Introduction

Nisbet, Bob

doi:10.1016/b978-0-12-416632-5.09979-5

Cited by 1 publication

(5 citation statements)

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“…Criteria are variables that provide the best separation of the individuals in a class, containing the largest possible proportion of individuals (Tufféry & Tufféry, 2011). The result is a network of questions that forms a treelike structure with the ends of the tree as “leaf” nodes (Nisbet, 2017). Financial ratios are the most common criteria/variables (Korol, 2013).…”

Section: Methodsmentioning

confidence: 99%

“…Fuzzy logic does not replace other conventional or machine-learning statistical methods. It rather adds rules induction applicable in decision systems or processes combined with other statistical methods (Nisbet, 2017). See Korol (2018) for fuzzy logic applied to financial ratios.Machine learning methods:Neural network: an algorithm inspired by neurons (units) and their synapses (weights).…”

Section: Methodsmentioning

confidence: 99%

“…The learning occurs in the hidden layer(s). It expresses a nonlinear function by assigning weights to the input variables to produce an output value (Nisbet, 2017). A neural network can handle a high amount of input variables, both traditional; financial and non-traditional, structure and ownership (Jones, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…However, it differs in learning. Contrary to a neural network, during learning, the decision tree method conveys effects by developing methods to find rules that allow the evaluation of input values for categorizing them into distinct groups, without directly expressing the functional relationship (Nisbet, 2017). There are various algorithms applied to decision trees (Sun et al.…”

Section: Methodsmentioning

confidence: 99%

“…Support vector machine: the method is based on a concept of decision planes that define decision boundaries. In their simplest form, such boundaries resemble a separating line which ideally separates objects with different class memberships (Nisbet, 2017). Therefore, some authors call the lines margin separators (Russell & Norvig, 2021).…”

Section: Methodsmentioning

confidence: 99%

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A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors: 2010–2022

Soukal,

Mačí,

Trnková

et al. 2023

CEMJ

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PurposeThe primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.Design/methodology/approachThe authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.FindingsThis paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.Research limitations/implicationsThe authors identify several gaps in the literature which this research does not address but could be the focus of future research.Practical implicationsThe authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.Social implicationsBankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.Originality/valueTo the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

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Section: Methodsmentioning

confidence: 99%