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Journal ArticleDOI

Comparing the predictive performance of a neural network model with some traditional market response models

TLDR
This study believes that its study is one of the first applications of neural network models using individual level cross-sectional survey data for predicting market response and that its findings have opened the doors to further research about the applicability of Neural Network Modeling using Individual levelCross-sectional data, as opposed to using aggregate company level data.
About
This article is published in International Journal of Forecasting.The article was published on 1994-09-01. It has received 133 citations till now. The article focuses on the topics: Artificial neural network.

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Citations
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Journal ArticleDOI

Review: Neural networks and statistical techniques: A review of applications

TL;DR: A comprehensive review of articles that involve a comparative study of feed forward neural networks and statistical techniques used for prediction and classification problems in various areas of applications is carried out.
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How effective are neural networks at forecasting and prediction? A review and evaluation

TL;DR: Evaluating research in this area has been difficult, due to lack of clear criteria, so eleven guidelines that could be used in evaluating this literature are identified and used.
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Neural networks in business: a survey of applications (1992–1998)

TL;DR: Although still regarded as a novel methodology, neural networks are shown to have matured to the point of offering real practical benefits in many of their applications.
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Credit scoring, statistical techniques and evaluation criteria: A review of the literature

TL;DR: There is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist, but the applications of the scoring methodologies have been widely extended to include different areas and this subsequently can help decision makers, particularly in banking, to predict their clients' behaviour.
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An integrated data mining and behavioral scoring model for analyzing bank customers

TL;DR: It is demonstrated that identifying customers by a behavioral scoring model is helpful characteristics of customer and facilitates marketing strategy development.
References
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Journal ArticleDOI

Learning representations by back-propagating errors

TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Neural networks and physical systems with emergent collective computational abilities

TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
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A logical calculus of the ideas immanent in nervous activity

TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
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Managerial Applications of Neural Networks: The Case of Bank Failure Predictions

TL;DR: Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness.
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Bankruptcy prediction using neural networks

TL;DR: The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies, and implications for the accounting professional, neural networks researcher and decision support system builders are highlighted.
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