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Comparison of Wavelet Network and Logistic Regression in Predicting Enterprise Financial Distress

Ming-Chang Lee, Li-Er Su
2015 International Journal of Computer Science & Information Technology (IJCSIT)  
It discussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rate and error rate (accuracy of classification, Type I error, and Type II error).  ...  Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation.  ...  ACKNOWLEDGEMENTS I would like to thank the anonymous reviewers for their constructive comments on this paper.  ... 
doi:10.5121/ijcsit.2015.7307 fatcat:f7d53ht7onaxhm3gxvmulk7dfy


Ahmadreza Ahmadreza Ghasemi, Mohsen Seyghalib, Maryam Moradi
2018 Financial and credit activity problems of theory and practice  
Four models, Support vector machine, neural network back propagation, Decision trees and Adaptive Neuro-Fuzzy Inference System has been compared.Furthermore, the ratios of liquidity considered in a period  ...  All models have the precision to predict the financial crisis.  ...  Neophytou E. et al improved a distress classification model for UK public industrial companies using logit and Neural Networks analysis.  ... 
doi:10.18371/fcaptp.v1i24.128242 fatcat:6fvhhsj66nhkrcjrrzwzy5o2ku

Control of corporate ownership in the evolutional portfolio intelligent complex optimization (EPICO) model

Nikolaos Loukeris, Iordanis Eleftheriadis
2017 Corporate Ownership and Control  
Control of corporate ownership in the evolutional portfolio intelligent complex optimization (EPICO) model. Corporate  ...  , and 74.31% of the distressed in the initial classification whilst the CV classifications was altered in 98.32% and 68.80% respectively, though the MSE was at 0.149, the fitness to the model at 0.804,  ...  The last rank was given to the MLP Neural Network of 1 layer with quite inferior results to all the model types.  ... 
doi:10.22495/cocv14i4c1art12 fatcat:asjrxjrq2fffbd4tjzxyaxca7y


K. Riyazahmed
2021 Indian Journal of Finance and Banking  
To improve accuracy, financial researchers use machine learning architectures for the past two decades. Neural Networks (NN) are a widely used architecture in financial research.  ...  Hence, this descriptive study classifies and examines the NN application in finance into four broad categories i.e., investment prediction, credit evaluation, financial distress, and other financial applications  ...  CONCLUSION A descriptive systematic review was conducted to find the application of neural networks in financial research.  ... 
doi:10.46281/ijfb.v5i2.997 fatcat:eq4lq7a6svf63nxvwtihx3y3lq

Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks

Shih-Wei Lin, Yeou-Ren Shiue, Shih-Chi Chen, Hui-Miao Cheng
2009 Expert systems with applications  
Therefore, this study applies particle swarm optimization (PSO) to obtain suitable parameter settings for support vector machine (SVM) and decision tree (DT), and to select a subset of beneficial features  ...  The experimental results showed that the proposed approaches could obtain a better parameter setting, reduce unnecessary features, and improve the accuracy of classification significantly.  ...  Becerra, Galvao, and Abou-Seads (2005) proposed neural and wavelet network models for financial distress. The result showed that their approach is a valid alternative to the classical DA models.  ... 
doi:10.1016/j.eswa.2009.03.029 fatcat:k7gv5o3mqrcphihosh3nmps6ca

Combination of Biorthogonal Wavelet Hybrid Kernel OCSVM with Feature Weighted Approach Based on EVA and GRA in Financial Distress Prediction

Chao Huang, Fei Gao, Hongyan Jiang
2014 Mathematical Problems in Engineering  
put forward for financial distress prediction.  ...  Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM) is further  ...  The SVM is a powerful method for classification and has shown promising performance in financial distress prediction.  ... 
doi:10.1155/2014/538594 fatcat:3faf33gm3rbyhbqe3tlwxokhp4


Ion Smeureanu, Gheorghe Ruxanda, Andreea Diosteanu, Camelia Delcea, Liviu Adrian Cotfas
2012 Technological and Economic Development of Economy  
The purpose of such a platform is to develop flexible business applications for SCM transactions modeling, in collaborative and distributed economic systems.  ...  The framework is based on agent interaction and semantic web service composition.  ...  Acknowledgement This article is one of the results of the research activity carried out under the frame of the project "Doctoral Program and PhD Students in the education research and innovation triangle  ... 
doi:10.3846/20294913.2012.702696 fatcat:4mwaszzlyndszjytkaie2y4tiu

