مدل های ترکیبی مبتنی بر طبقه بندی کننده های مجموعه ناهموار برای تنظیم قوانین تصمیم گیری رتبه بندی اعتباری در صنعت بانکداری جهانی

Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve credit rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in credit rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998–2007. Experimental results demonstrate that the proposed hybrid models for credit rating classification outperform the listing models in this work. A set of decision rules for classifying credit ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners.

The world financial crisis during 2007–2009 was due to reckless and unsustainable lending practices under deregulation and securitization of United States (US) mortgages, which were marketed as investments to global individual investors and financial institutions. The risks of a broad-based credit boom arose from a near-global speculative bubble in real estate and equities, eventually exposing other risky loans and inflated asset values, and instigating a global recession. Clearly, the emergence of subprime loan losses from 2007 to present rapidly reduced economic activity and saw numerous financial institutions and firms facing extreme financial difficulties, distress, or even bankruptcy. Despite a significant easing of national fiscal and monetary policy in US in an effort to stem the global recession, the world has failed to shake the financial crisis. Global investors continue to face difficult challenges, particularly in relation to banks in Europe, particularly in Greece, Italy, Portugal, and Spain, which currently have the same debt problems [29]. Banks typically face numerous risks, including credit, debt, interest rate, currency, liquidity, and systematic risk. Serial bank runs and other blows to financial sector confidence inevitably result from extreme events, such as the current financial crisis, which has seriously jeopardized regional and national economic stability [48]. Banks are clearly important to national, and even global, economic stability. However, relative to other industries, banking stability is very dependent on trust and reputation, which is particularly true for large banks. Global banking investors should therefore protect their profits by identifying high-quality targets. Consequently, an indicator is urgently needed to identify a bank’s financial status and operational competence. Market investors have used numerous indicators to identify superior investment targets in seeking increased profits. Credit ratings [9] and [18] evaluate the attractiveness of banks as investments. When properly assigned by rating agencies, such as Standard and Poor’s (S&P), Moody’s, and Fitch, they are invaluable to financial market participants, providing objective opinions about credit worthiness, investment risk, and default probability. Interested parties include owners, customers, management, personnel, investors, competitors, suppliers, creditors, media, regulatory agencies, researchers, and special-interest groups. Each group uses credit ratings in its own way [47]. For example, credit ratings are extremely important to stock market investors. Although the process of assigning a credit rating requires an enormous amount of time and resources [24], classification models based on financial ratios [13], such as capital adequacy, asset quality, management competence, liquidity risk, sensitivity to market risk (CAMELS) [6] and Earnings Before Interest and Taxes (EBITs), can simplify this process [44]. Financial ratios are typically employed to evaluate bank financial and operational competence, and rate overall management effectiveness based on quarterly and/or annual sales and investment performance. Financial ratios are widely used for modeling by both practitioners and researchers, and have been expressed in various forms [25] and [56]. Problems associated with assigning credit ratings resemble those related to forecasting financial crises and bankruptcy [13], [30], [44] and [53], which can be developed to construct early warning systems (EWSs) [6] using classification models based on financial ratios. Since the 1960s, numerous studies have constructed models for predicting financial crises and bankruptcy. These studies have applied both statistical methods and artificial intelligence (AI) techniques, including multiple discriminant analysis (MDA) [40], a logistic model [34], support vector machines (SVMs) [9], [24] and [30], and neural networks (NNs) [33]. Although these statistical methods are simple and their outcomes are easy to explain, their explanatory power is inferior to that of AI techniques. This creates decision-making difficulties, as policy-makers cannot fully comprehend and follow the results of the models they use. Moreover, almost all studies comparing the efficiency of these methods found that performance was highly dependent on the application field [8], study goals [17], context and data [27], or user experience [3]. Thus, we recommend employing AI techniques to develop efficient classifiers for forecasting. Artificial intelligence techniques, which have been extensively used when generating credit ratings, have outperformed statistical methods [9] and [24]. Particularly, intelligent hybrid systems integrate several models for processing classification problems [2], [50] and [55]. In practice, an ensemble classifier outperforms stand-alone models [43] and [44]. Given the limitations of statistical methods and AI techniques in stand-alone models, an intelligent hybrid model is needed that maximizes the advantages of statistical methods and AI techniques while minimizing their limitations. Interest in designing and applying various intelligent hybrid models has increased considerably over the last decade [43]. To improve prediction performance and increase investor profits, a reliable forecasting tool based on a hybrid model is required for classifying bank credit ratings. Designing a rule-based model that can reasonably and powerfully explain data is a significant trend in knowledge discovery. Notably, AI techniques for classification can automatically extract knowledge-based decision rules from a dataset and construct different model representations to explain that dataset [54]. Market investors are very interested in rule-based models that are based on AI techniques and germane to the global banking industry. Research to improve models that solve credit rating problems is valuable for two reasons. First, although the financial industry focuses on investors seeking personal benefit, AI techniques have rarely been used in credit rating research to generate comprehensive decision rules, particularly when compared with statistical methods; therefore, this work fills this knowledge gap. Second, the authors have extensive experience in the financial industry, about 14 years in total, and thus have relevant knowledge. Because each interested party has an opinion of how to best apply intelligent hybrid systems to real-world problems, particularly the current financial crisis, a reliable model that predicts credit ratings is welcomed. To objectively address the practical problems of classifying bank credit ratings and generate decision rules in the form of knowledge-based systems, this work applies two hybrid models. This work has the following four objectives: (1) implement the two hybrid models that use rough sets (RSs) to classify credit ratings in the global banking industry, minimize the number of selected attributes and generated rules, and increase prediction accuracy; (2) examine the main determinants influencing credit ratings; (3) assess the performances of the proposed hybrid models; and (4) generate comprehensive decision rules that can be applied to knowledge-based systems using RSs and the LEM2 algorithm, and provide reasonable explanatory power to interested parties. The remainder of this paper is organized as follows. Section 2 reviews the literature on credit rating classification. Section 3 describes the proposed hybrid models and algorithms. Section 4 gives verification details and compares the models. Finally, Section 5 draws conclusions and provides directions for future research.

