The Classification of Financial Distress Prediction Patterns in Sinopec Corp. and Its Subsidiaries Based on Self-Organizing Map Neural Network

2011 ◽  
Author(s):  
Yu Dong ◽  
Tao Sun
Keyword(s):  
Neural Network ◽  
Financial Distress ◽  
Self Organizing Map ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Self Organizing

Classification of salt-contaminated velocities with self-organizing map neural network

2002 ◽  
Vol 21 (12) ◽  
pp. 1193-1196 ◽  
Author(s):  
Lin Zhang ◽  
Al Fortier ◽  
David C. Bartel
Keyword(s):  
Neural Network ◽  
Self Organizing Map ◽  
Self Organizing

Genetic Neural Network Based Financial Distress Prediction

2014 ◽  
Vol 933 ◽  
pp. 921-925
Author(s):  
Xin Yun Liu ◽  
Heng Jun Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Financial Distress ◽  
Structural Parameters ◽  
Optimal Structure ◽  
Structure Parameters ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Genetic Neural Network

Enterprise financial distress prediction based on neural network has some disadvantages, such as complex structure, slow convergence rate and easily falling into local minimum points. The paper presents the genetic neural network based enterprise financial distress prediction. Firstly, the structural parameters of neural network model are encoded and connected into gene sequence to obtain an individual. A certain number of individuals make up a population. Secondly, after the reproduction, crossover and mutation operations upon the population, the best individual, that is the optimal structure parameters of neural network model, is obtained. Finally, the neural network model with the optimal structure parameters is trained by the training samples and the trained neural network model can realize enterprise financial distress prediction. The testing results show that the method achieves higher training speed and lower error rate.

scholarly journals Financial Distress Prediction Using GA-BP Neural Network Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1
Author(s):  
Lei Ruan ◽  
Heng Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Financial Distress ◽  
Test Model ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Bp Neural Network Model

Financial distress prediction, the crucial link of enterprise risk management, is also the core of enterprise financial distress theory. With currently global economic recession and the gradual perfection of artificial intelligence technology, the study in this paper begins by optimizing the back-propagation (BP) neural network model using the genetic algorithm (GA). In doing so, it can overcome the deficiency that the BP neural network model is slow in convergence and easily trapped into local optimal solution. The study then conducts training and tests on the optimized GA-BP neural network model, using financial distress data from Chinese listed enterprises. As can be seen from the experimental results, the optimized GA-BP neural network model is significantly improved in terms of the accuracy and stability in financial distress prediction. The study in this paper not only provides an effective test model for the automatic recognition and early warning of enterprise financial distress, but also contributes to new thoughts and approaches for the application of artificial intelligence in the field of financial accounting.

scholarly journals Comparison of the models of financial distress prediction

2013 ◽  
Vol 61 (7) ◽  
pp. 2587-2592 ◽  
Author(s):  
Jiří Omelka ◽  
Michaela Beranová ◽  
Jakub Tabas
Keyword(s):  
Financial Distress ◽  
Future Research ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
The Sixties ◽  
Business Entities ◽  
A Company ◽  
Critical Aspects

Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman’s model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company.The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company’s financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.

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