Credit scoring with macroeconomic variables using survival analysis

Bellotti, Tony; Crook, Jonathan

doi:10.1057/jors.2008.130

Cited by 181 publications

(116 citation statements)

References 20 publications

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“…the strong do not become stronger. In fact in our experiment the performance of the best performing two-class classifier, logistic regression, registered a small decline when oversampling was applied which matches the results of Bellotti and Crook (2009). Based on these findings, oversampling should not be employed with logistic regression as a suitable technique to address the LDP problem.…”

Section: Discussionsupporting

confidence: 74%

Using semi-supervised classifiers for credit scoring

Kennedy¹,

Namee²,

Delany³

2013

Journal of the Operational Research Society

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Section: Discussionsupporting

confidence: 74%

Using semi-supervised classifiers for credit scoring

Kennedy¹,

Namee²,

Delany³

2013

Journal of the Operational Research Society

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“…The Cox proportional hazards model was also used for bankruptcy prediction in Duffie et al [31]. T. Bellotti and J. Crook [32]) in their research reported an application of survival analysis to model default on a large data set of credit card accounts.…”

Section: Guptamentioning

confidence: 99%

A Survival Approach to Prediction of Default Drivers for Indian Listed Companies

Gupta¹

2017

TEL

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The objective of the research study is to identify the key predictors that can explain default risk for Indian listed companies using survival analysis. The author has applied the semi-parametric Cox proportional hazard model to test the impact of financial ratios, capital market ratios, macro-economic variables, size and age of companies, and the ownership structure of promoters to a dataset of 859 companies panning across 10 sectors. Unlike traditional models on default prediction, survival models focus on "time to default" as the dependent variable. The empirical findings reveal that return on capital employed (ROCE), return on net worth (ROE), interest coverage ratio, exchange rate volatility, GDP growth rate, stock index, promoters holdings % and the percent of shares pledged are all significant predictors of default. Among the market variables, it is seen that beta and the ratio of market value of equity/book value of debt are statistically significant variables in explaining default risk. The empirical findings also generate the hazard ratio for each covariate which examines the predicted change in the hazard for a unit increase in the predictor. The author extends the research by applying the marketbased KMV structural model to obtain continuous observations of default probability and regressing the same against all the 1 covariates (Gupta et al.,. It is observed that the set of significant covariates are almost common to those generated by our survival approach. The study concludes in emphasizing the significance of survival models in default prediction as unlike traditional accounting-based and market-based models, these models assess relationship between survival time and covariates. The application of survival models is strongly recommended for credit risk evaluation and modeling as structuring of loans can be done by lenders by assessing the survival times of different firms across the entire observation period being considered.

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“…For further details about building and assessment of a survival model using application and macroeconomic variables for retail credit cards, see Bellotti and Crook (2009). Models were built with behavioural variables lagged by 12 months in order to reduce the possible effect of endogeneity between behavioural data and default event (eg a rise in account balance and default may have a common external cause) and to allow for forecasts up to 12 months ahead.…”

Section: Model Fitmentioning

confidence: 99%

“…Rösch and Scheule (2008) present a general stress testing approach based on an asset correlation model that includes macroeconomic factors and show it can be used to estimate stressed loss rates, given severe economic scenarios. Bellotti and Crook (2009) …”

Section: Introductionmentioning

confidence: 99%

Retail credit stress testing using a discrete hazard model with macroeconomic factors

Bellotti

Crook

2014

Journal of the Operational Research Society

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Retail credit models are implemented using discrete survival analysis, enabling macroeconomic conditions to be included as time-varying covariates. In consequence, these models can be used to estimate changes in probability of default given downturn economic scenarios. Compared to traditional models we offer improved methodologies for scenario generation and for the use of them to predict default rates. Monte Carlo simulation is used to generate a distribution of estimated default rates from which Value at Risk and Expected Shortfall are computed as a means of stress testing. Several macroeconomic variables are considered and in particular factor analysis is employed to model the structure between these variables. Two large UK data sets are used to test this approach, resulting in plausible dynamic models and stress test outcomes.Credit card stress testing using a dynamic model with macroeconomic factors Bellotti & Crook

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Credit scoring with macroeconomic variables using survival analysis

Cited by 181 publications

References 20 publications

Using semi-supervised classifiers for credit scoring

Using semi-supervised classifiers for credit scoring

A Survival Approach to Prediction of Default Drivers for Indian Listed Companies

Retail credit stress testing using a discrete hazard model with macroeconomic factors

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