedbythepronouncedred circles,themarginlinesarerepresentedwiththecontinuouslines,andthehyperplaneisrepresented withthedottedline. Inordertoallowforapredefinedleveloferrortoleranceinthetrainingprocedure,Cortesand Vapnik(1995)introducednon‐negativeslackvariables,𝜉0,∀𝑖,andaparameter,C,describingthe desiredtolerancetoclassificationerrors.Thesolutiontotheproblemofidentifyingtheoptimal hyperplanecanbedealtthroughtheLagrangerelaxationprocedureofthefollowingequation: min 𝐰,,𝛏max 𝐚𝛍 1 2‖𝐰‖𝐶𝜉 𝑎 𝑦𝐰𝐱𝑏1𝜉𝜇𝜉 (2) whereξimeasuresthedistanceofvectorxifromthehyperplanewhenclassifiederroneously,and𝑎1, …,𝑎𝑛arethenon‐negativeLagrangemultipliers. Thehyperplaneisthendefinedas: 𝐰 𝑎𝑦𝐱 (3) b𝐰 𝐱𝑦,𝑖∈𝑉(4) where𝑉𝑖:0𝑦𝐶isthesetofsupportvectorindices. Whenthetwo‐classdatasetcannotbeseparatedbyalinearseparator,theSVMispairedwith kernelmethods.Theconceptisquitesimple:thedatasetisprojectedthroughakernelfunctionintoa richerspaceofhigherdimensionality(calledafeaturespace),wherethedatasetislinearlyseparable. ThesolutiontothedualproblemwiththeprojectionofEquation(2)nowtransformsto: max 𝐚𝑎 1 2𝑎𝑎𝑦𝑦 K 𝐱,𝐱(5) undertheconstraints∑𝑎𝑦0 and0𝑎𝐶,∀ 𝑖,whereK𝐱,𝐱isthekernelfunction.The SVMmodelcanbeextendedtoamulticlassclassificationmethod,usingtheone‐against‐the‐rest Figure 1. Hyperplane selection and support vectors. The SVs are indicated by the pronounced red circles, the margin lines are represented with the continuous lines, and the hyperplane is represented with the dotted line. In order to allow for a predefined level of error tolerance in the training procedure, Cortes and Vapnik (1995) introduced non-negative slack variables, ξi≥ 0, ∀i , and a parameter, C, describing the desired tolerance to classification errors. The solution to the problem of identifying the optimal hyperplane can be dealt through the Lagrange relaxation procedure of the following equation: min w,b,ξmax aµ 1 2kwk2+C N X i=1 ξi− N X j=1 ajhyjwTxj−b−1+ξji− N X k=1 µkξk (2) where ξi measures the distance of vector x i from the hyperplane when classified erroneously, and a 1 , . . . ,an are the non-negative Lagrange multipliers. 1In the SVM jargon.
Int. J. Financial Stud. 2020,8, 49 7 of 15 The hyperplane is then defined as: ˆ w= N X i=1 aiyixi(3) ˆ b=ˆ wTxi−yi,i∈V(4) where V=i: 0 <yi<Cis the set of support vector indices. When the two-class dataset cannot be separated by a linear separator, the SVM is paired with kernel methods. The concept is quite simple: the dataset is projected through a kernel function into a richer space of higher dimensionality (called a feature space), where the dataset is linearly separable. The solution to the dual problem with the projection of Equation (2) now transforms to: max a= N X i=1 ai−1 2 N X j=1 N X k=1 ajakyjykKxj,xk(5) under the constraints PN i=1aiyi= 0 and 0 ≤ai≤C , ∀i , where Kxj,xk is the kernel function. The SVM model can be extended to a multiclass classification method, using the one-against-the-rest approach; one class is kept aside and all others are grouped to form a new “grouped” class. After measuring the accuracy in forecasting the independent class kept aside, the second one is considered as independent and the others are grouped and so on until all classes are rotated. The overall accuracy is measured as the mean accuracy over all independent classes. In our models we examined two kernels: the linear kernel and the radial basis function (RBf) 2 . The linear kernel detects the separating hyperplane in the original dimensional space of the dataset, while the RBF projects the initial dataset onto a higher dimensional space. The mathematical representation of each kernel is: Linear K1(x1,x2)=xT 1x2(6) RBF K2(x1,x2)=e−γkx1−x2k2(7) 4. Empirical Findings 4.1. Feature Selection We identified the variables that contribute the most to the assigned bank ratings following a thorough regression-based variable selection procedure. The selected variables were then fed to both a Probit and an SVM model. As a first step, we measured the correlation coefficient, ri,R , between each independent variable i and the assigned rating R. Based on the correlation values, we created six groups of regressors as follows: In group 1, we included all variables with ri,R≥ 0.4. This resulted in 18 variables in group 1; TASSET, NIM, TIR, NOEAA for the period 2013–2016 and NIRAA for years 2014 and 2016. In a similar manner, in group 2 we did the same for ri,R≥ 0.4 along with all the lags of variable NIRA. This group included 20 variables. In groups 3 and 4, we included the 30 variables with the highest positive correlation and the 30 variables with the highest negative correlation, respectively. In group 5, the variables included were the five with the highest correlation with the dependent variable and the five with the lowest one, a total of 10 variables. The last group, group 6, contained the entire sample of explanatory variables, a total of 136 features. Table 3summarizes the variables’ groups. 2 Our implementation of SVR models is based on LIBSVM (Chang and Lin 2011). The software is available at http: //www.csie.ntu.edu.tw/~{}cjlin/libsvm/.
