Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks

Sen, Tarun K.; Ghandforoush, Parviz; Stivason, Charles T.

doi:10.1207/s15327612jamd0804_2

Cited by 5 publications

(8 citation statements)

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“…Several studies showed that the predictions made using neural networks were superior to the ones obtained by logistic regression and discriminant analysis (Coats et al, 1993;Anandarajan et al, 2001;Charitou et al, 2004;Neves et al, 2006). In many other studies, this superiority was not confi rmed as logistic regression provided overly similar results to neural networks (Fanning et al, 1993;Sen et al, 2004;Pompe et al, 2005;Chen et al, 2006;Youn et al, 2010). These three outlined methods remain the most prominent within business failure prediction research (Du Jardin, 2009, p. 44).…”

Section: Introductionmentioning

confidence: 99%

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

Situm

2015

Zagreb International Review of Economics and Business

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Abstract:This study investigated the potential of a specifi c trend, defi ned as the relative change of accounting ratios for two consecutive years, to improve the classifi cation accuracy and model performance of insolvency prediction models based on multivariate linear discriminant analysis. The results show that the respective trend can include information from both consecutive years, but this informational content could not be exploited to improve early detection of corporate crises and insolvencies.

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Section: Introductionmentioning

confidence: 99%

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

Situm

2015

Zagreb International Review of Economics and Business

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“…Several authors have used neural networks to successfully predict financial phenomena, such as stock market trends, corporate bankruptcy, gold prices and foreign exchange prices (Sen et al, 2004). The main advantage of the logistic regression method over ANN is the understanding of the contribution of every variable in bankruptcy prediction.…”

Section: Methodsmentioning

confidence: 99%

Corporate bankruptcy prediction in the Republic of Serbia

Stanišić¹,

Mizdraković²,

Knežević³

2013

Industrija

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“…To overcome these limitations, some authors have explored other techniques such as genetic algorithms [11,26,27,12,28,13,29] or methods that fit a neural network [30,31,32,33]. But these examples are very few and no comparative study has analyzed the influence of a variable selection technique on the predictive performance of a model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sen et al [32] Tests applied to the weight of the neural network MLP-BP Serrano-Cinca [46] Variables used in one previous study MLP-BP Sexton et al [13] Genetic algorithm applied to variables commonly used in financial analysis MLP-GA Shin et Lee [71] Stepwise search with a criterion optimized for discriminant analysis and t test…”

Section: Mlp-bpmentioning

confidence: 99%

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Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

110

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We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

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Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks

Cited by 5 publications

References 0 publications

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

The Relevance of Trend Variables for the Prediction of Corporate Crises and Insolvencies

Corporate bankruptcy prediction in the Republic of Serbia

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

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