Two-step classification method based on genetic algorithm for bankruptcy forecasting

Zelenkov, Yuri; Fedorova, Elena; Chekrizov, Dmitry

doi:10.1016/j.eswa.2017.07.025

Cited by 79 publications

(69 citation statements)

References 47 publications

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“…This limitation was in some way eliminated by designation of a predictive model that can be easily adapted to any new situation, both in terms of model architecture and the indicators used [2].…”

Section: Resultsmentioning

confidence: 99%

“…On the other side little is known about the practical application of existing models mainly because the use of existing models is limited by the conditions in which they are developed. Another question concerns the factors that can be significant for forecasting [2]. 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.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling bankruptcy prediction models in Slovak companies

Kováčová

Klieštiková

2017

SHS Web Conf.

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“…The most frequently used methods for studying stock survivability include genetic fuzzy models (Kuo et al 2001), artificial networks (Zhang et al 1999), genetic algorithms (Zelenkov et al 2017), and neural networks and deep learning networks (Ticknor 2013; Chong et al 2017). Other traditional statistical models have also been proposed, such as multivariate discriminant analysis and logistic regression (Beaver 1966; Altman 1968; Shumway 2001; Mossman et al 1998; Lee et al 1996).…”

Section: Introductionmentioning

confidence: 99%

Characterisation of survivability resilience with dynamic stock interdependence in financial networks

Tang¹,

Khoja²,

Heinimann³

2018

Appl Netw Sci

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This paper examines the dynamic evolutionary process in the London Stock Exchange and uses network statistical measures to model the resilience of stock. A large historical dataset of companies was collected over 40 years (1977-2017) and conceptualised into weighted, temporally evolving and signed networks using correlation-based interdependences. Our results revealed a “fission-fusion” market growth in network topologies, which indicated the dynamic and complex characteristics of its evolutionary process. In addition, our regression and modelling results offer insights for construction a “characterisation tool” which can be used to predict stocks that have delisted and continuing performance relatively well, but were less adequate for stocks with normal performance. Moreover, the analysis of deviance suggested that the survivability resilience could be described and approximated by degree-related centrality measures. This study introduces a novel alternative for looking at the bankruptcy in the stock market and is potentially helpful for shareholders, decision- and policy-makers.

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“…Next, Zelenkov et al [22] proposed a two-step classification method for bankruptcy prediction. In the first stage, training of individual classifiers and the selection of an adequate feature set is made for each of classifier.…”

Section: Related Workmentioning

confidence: 99%

“…In chemical and biomedical engineering, protein detection [17], and disease diagnoses [18] are the most common topics related to imbalanced data. In business management, bankruptcy prediction [19][20][21][22] and fraud detection [11,23] are two very attractive topics. Bankruptcy prediction is a model to forecast the fate of firms and has a great utility for all economic stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset

Lee

Park

et al. 2018

Symmetry

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Abstract:In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders, governments, and economic stakeholders. Due to the number of bankrupt companies compared to that of non-bankrupt companies, bankruptcy prediction faces the problem of imbalanced data. This study first presents the bankruptcy prediction framework. Then, five oversampling techniques are used to deal with imbalance problems on the experimental dataset which were collected from Korean companies in two years from 2016 to 2017. Experimental results show that using oversampling techniques to balance the dataset in the training stage can enhance the performance of the bankruptcy prediction. The best overall Area Under the Curve (AUC) of this framework can reach 84.2%. Next, the study extracts more features by combining the financial dataset with transaction dataset to increase the performance for bankruptcy prediction and achieves 84.4% AUC.

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Two-step classification method based on genetic algorithm for bankruptcy forecasting

Cited by 79 publications

References 47 publications

Modelling bankruptcy prediction models in Slovak companies

Modelling bankruptcy prediction models in Slovak companies

Characterisation of survivability resilience with dynamic stock interdependence in financial networks

Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset

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