The effects of handling outliers on the performance of bankruptcy prediction models

Nyitrai, Tamás; Virág, Miklós

doi:10.1016/j.seps.2018.08.004

Cited by 66 publications

(36 citation statements)

References 41 publications

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“…The impact of relating stock balance sheet items to flow profit-and-loss statement items on the performance of bankruptcy prediction models was in-depth researched by Nyitrai (2017). The effects of handling outliers on model performance in different manners were examined by Nyitrai and Virág (2019). It was concluded that categorisation by Chi-square automatic interaction detection (CHAID) decision trees more effectively handled outliers than coercing by external percentiles or by the mean ± different standard deviations.…”

Section: The Challenges Of Data Transformations and Methods Combinationsmentioning

confidence: 99%

A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary

Kristóf

Virág

2020

JRFM

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The article provides a comprehensive review regarding the theoretical approaches, methodologies and empirical researches of corporate bankruptcy prediction, laying emphasis on the 30-year development history of Hungarian empirical results. In ex-socialist countries corporate bankruptcy prediction became possible more than 20 years later compared to the western countries, however, based on the historical development of corporate bankruptcy prediction after the political system change it can be argued that it has already caught up to the level of international best practice. Throughout the development history of Hungarian bankruptcy prediction, it can be tracked how the initial, small, cross-sectional sample and classic methodology-based bankruptcy prediction has evolved to today’s corporate rating systems meeting the requirements of the dynamic, through-the-cycle economic capital calculation models. Contemporary methodological development is characterized by the domination of artificial intelligence, data mining, machine learning, and hybrid modelling. On the basis of empirical results, the article draws several normative proposals how to assemble a bankruptcy prediction database and select the right classification method(s) to accomplish efficient corporate bankruptcy prediction.

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Section: The Challenges Of Data Transformations and Methods Combinationsmentioning

confidence: 99%

A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary

Kristóf

Virág

2020

JRFM

Self Cite

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“…Winsorization has been used on regression problems using deterministic neural networks [38,39]. Asymptotic properties of trimmed and Winsorized M and Z estimators have been investigated and trimmed M-estimators have been used for robust estimation in neural networks [40].…”

Section: Related Literaturementioning

confidence: 99%

Winsorization for Robust Bayesian Neural Networks

Sharma

Chatterjee

2021

Entropy

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With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.

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“…There is also the question of the verification of these methods, whereby, based on the assessment of bankruptcy model performance (discriminant analysis, logistic regression, and multilayer perceptron network), decision trees appear to be the most efficient [58], as well as the success rate of the bankruptcy models, which, according to Kubenka and Myskova [59], is lower in the overall comparison of the three methods than the researchers stated.…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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The effects of handling outliers on the performance of bankruptcy prediction models

Cited by 66 publications

References 41 publications

A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary

A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary

Winsorization for Robust Bayesian Neural Networks

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

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