CMARS and GAM &amp; CQP—Modern optimization methods applied to international credit default prediction

Sezgin, Özge; Büyükbebeci, Erkan; Çekiç, Ayşegül Işcanoğlu; Özkurt, Fatma Yerlikaya; Taylan, Pakize; Weber, Gerhard‐Wilhelm

doi:10.1016/j.cam.2010.04.039

Cited by 32 publications

(13 citation statements)

References 18 publications

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“…The CMARS method has such a comparative performance to that of MARS, [30][31][32] follow-up successful improvements on it have appeared soon. [33][34][35][36] In this work, we consider CMARS as a potential solution for learning to rank and analyze its performance for the problem.…”

Section: Conic Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Altinok

Karagöz

Batmaz

2020

Computational Intelligence

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Learning to rank is a supervised learning problem that aims to construct a ranking model for the given data. The most common application of learning to rank is to rank a set of documents against a query. In this work, we focus on point-wise learning to rank, where the model learns the ranking values. Multivariate adaptive regression splines (MARS) and conic multivariate adaptive regression splines (CMARS) are supervised learning techniques that have been proven to provide successful results on various prediction problems. In this article, we investigate the effectiveness of MARS and CMARS for point-wise learning to rank problem. The prediction performance is analyzed in comparison to three well-known supervised learning methods, artificial neural network (ANN), support vector machine, and random forest for two datasets under a variety of metrics including accuracy, stability, and robustness. The experimental results show that MARS and ANN are effective methods for learning to rank problem and provide promising results.

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Section: Conic Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Altinok

Karagöz

Batmaz

2020

Computational Intelligence

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“…GAMs have already been applied in various analyses of creditworthiness (Alp et al, 2010;Burkhard & de Giorgi, 2006) and in bankruptcy predictions (Berg, 2007;Cheng, Chu, & Hwang, 2010;Dakovic, Czado, & Berg, 2010;Hwang, Cheng, & Lee, 2007;Lohmann & Ohliger, 2017). The validity measures that comparable studies apply are based either on likelihood or on classification.…”

Section: Literature Reviewmentioning

confidence: 99%

The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models

Lohmann

Ohliger²

2018

Journal of Forecasting

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This study examines whether the evaluation of a bankruptcy prediction model should take into account the total cost of misclassification. For this purpose, we introduce and apply a validity measure in credit scoring that is based on the total cost of misclassification. Specifically, we use comprehensive data from the annual financial statements of a sample of German companies and analyze the total cost of misclassification by comparing a generalized linear model and a generalized additive model with regard to their ability to predict a company's probability of default. On the basis of these data, the validity measure we introduce shows that, compared to generalized linear models, generalized additive models can reduce substantially the extent of misclassification and the total cost that this entails. The validity measure we introduce is informative and justifies the argument that generalized additive models should be preferred, although such models are more complex than generalized linear models. We conclude that to balance a model's validity and complexity, it is necessary to take into account the total cost of misclassification.

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“…In this paper we use a structured additive regression model to analyze how a firm's age interacts with its accounting‐based key performance indicators and how this interaction affects the prediction of bankruptcy. A number of studies have applied GAMs to examine creditworthiness (Alp et al, ; Burkhard & de Giorgi, ) and bankruptcy prediction (Berg, ; Cheng, Chu, & Hwang, ; Dakovic, Czado, & Berg, ; Hwang, Cheng, & Lee, ; Lohmann & Ohliger, , ). However, these studies focus on comparing several empirical models from a strictly statistical perspective and do not analyze or describe existing interaction effects between a firm's age and its accounting‐based key performance indicators.…”

Section: Introductionmentioning

confidence: 99%

Using accounting‐based information on young firms to predict bankruptcy

Lohmann

Ohliger²

2019

Journal of Forecasting

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This study analyzes the nonlinear relationships between accounting‐based key performance indicators and the probability that the firm in question will become bankrupt or not. The analysis focuses particularly on young firms and examines whether these nonlinear relationships are affected by a firm's age. The analysis of nonlinear relationships between various predictors of bankruptcy and their interaction effects is based on a structured additive regression model and on a comprehensive data set on German firms. The results of this analysis provide empirical evidence that a firm's age has a considerable effect on how accounting‐based key performance indicators can be used to predict the likelihood that a firm will go bankrupt. More specifically, the results show that there are differences between older firms and young firms with respect to the nonlinear effects of the equity ratio, the return on assets, and the sales growth on their probability of bankruptcy.

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CMARS and GAM & CQP—Modern optimization methods applied to international credit default prediction

Cited by 32 publications

References 18 publications

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

Learning to rank by using multivariate adaptive regression splines and conic multivariate adaptive regression splines

The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models

Using accounting‐based information on young firms to predict bankruptcy

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