Bankruptcy Prediction Using Cerebellar Model Neural Networks

Chung, Chang-Chih; Chen, Tsung-Shih; Lin, Lee-Hsuan; Lin, Yuchen; Lin, Chih‐Min

doi:10.1007/s40815-015-0121-5

Cited by 24 publications

(9 citation statements)

References 18 publications

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“…When using multivariate analysis, in literature, we often encounter also called mathematicalstatistical methods of prediction, Chung et al (2016). Since the model, in this case, is Prediction financial stability of Romanian production companies through Altman Z-score Authors: Anna Siekelova, Erika Kovalova, Florin Cristian Ciurlău constructed based on data and not based on expert evaluation, we can also mark them as exact.…”

Section: Methodsmentioning

confidence: 99%

Prediction Financial Stability of Romanian Production Companies Through Altman Z-Score

Siekelová¹,

Kovalová²,

Ciurlău³

2019

EMS

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Nowadays, there is a much higher demand for financial and economic analyses in companies that help to face the challenges they face. The analysis is a tool for research, and evaluation of business transformation processes, conditions under which they take place. It also examines the impacts and causal relationships that take place here. Under the current economic conditions, it is necessary to apply new adequate methods in corporate governance that meet market needs and requirements. Prognosis of the development of the financial situation of the company, i.e., the forecast of its future solvency, or insolvency, is based on the evaluation and interpretation of the results achieved today. Financial analysis of the company plays a vital role. The main aim of the research paper is to predict the financial health of the company using Altman's Z score. The work consists of the theoretical part of the financial-economic analysis. In the second chapter, we defined the method and indicators by which we will analyse the selected production companies in Romania. We managed to obtain data from 105 manufacturing companies that have been on the market for at least five years for the analysis, and we focused on the last three years of their activity using Altman's Z score. In the results of the work, we specifically evaluated the financial health of the company using the chosen method. At the end of the paper, we generally evaluated the achieved results by analysis. All the data used in the processing were extracted from professional book literature and provided corporate resources, especially from the balance sheet and profit and loss account.

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Section: Methodsmentioning

confidence: 99%

Prediction Financial Stability of Romanian Production Companies Through Altman Z-Score

Siekelová¹,

Kovalová²,

Ciurlău³

2019

EMS

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“…Abid et al (2022) used neural networks combined with 30 financial ratios to predict bankruptcy for 856 French companies from the industrial sector. Other authors who deal with bankruptcy prediction using a neural network include Tsai and Wu (2008), Tsai (2009), Salehi and Davoudi Pour (2016), Chung et al (2016), andKim et al (2018). Charambous et al (2000) compared logistic regression with BP ANN on a sample of 139 US companies.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Gavurová

Jenčová

Bačík

et al. 2022

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Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.

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“…Compared with other neural networks, CMAC is advantageous insofar that it has fast learning properties, simple computations, and good generalization capabilities [13]. In the past decade, CMAC has been applied to various fields, such as control systems [14][15][16][17], classification systems [18][19][20], signal processing [21][22][23], and image processing [24,25]. Due to the work of Zadeh [26], fuzzy modeling and fuzzy control have attracted many researchers since said methods can be used to convert problems into simple human terms.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Lin

Quynh

et al. 2020

Mathematics

Self Cite

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In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

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Bankruptcy Prediction Using Cerebellar Model Neural Networks

Cited by 24 publications

References 18 publications

Prediction Financial Stability of Romanian Production Companies Through Altman Z-Score

Prediction Financial Stability of Romanian Production Companies Through Altman Z-Score

Artificial intelligence in predicting the bankruptcy of non-financial corporations

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

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