Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines

Highlights

• Combination of option-based model with accounting data for credit risk model.

• Application of market model to non-listed firms.

• Use of a novel additive support vector machines model.

Abstract

Credit risk rating is an important issue for both financial institutions and companies, especially in periods of economic recession. There are many different approaches and methods which have been developed over the years. The aim of this paper is to create a credit risk rating model, using a machine learning methodology that combines accounting data with the option-based approach of Black, Scholes, and Merton. The model is built on data for companies listed in the Greek stock exchange, but it is also shown to provide accurate results for non-listed firms as well. Linear and nonlinear support vector machines are used for model building, as well as an innovative additive modeling approach, which enables the construction of comprehensible and accurate credit scoring models.

Introduction

Credit risk refers to the probability that a client will not be able to meet his/her debt obligations (default). Over the years, many factors have contributed to the increasing importance of accurate credit risk measurement. Altman and Saunders [1] list five main issues, which are still valid in the current context: (i) a worldwide structural increase in the number of defaults, (ii) a trend towards disintermediation by the highest quality and largest borrowers, (iii) more competitive margins on loans, (iv) a declining value of real assets (and thus collateral) in many markets, and (v) a dramatic growth of high risk exposures including credit derivatives. Credit risk measurement is nowadays a critical issue as demonstrated by the recent outbreak of the global credit crisis in 2007–2008.

In a credit risk management context, the accurate estimation of the probability of default is a crucial point. Credit rating models (CRMs) are widely used for that purpose. CRMs evaluate the creditworthiness of obligors, estimate the probabilities of default, and classify obligors into risk groups. In a corporate credit granting context, most CRMs combine key financial (accounting) and non-financial data into an aggregate index indicating the credit risk of the firms. Such models can be constructed with a variety of statistical, data mining, and operations research techniques (e.g., logistic regression, neural networks, support vector machines, rule induction algorithms, multicriteria decision making, etc.). Comprehensive reviews of this line of research can be found in [2], [3], [4]. Despite their success and popularity, traditional credit scoring models are mostly static and they are based on historical accounting data, which may fail to represent adequately the future of the firms and the trends in the business environment [1], [5]. This is particularly important in the context of an economic turmoil, where exogenous conditions deteriorate rapidly in a short time period, thus affecting corporate activity and leading to increased credit risk levels throughout the market. Mensah [6] and Hillegeist et al. [7] also discuss issues related to the accounting standards and practices, which affect the quality of the information that financial statements provide.

The shortcomings of accounting-based credit scoring models have led to the consideration of a wide variety of alternative approaches (comprehensive overviews can be found in [1], [8]). Among them, structural models have attracted considerable interest. Structural models use stock exchange data to assess the probability of default [9], [10]. Stock prices reflect all the information related to the current status of the firms as well as the investors’ expectations about their future prospects [5]. Furthermore, market data are constantly updated in accordance with new information that becomes available about the operation of firms and the environment in which they operate. These features of market data and models indicate that they may be better suited for default prediction and credit risk measurement. Actually, several studies provide empirical results in support of market models in the context of credit risk modeling and bankruptcy prediction [5], [7]. Market models have also been shown to contribute in the construction of improved hybrid systems in combination with accounting-based models [11], [12].

Despite their strong theoretical grounds and good predictive power, market models are limited to firms listed in stock exchanges. Therefore, their extension to non-listed firms has attracted some interest over the past decade. Moody’s KMV RiskCalc™ model [13] is a commercial implementation, which has been employed in several countries with positive results [14], [15]. Altman et al. [16] used US data to examine the potential of developing multivariate regression models providing estimates for the probability of default implied by a market model. The authors found that this approach provides similar results to default prediction models, thus concluding that both approaches should be treated as complementary sources of information.

This study extends the results of Altman et al. [16] by investigating the applicability of a market-based credit risk modeling approach in a context where the hypotheses of market efficiency may be invalid [17]. In particular, we test whether a definition of default on the basis of a market model can be employed to build a credit scoring model for non-listed firms and compare the results to a default prediction model fitted on historical default data. The analysis is based on data from Greece over the period 2005–2010 using samples of listed and non-listed firms. The Greek case provides a challenging context due to two main reasons. First, the Greek stock market, after flourishing at the end of the 1990s, it entered a period characterized by increasing volatility, decreasing liquidity, and high market concentration with few large capitalization companies dominating the market. These features became even clearer during the international credit crisis and the subsequent sovereign debt crisis that hit the country, thus putting into serious question the efficiency of the Greek stock market [18]. Second, the crisis had a particularly strong effect on the Greek economy, with a sharp deterioration of the general economic and business conditions, which led to an unprecedented increase in the number of defaults and bankruptcies over a very short period of time. Thus, credit risk management becomes a challenging issue in this context, and the peculiarities of the Greek case cast doubts on whether an approach based on the grounds of a market model could actually provide useful results.

On the methodological side, non-parametric machine learning techniques are employed based on the framework of support vector machines (SVMs). The analysis is performed in two stages. First, a market model is used to assess the probability of default for listed companies and classify them into risk groups under different risk-taking scenarios. Risk assessment and classification models are then developed using linear and nonlinear support vector machines, as well as a recently developed innovative additive SVM model that suits well the requirements of credit rating systems. Logistic regression is also employed for comparative purposes and feature selection. The developed models are applied to a sample of non-listed firms. The comparison against traditional credit scoring models fitted on historical default data shows that the market-based modeling approach provides very competitive results. Among, the machine learning techniques used in the analysis, the additive SVM model provides the best results.

The rest of article is organized as follows. Section 2 presents the market model used in the analysis as well as the SVM formulations used for constructing the credit risk assessment models. Section 3 is devoted to the empirical analysis, including the presentation of the data and the obtained results. Finally, Section 4 concludes the paper, summarizes the main findings of this research, and proposes some future research directions.