Diabetes Disease Prediction Using Machine Learning

Preetha S, Chandan N, Darshan N K, Gowrav P B.
2020 International Journal of Recent Trends in Engineering and Research  
Classification, Association rules, Prediction, Clustering, and Sequential patterns are some of the most important and common data mining techniques.  ...  Data mining is known as the process of sorting through a large number of data sets to create relationships and to find patterns for solving a given problem through data analysis.  ...  Since the financial distress of companies is the key step of bankruptcy, the use of financial proportions to predict financial distress has drawn the scholastics and economic and financial institutions  ... 
doi:10.23883/ijrter.2020.6029.65q5h fatcat:x52pjn2ybjcpflqqx4auiy6xma

Financial distress prediction using the hybrid associative memory with translation

L. Cleofas-Sánchez, V. García, A.I. Marqués, J.S. Sánchez
2016 Applied Soft Computing  
This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation.  ...  While many different neural network architectures have successfully been used to predict credit risk and corporate failure, the power of associative memories for financial decision-making has not been  ...  We would like to thank the Reviewers for their valuable comments and suggestions, which have helped to improve the quality of this paper substantially.  ... 
doi:10.1016/j.asoc.2016.04.005 fatcat:f622tuzyqjgobbxwcsqrrhom3u

Indicators of financial distress - An empirical study of Indian Textile sector

Jyoti Jaydeep Nair, J K Sachdeva
2016 Journal of global economy  
Key words: Financial distress, distress signals, textile sector, continuous losses, financial ratios  ...  Listed companies in textile sector incurring continuous losses for three years were selected for the study. Financial ratios were used as variables.  ...  Becerra et al (2005) used linear discriminant models, neural networks and wavelet networks for corporate financial distress prediction. 60 failed and non-failed UK firms were selected for the study for  ... 
doi:10.1956/jge.v12i2.418 fatcat:d3xczkrhkberzcx3xvz2jdknuu

An Artificial Neural Network Approach for Credit Risk Management

Vincenzo Pacelli, Michele Azzollini
2011 Journal of Intelligent Learning Systems and Applications  
panel of companies, showing the differences between the two neural network models.  ...  In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to another one, built for a research conducted in 2004 with a similar  ...  Acknowledgement The authors acknowledge to the anonymous referees for their thoughtful and constructive suggestions and Maria Rosaria Di Muro for the valuable support.  ... 
doi:10.4236/jilsa.2011.32012 fatcat:el5cm4icyrb3pholxaz3hk7zwa

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Hima Keerthi Sagiraju, Shashi Mogalla
2021 Indonesian Journal of Electrical Engineering and Computer Science  
Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined.  ...  This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and  ...  The support vector machine (SVM) and convolution neural Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  network (CNN) classification model was developed for iris recognition system [17] .  ... 
doi:10.11591/ijeecs.v24.i3.pp1759-1771 fatcat:zwimlr4dbnf4nn5yeesyks2n3q


Martin O'Halloran, Brian McGinley, Raquel Cruz Conceicao, Fearghal Morgan, Edward Jones, Martin Glavin
2011 Electromagnetic Waves  
In this paper, the effects of dielectric heterogeneity on a novel Spiking Neural Network (SNN) classifier are examined in terms of both sensitivity and specificity, using a 3D dielectrically heterogeneous  ...  Finally and importantly, misclassified tumours are analysed and suggestions for future work are discussed.  ...  SPIKING NEURAL NETWORKS & GENETIC ALGORITHMS Spiking Neural Network The organic nervous system has inspired the development of Artificial Neural Networks (ANNs).  ... 
doi:10.2528/pier10122203 fatcat:eictsrybbza2filtrbjeavbhia

Bacterial foraging trained wavelet neural networks: application to bankruptcy prediction in banks

Paramjeet N.A., V. Ravi
2011 International Journal of Data Analysis Techniques and Strategies  
The performance of BFTWNN is compared with that of threshold accepting wavelet trained wavelet neural network (TAWNN) [Vinay Kumar et al.[38]] and the original WNN.  ...  1 Certificate 2 2 Acknowledgement 3 3 Abstract and Keywords 4 4 Nomenclature and Abbreviations 6 5 ABSTRACT The present report proposes training of wavelet neural network (WNN) with the newly proposed  ...  Cheng Chen & Fu[10] combined RBF network with logit analysis learning to predict financial distress.  ... 
doi:10.1504/ijdats.2011.041334 fatcat:ypobj5wm3nftzk7olp2bedi5me

Data mining techniques and applications – A decade review from 2000 to 2011

Shu-Hsien Liao, Pei-Hui Chu, Pei-Yuan Hsiao
2012 Expert systems with applications  
Some applications for neural networks are radial basis function networks, neural classification, Bayesian confidence propagation neural networks, gene regulatory networks, fuzzy recurrent neural networks  ...  , neural nets, back-propagation artificial neural networks, Bayesian networks, general regression neural networks and flow networks.  ...  management, classification, neural networks and decision trees.  ... 
doi:10.1016/j.eswa.2012.02.063 fatcat:2ydn6gpclzac3kfwpeqkq5ezoa
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