Int. J. Financial Stud. 2020,8, 49 8 of 15 Table 3. Number of variables in each regressor group. Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 18 variables 20 variables 30 variables 30 variables 10 variables 136 variables The next step was to use the selected groups in order to identify the most significant variables in terms of identifying bank ratings. This was done in each group either by: (a) a combinatorial exhaustive search methodology of all possible sets of four variables in each one of the above six groups, hand-picking the ones with the highest R-square and (b) the same process but with all possible sets of eight variables from within each one of the six groups, and (c) a stepwise forward least squares technique where we kept the set of variables with a p-value greater than 0.1. This variable selection procedure produced a total of 18 groups of regressors. Table 4summarizes the variables selected from each method. Table 4. Selected variables in each one of the 18 group. Combinatorial 4 Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 TASSET14 TASSET14 TASSET14 TIR16 TASSET14 TIR16 TIR16 TIR16 TASSET13 DSF14 TIR16 DSF14 NOEAA13 NIRAA13 OPPR13 NIRAA13 OIBL13 TASSET15 NIM13 NOEAA13 OIBL16 NOEAA13 TASSET13 NOR14 Combinatorial 8 Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 TASSET14 TASSET14 TASSET14 DSF14 TASSET14 TIR16 TIR16 TIR16 TASSET13 NIRAA13 TIR16 TASSET13 TASSET13 NIRAA13 OIBL16 EQ16 OIBL13 ETA16 TIR14 NOEAA13 LADSF16 TIR15 TASSET13 LO15 NOEAA13 TASSET13 LADSF14 GRLO14 TIR14 GRLO15 NIM13 TIR14 LADSF13 LO14 NOEAA14 NOEAA16 NOEAA15 NOEAA15 OPPR14 OOPIAA13 TIR15 OOPIAA13 NOEAA16 NOEAA16 NI13 OOPIAA16 TASSET16 NLTA14 Stepwise-forward Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 TASSET14 TASSET14 TASSET14 TIR15 TASSET14 TASSET14 TIR16 TIR16 TASSET13 DSF14 TIR16 TIR16 NIRAA14 NIRAA14 OPPR13 NIRAA13 OIBL13 DSF14 NOEAA14 NOEAA14 OIBL16 NOEAA13 TASSET13 PBT13 (4) (4) (4) EQ16 (4) NIR13 (5) CBCB16 CBCB13 OOPIAA14 TIR14 NIR14 (10) 4.2. Ordered Probit Model Results The above selection procedure resulted in 18 different sets of regressors. These sets were fed to an ordered probit model that forecasts the credit bank rating assigned by Fitch for each institution for the year 2017. The evaluation of the forecasting accuracy of each forecasting model is depicted in Table 5. Each column corresponds to each one of the six groups of the prefiltered regressors while each row presents the forecasting results for the corresponding selection criterion. According to these results, the best accuracy using the probit model for all regressor selection criteria was achieved from the combinatorial search of eight variables from group 6.
Int. J. Financial Stud. 2020,8, 49 15 of 15 Ravi, Vadlamani, H. Kurniawan, Peter Nwee Kok Thai, and P. Ravi Kumar. 2008. Soft computing system for bank performance prediction. Applied Soft Computing 8: 305–15. [CrossRef] Surkan, Alvin J., and J. Clay Singleton. 1990. Neural networks for bond rating improved by multiple hidden layers. Paper presented at IEEE International Conference on Neural Networks, San Diego, CA, USA, June 17–21; pp. 157–162. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).