Data Sample Selection Issues for Bankruptcy Prediction

Tian, Shaonan; Yu, Yan; Zhou, Ming

doi:10.1002/rhc3.12071

Cited by 16 publications

(10 citation statements)

References 35 publications

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“…Deep analyses were taken for exploiting the statistical methods such as Partial Least Square Discriminant Analysis (PLS-DA) [7]. Multicollinearity [8] strategies were also utilized to meet the requirements of econometrics with better data availability.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm

Saladi¹,

Yarlagadda²

2021

RIA

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The rapid advancements made in Information Technologies (IT) have evolved in the prediction model for financial data. Research in the prediction of bankruptcy is inclining to owe to the growth of related associations with economic and social phenomena. Financial crises in recent scenarios have influenced the growth of financial institutions. Hence, the need for bankruptcy risk prediction at an earlier stage is of prime importance. Though several prediction algorithms were suggested, the predictive models' accuracy is still a challenging task. In this paper, a bankruptcy prediction model is developed by integrating the Fuzzy clustering model and Multi-objective random forest classifiers. The voluminous number of records of the financial dataset and polish bankruptcy dataset is collected from a public repository. It is pre-processed using a MapReduce technique, one of the Big Data approaches. Benefits given by big data approaches help to achieve better flexibility towards a variable declaration. The collected records are pre-processed and organized under efficient index construction. FCM is employed to cluster the data for analytic purposes. Finally, a multi-objective Random Forest classifier helps to develop a prediction model for bankruptcy. Experimental analysis is carried out with accuracy, precision, sensitivity, and specificity compared with existing, Genetic algorithms. Compared to the existing technique, the proposed technique has obtained 80% accuracy.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm

Saladi¹,

Yarlagadda²

2021

RIA

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“…Having established this definition of failure, it is necessary to set guidelines for constructing the data samples from which to extract the models. The data collection is central to any prediction model because it informs the preliminary investigation of the data and also facilitates effective model construction (Tian et al, 2015). The review of prior literature reveals two main approaches to data collection.…”

Section: Data Sample Selectionmentioning

confidence: 99%

“…Yet a balanced distribution also can have detrimental effects on data size, due to the scarcity of bankrupt firms, inconsistent bankruptcy rates and a lack of accessibility to these firms' information, such that it is difficult and costly to gather information about failed firms (Tian et al, 2015). For a data set built using a balanced distribution, in which failed firms are paired with firms that did not fail, the capacity to collect failed firms becomes a key condition for the data size.…”

Section: Data Sample Selectionmentioning

confidence: 99%

Corporate failure prediction models in the twenty-first century: a review

Veganzones

Séverin

2020

EBR

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Purpose Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes corporate failure prediction models for the twenty-first century. Design/methodology/approach This paper gathers information from 106 published articles that contain corporate failure prediction models. The focus of the analysis is on the elements needed to design corporate failure prediction models (definition of failure, sample approach, prediction methods, variables and evaluation metrics and performance). The in-depth review creates a synthesis of current trends, from the view of those elements. Findings Both consensus and divergences emerge regarding the design of corporate failure prediction models. On the one hand, authors agree about the use of bankruptcy as a definition of failure and that at least two evaluation metrics are needed to examine model performance for each class, individually and in general. On the other hand, they disagree about data collection procedures. Although several explanatory variables have been considered, all of them serve as complements for the primarily used financial information. Finally, the selection of prediction methods depends entirely on the research objective. These discrepancies suggest fundamental advances in discovery and establish valuable ideas for further research. Originality/value This paper reveals some caveats and provides extensive, comprehensible guidelines for corporate failure prediction, which researchers can leverage as they continue to investigate this critical financial subject. It also suggests fruitful directions to develop further experiments.

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“…Piri et al [58] use a synthetic informative minority oversampling algorithm to enhance SVM performance with an imbalanced dataset. Tian et al [59] claim that different sampling techniques are required depending on the purpose of the study. Song and Peng [60] suggest a multi-criteria decision making-based approach.…”

Section: Other Studiesmentioning

confidence: 99%

Corporate Default Predictions Using Machine Learning: Literature Review

2020

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Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.

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Data Sample Selection Issues for Bankruptcy Prediction

Cited by 16 publications

References 35 publications

An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm

An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm

Corporate failure prediction models in the twenty-first century: a review

Corporate Default Predictions Using Machine Learning: Literature Review

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