Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP

2010 ◽  
Vol 23 (8) ◽  
pp. 823-831 ◽  
Author(s):  
P. Ravisankar ◽  
V. Ravi
Keyword(s):  
Neural Network ◽  
Financial Distress ◽  
Fuzzy Artmap ◽  
Group Method ◽  
Data Handling ◽  
Financial Distress Prediction ◽  
Distress Prediction

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.

An Analysis of Financial Distress Prediction of Selected Listed Companies in Colombo Stock Exchange

2021 ◽  
Vol 13 (2) ◽  
pp. 48-70
Author(s):  
Kennedy Degaulle Gunawardana
Keyword(s):  
Neural Network ◽  
Financial Distress ◽  
Stock Exchange ◽  
Sri Lankan ◽  
Cross Sectional ◽  
Listed Firms ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Distressed Firms ◽  
One Year

The main objective of the study is to predict financial distress and developing a prediction model using accounting related variables in selected listed firms in Sri Lanka. Decision criteria for financial distress has been selected based on the existing literature on financial distress prediction applicable to the Sri Lankan firms. A sample of 22 financially distressed firms along with 33 financially non-distressed firms have been used to conduct this study. Artificial neural network was used as the basic approach to the study in predicting financial distress. A neural network to predict financial distress was developed with an accuracy of 85.7% one year prior to its occurrence. The second analysis conducted was the panel regression considering five years of cross-sectional data for the sample of companies selected. This analysis was able to identify a significant relationship of leverage, price-to-book ratio and Tobin's Q ratio to the prediction of financial distress of a firm.

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