For highly educated and skilled investors, bank financial reports are the most important available source of research data. A bank’s financial report helps investors stay up to date on a bank’s outlook, carefully study relevant indicators, and make forecasts regarding specific targets. In this era of global economic uncertainty, investors are building intelligent forecasting models for classifying credit rating problems. Successful investors use knowledge-based systems to differentiate good and bad trades accurately. Suitable tools can help successful investors understand their investment portfolios and make wise investment decisions. This work designed two hybrid models, incorporating the EK of the authors of the financial industry, FA, attribute discretization, feature selection method, and RS classification, to objectively classify credit ratings and clarify for interested investors the financial or operational competence of banks. (1) The FA-RS model first applies FA to reduce data dimensions and determine which variables influence credit ratings, and employs a global method to improve classification accuracy. (2) The MEPA-RS model first uses an MEPA discretization method to discretize continuous attributes and further improve the classification accuracy of rule-based models, and then uses attribute reduction to identify core attributes. Both hybrid models use RST to extract understandable decision rules and valuable hidden information from the original dataset. Experimental results for the FA-RS model reveal that the scree test criterion outperforms the latent root criterion, with a performance of 79.29% versus 78.55%, respectively. A comparison of the scree test criterion with the latent root criterion reveals that the former uses fewer attributes. Thus, extraction by the scree test criterion is probably intended to optimize the number of factors in financial data. Some studies have revealed that the scree test performs best with strong common factors [22]. Analytical results suggest that adopting the scree test criterion of FA is appropriate for financial data. Experimental results for the scree test criterion are consistent with those in literature [22]. Moreover, the MEPA-RS model markedly outperforms the FA-RS model and the other methods listed in Table 16 and Table 17, with performances of 82.14% and 79.29%, respectively. Experimental results indicate that the discretization method of MEPA can improve classification performance of rule-based models [10], and is an objective approach because the universe of discourse is partitioned based on data characteristics [11]. Attribute reduction using RSs is a practical method of feature selection for reducing the size of the set of attributes while maintaining the same classification quality as the original set. Furthermore, applying an RF is effective because it simultaneously improves accuracy and reduces the number of rules. Consequently, the MEPA-RS outperforms the FA-RS model in both accuracy and standard deviation, but the FA-RS model interprets decision rules based on common underlying factors best. In terms of managerial implications, banks that are large, have good asset quality, and sufficient liquidity are usually less risky and thus receive higher credit ratings than small banks that have poor asset quality and insufficient liquidity, which usually have high risk and are assigned lower credit ratings. Moreover, the ‘Asset Quality’ category strongly influences investment grades, while the ‘Capital’ category strongly influences speculative grades. Although the proposed hybrid models perform well, further experiments and improvements are required. Future studies should apply various types of credit rating other than from Fitch Ratings, such as S&P and Moody’s, to validate the classification performance of the proposed hybrid models. Numerous extraction methods exist for FA, and can extract important factors. Additionally, the proposed hybrid models can be applied to classification problems in other fields.