A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China

Chai, Nana; Wu, Bi; Yang, Wenxu; Shi, Baofeng

doi:10.1080/1540496x.2019.1577237

Cited by 46 publications

(35 citation statements)

References 36 publications

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“…e second part is the study of false financial statement identification based on FCM algorithm, reviewing and evaluating the current situation of financial falsification at home and abroad, the determination of financial falsification model samples and indicators, analyzing the problem that the basic FCM algorithm is sensitive to noise and isolated points, then analyzing three related algorithms for affiliation correction, and explaining the defects of the algorithms. And finally, a brief description of other algorithms for affiliation correction is given [5]. Firstly, the research objective of this paper is determined, and then a sample financial fraud model is established.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] False Financial Statement Identification Based on Fuzzy C‐Means Algorithm

2021

Complexity

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Financial accountants falsify financial statements by means of financial techniques such as financial practices and financial standards, and when compared with conventional financial data, it is found that the falsified financial data often lack correlation or even contradict each other in terms of financial data indicators. At the same time, there are also inherent differences in reporting patterns from conventional financial data, but these differences are difficult to test manually. In this paper, the fuzzy C-means (FCM) clustering method is used to amplify these differences and thus identify false financial statements. In the proposed algorithm, firstly, the normalization constraint of the FCM clustering algorithm on the sum of affiliation of individual samples is relaxed to the constraint on the sum of affiliation of all samples, thus reducing the sensitivity of the algorithm to noise and isolated points; secondly, a new affiliation correction method is proposed to address the problem that the difference in affiliation is too large after the relaxation of the constraint. In the discussion of this paper, most of the information comes from the annual reports of companies, administrative penalty decisions of the Securities Regulatory Commission, and some information comes from research reports made by securities institutions, which are limited sources of information. The proposed method can correct the affiliation to a reasonable range, effectively avoiding the problem that some samples have too much affiliation and become a class of their own and also avoiding the problem that it is difficult to choose the termination threshold of the algorithm iteration due to too little affiliation, and can ensure that the constraint on the sum of affiliation of all samples is always satisfied during the iteration of the algorithm. The method has the characteristics of high recognition accuracy and has the significance of theoretical method innovation.

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Section: Introductionmentioning

confidence: 99%

[Retracted] False Financial Statement Identification Based on Fuzzy C‐Means Algorithm

2021

Complexity

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“…One of the most relevant findings in this cluster is to demonstrate the effectiveness of non-financial factors related to product innovation (patents and brand products) as predictive variables together with financial ratios, macroeconomic indicators, and some characteristics of legal representatives such as age, gender and the value of their real estate properties (Chi and Meng 2018 ; Yu et al 2019 ). Few recent studies in this cluster try to solve the methodological issues generated by the need to combine different sources of information to assess SME default risk better, suggesting the use of machine learning/non-linear programming tools such as MCDA (Corazza et al 2016 ; Gonçalves et al 2016 ), fuzzy clustering (Chai et al 2019 ), neural networks (Giannopoulos and Aggelopoulos 2019 ), non-linear programming with maximum discriminating power of credit scores (Chi et al 2019 ) and cognitive mapping (Oliveira et al 2017 ).…”

Section: Results Of the Vos Analysis And The Systematic Literature Rementioning

confidence: 99%

Rethinking SME default prediction: a systematic literature review and future perspectives

et al. 2021

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Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.

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“…At the same time, the experimental results of Stornetta and Huberman show that when the weights are adjusted in the range of [−0.5, 0.5], the state value of each neuron is close to zero. As a result, the convergence time will be shortened by approximately 30% to 50% [19].…”

Section: Application Analysis Of the Bp Neural Network In En-mentioning

confidence: 99%

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Peng

2021

Scientific Programming

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In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

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A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China

Cited by 46 publications

References 36 publications

[Retracted] False Financial Statement Identification Based on Fuzzy C‐Means Algorithm

[Retracted] False Financial Statement Identification Based on Fuzzy C‐Means Algorithm

Rethinking SME default prediction: a systematic literature review and future perspectives

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

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