Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach

Tang, Tseng‐Chung; Chi, Li‐Chiu

doi:10.1016/j.eswa.2005.03.003

Cited by 40 publications

(32 citation statements)

References 33 publications

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“…Seminal work on early warning signs has been largely summarised by academics such as Cannon and Edmundson (2005), Collard (2002), Lohrke, Bedeian and Palmer (2004), McGurr and DeVaney (1998), Moncraz and Kron (1993), Sharma (2001), Sharma and Mahajan (1980), Stead and Smallman (1999) and Tang and Chi (2005). It appears that these authors use the following phrases inconsequently to describe 'early warning signs': success versus failure variables, causes of decline and or failure, warning indicators for business decline, performing and non-performing variables, root causes for decline or failure, warning indicators, material defects, external and internal factors, distress variables, problems and challenges.…”

Section: Early Warning Signs Theorymentioning

confidence: 99%

Business rescue decision making through verifier determinants – ask the specialists

Pretorius

Holtzhauzen²

2013

SAJEMS

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Orientation: Business rescue has become a critical part of business strategy decision making, especially during economic downturns and recessions. Past legislation has generally supported creditor-friendly regimes, and its mind-set still applies, which increases the difficulty of such turnarounds. There are many questions and critical issues faced by those involved in rescue. Despite extensive theory in the literature on failure, there is a void regarding practical verifiers of the signs and causes of venture decline, as specialists are not forthcoming about what they regard as their 'intellectual property'.Research purpose: This article introduces the concept and role of 'verifier determinants' of early warning signs, as a tool to confirm the causes of decline in order to direct rescue strategies and, most importantly, reduce time between the first observation and the implementation of the rescue.Motivation for the study: Knowing how specialists confirm causes of business decline in practice could assist in analysis and deciding on strategies for the rescue earlier than can be done using traditional due diligence, which is time consuming. Reducing time is a crucial element of a successful rescue.Research design and approach: The researchers interviewed specialists with extensive experience in credit for rescue and turnaround. An experimental design was used to ensure the specialists evaluated the same real cases to extract their experiences and base their decisions on.Main findings: The specialists confirmed the use of verifier determinants and identified such determinants as they personally used them to confirm causes of decline. These verifier determinants were classified into five categories namely, management, finance, strategic, banking and operations and marketing of the ventures under investigation. The verifier determinants and their use often depend heavily on subconscious (non-factual) information based on previous experiences, rendering them 'irrational' in modern management perspectives.Practical/managerial implications: Decision makers and affected persons could benefit from the insights obtained through this study. Confirming early warning signs through verifier determinants would be beneficial for entrepreneurs who are creditors, company directors, rescue practitioners, government regulators, court officials and educators alike.Contribution/value add: Knowing the verifier determinants could assist decision making and improve the effectiveness of rescue strategies. Business rescue practitioners can improve their 'investigation of the affairs' activity by using such verifier determinants.

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Section: Early Warning Signs Theorymentioning

confidence: 99%

Business rescue decision making through verifier determinants – ask the specialists

Pretorius

Holtzhauzen²

2013

SAJEMS

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“…PCA is mainly used in combination with neural networks (Mensah, 1984;Zavgren, 1985;Gombola et al 1987;Skogsvik 1990;Alici, 1996;Sharma and Iselin, 2003;Shin and Lee, 2003;Wang, 2004;Canbas et al, 2005;Min and Lee, 2005;Tang and Chi, 2005;Shin et al, 2006;Sookhanaphibarn et al, 2007;Yao, 2007. Ravi andPramodh, 2008;Chen and Du 2009;Balas et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Bankruptcy of Belgian SMEs: A Hybrid Approach Based on Factorial Analysi

Cultrera¹,

Croquet²,

Jospin³

2017

IBR

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The aim of this paper is to verify the relevance of technical data analysis which seems to be useful for identifying predictors of bankruptcy of Belgian SMEs. To do so, a sample of 1,860 Belgian companies, including healthy and bankrupt firms, was used. The sample was constituted using Belfirst software (2015). A mixed method data analysis, coupling the Ward aggregation criterion, the method of mobile centres and principal component analysis, was performed on the variables commonly cited in the literature as predictive of corporate bankruptcies. The results of this study show that the use of these methods is not relevant in the context of bankruptcy prediction using this sample, but the results of the logistic regressions did not question the discriminatory power of the introduced active variables.

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“…Numerous classification techniques have been adopted for credit quality assessments. These techniques include discriminant analysis, logistic regression, Bayesian network, k-nearest neighbors, decision trees (Yobas et al, 2000), neural networks (Yobas et al [16]; Tang and Chi [14]; Abdou et al [1]), and support vector machines (Huang et al [10]; Chen and Shih [5]). …”

Section: Introductionmentioning

confidence: 98%

Credit quality assessments using manifold based semi-supervised discriminant analysis and support vector machines

Huang

2011

2011 Seventh International Conference on Natural Computation

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Due to the large scale of financial data in credit quality forecasting, dimensionality reduction is a key step to enhance classifier performance. By using manifold based semisupervised discriminant analysis (SSDA) and support vector machines, this study develops a novel prediction system for credit quality assessment, where SSDA makes efficient use of labeled and unlabeled (testing) data points to gain a perfect low dimensional approximation of data manifold and simultaneously maintain the discriminating power. More specifically, the labeled data points are used to maximize the separability between different classes, and the testing data points are used to estimate the intrinsic geometric structure of the data space. Empirical results indicate that SSDA outperforms other dimensionality reduction methods with a significant performance improvement, and our hybrid classifier substantially outperforms other conventional classifiers.

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Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach

Cited by 40 publications

References 33 publications

Business rescue decision making through verifier determinants – ask the specialists

Business rescue decision making through verifier determinants – ask the specialists

Predicting Bankruptcy of Belgian SMEs: A Hybrid Approach Based on Factorial Analysi

Credit quality assessments using manifold based semi-supervised discriminant analysis and support vector machines

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