Galina Andreeva

Predicting default of a small business using different definitions of financial distress

The paper introduces a number of risk-rating models for UK small businesses applying an accounting-based approach, which uses financial ratios to predict corporate bankruptcy. An enhancement to these models is considered through features typical to retail credit risk modelling. A common problem of default prediction consists in the relatively small number of bankruptcies or real defaults available for model-building. In order to expand the ‘default’ group beyond bankrupt companies, the paper considers adopting four different definitions of ‘a failing business’ by investigating combinations of financial distress levels. The impact of each default definition on the choice of predictor variables and on the models predictive accuracy is explored. In addition, the paper examines the value of categorizing financial ratios used as predictor variables.

European generic scoring models using survival analysis

Credit scoring discriminates between ‘good’ and ‘bad’ credit risks to assist credit-grantors in making lending decisions. Such discrimination may not be a good indicator of profit, while survival analysis allows profit to be modelled. The paper explores the application of parametric accelerated failure time and proportional hazards models and Cox non-parametric model to the data from the retail card (revolving credit) from three European countries. The predictive performance of three national models is tested for different timescales of default and then compared to that of a single generic model for a timescale of 25 months. It is found that survival analysis national and generic models produce predictive quality, which is very close to the current industry standard—logistic regression. Stratification is investigated as a way of extending Cox non-parametric proportional hazards model to tackle heterogeneous segments in the population.

Modelling profitability using survival combination scores

Abstract The paper presents the first empirical investigation of the relationship between present value of net revenue from a revolving credit account and times to default and to second purchase. The analysis is based on the data for a store card which is used to buy ‘white’ durable goods in Germany. It is demonstrated that there exists a relationship between the above given measures. It appears that there is a scope for improving profit if an application for a store card is assessed by using a model which estimates the revenue and includes the survival probability of default and the survival probability of second purchase (a survival combination model) rather than merely a static probability of default predicted by a logistic regression.

Support vector regression for loss given default modelling

Loss given default modelling has become crucially important for banks due to the requirement that they comply with the Basel Accords and to their internal computations of economic capital. In this paper, support vector regression (SVR) techniques are applied to predict loss given default of corporate bonds, where improvements are proposed to increase prediction accuracy by modifying the SVR algorithm to account for heterogeneity of bond seniorities. We compare the predictions from SVR techniques with thirteen other algorithms. Our paper has three important results. First, at an aggregated level, the proposed improved versions of support vector regression techniques outperform other methods significantly. Second, at a segmented level, by bond seniority, least square support vector regression demonstrates significantly better predictive abilities compared with the other statistical models. Third, standard transformations of loss given default do not improve prediction accuracy. Overall our empirical results show that support vector regression techniques are a promising technique for banks to use to predict loss given default.

Chinese companies distress prediction: an application of data envelopment analysis

Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency. In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency—Technical Efficiency (TE), we assume Variable Returns to Scale, and decompose TE into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the level of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, while the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power of the model is improved by this corporate efficiency information.

Modelling the purchase propensity: analysis of a revolving store card

We investigate the incremental roles of information that becomes available only after a revolving loan has been granted in explaining and predicting the time taken until the borrower makes a second purchase. Using data relating to a store card, granted around the time of first purchase and used in Belgium, we find that characteristics of a first purchase and remaining credit available for use enhance the explanatory and predictive power of application characteristics. The relationship differs between good and poor payers.

Exploring Management Capability in SMEs using transactional data

Small- and medium-sized enterprises (SMEs) have become very important in most world economies. Governments have developed policies to support them and within the United Kingdom the government has encouraged lending to SMEs. Traditional relationship banking is based on the confidence a banker may have in the quality of SME’s management. Yet with a shift towards transactional quantitative risk assessment, there is a concern that Management Capability, which is critical to the success of a SME, is not necessarily captured by risk modelling. This paper reports findings from work on determining Management Capability from quantitative transactional measures. The study has used principal component analysis and partial least squares regression to elicit manifestations of Management Capability. The results indicate some success in determining measures for Management Capability.

A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models

This paper presents a cross-country comparison of significant predictors of small business failure between Italy and the UK. Financial measures of profitability, leverage, coverage, liquidity, scale and non-financial information are explored, some commonalities and differences are highlighted. Several models are considered, starting with the logistic regression which is a standard approach in credit risk modelling. Some important improvements are investigated. Generalised Extreme Value (GEV) regression is applied in contrast to the logistic regression in order to produce more conservative estimates of default probability. The assumption of non-linearity is relaxed through application of BGEVA, non-parametric additive model based on the GEV link function. Two methods of handling missing values are compared: multiple imputation and Weights of Evidence (WoE) transformation. The results suggest that the best predictive performance is obtained by BGEVA, thus implying the necessity of taking into account the low volume of defaults and non-linear patterns when modelling SME performance. WoE for the majority of models considered show better prediction as compared to multiple imputation, suggesting that missing values could be informative.

Enhancing two-stage modelling methodology for loss given default with support vector machines

We propose to incorporate least squares support vector machine technique into a two-stage modelling framework to predict recovery rates of credit cards from a UK retail bank. The two-stage model requires a classification step that discriminates the cases with recovery rate equal to 0 or 1 and a regression step to estimate recovery rates for the cases with recovery rates in (0, 1). The two-stage model with a support vector machine classifier is found to be advantageous on an out-of-time sample compared with other methods, suggesting that a support vector machine is preferred to a logistic regression as the classification technique. We further examine the predictive performances on a subset where recovery rate is bounded in (0, 1) and the empirical evidence demonstrates that support vector regression yields significant but modest improvement compared with other statistical regression models. When modelling on the whole sample, the support vector regression does not present any advantage compared with other techniques within the two-stage modelling framework. We suggest that the choice of regression models is less influential in prediction of recovery rates than the choice of classification methods in the first step of two-stage models.

Exploring the performance of Small and Medium Sized Enterprises through the Credit Crunch

Small- and medium-sized enterprises (SMEs) are major contributors to most western economies. They are often supported by government policies, and in the UK the government encourages banks to lend to them. It is generally believed that the credit crunch has had an impact on the performance of SMEs. This study looks at the impact of the crunch using large samples from 2007 through to 2010. It looks at performance by region, age and industrial sector (SIC code). It then proceeds to explore the modelling of default over the years, with a focus on young businesses. It was found that there is a degree of stability within the models, though the level of default varies across years. Young businesses, as has been found before, are shown to be more vulnerable.