The effect of feature selection on financial distress prediction

Liang, Deron; Tsai, Chih‐Fong; Wu, Hsin-Ting

doi:10.1016/j.knosys.2014.10.010

Cited by 170 publications

(100 citation statements)

References 36 publications

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“…Our findings are in strong correlation with findings of other research studies confirming the need to develop specific models for different countries [3]. Additionally also new indicators, not only financial, should be included for forecasting in such surroundings [31].…”

Section: Resultssupporting

confidence: 89%

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Modelling bankruptcy prediction models in Slovak companies

Kováčová

Klieštiková

2017

SHS Web Conf.

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Abstract. An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression) and early artificial intelligence models (e.g. artificial neural networks), there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest) to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

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Section: Resultssupporting

confidence: 89%

“…This is given by the fact that since different economic environments have various properties that do not allow reusing models and related sets of factors in other conditions. This fact has been also confirmed by comparative studies of models for different countries [3,4].…”

Section: Introductionsupporting

confidence: 61%

Modelling bankruptcy prediction models in Slovak companies

Kováčová

Klieštiková

2017

SHS Web Conf.

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“…It also means that the filter approach is more applicable than the embedded approach to be used in the selection procedure for optimal feature subset to some extent. These results are consistent with Liang et al [37], who compared filter and wrapper feature selection methods for credit scoring and bankruptcy application and found the filter based feature selection for credit scoring to be the optimal feature selection method.…”

Section: Comparisons Of the Ten Best Credit Classifierssupporting

confidence: 90%

Chinese Small Business Credit Scoring: Application of Multiple Hybrids Neural Network

Chi¹,

Abedin²,

Moula³

2017

IJDTA

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“…Baseline is the classifier without feature selection. Classifiers used in [22] include: Linear SVM, CART, k-NN, Naïve Bayes, MLP. Filter methods include: t-test, Linear Discriminant analysis (LDA), Logistic regression (LR).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Credit scoring with a feature selection approach based deep learning

Nguyen

2016

MATEC Web of Conferences

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Abstract. In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant's credit score from the applicant's input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.

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The effect of feature selection on financial distress prediction

Cited by 170 publications

References 36 publications

Modelling bankruptcy prediction models in Slovak companies

Modelling bankruptcy prediction models in Slovak companies

Chinese Small Business Credit Scoring: Application of Multiple Hybrids Neural Network

Credit scoring with a feature selection approach based deep learning

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