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NEURAL NETWORKS AND BUSINESS FORECASTING AN APPLICATION TO CROSSSECTIONAL AUDIT FEE DATA

NEURAL NETWORKS AND BUSINESS FORECASTING AN APPLICATION TO CROSSSECTIONAL AUDIT FEE DATA Neural Network NN simulation models are being increasingly utilised in the business and management fields as forecasting, pattern recognition and classification tools. Their growing popularity appears to emanate from the ability of NNs to approximate complex nonlinear relationships, via their capacity to represent latent combinations of unobservable variables in hidden layers. Although there is a growing business literature on the ability of NNs to predict various corporate outcomes e.g., corporate failure, and to forecast time series data e.g., share prices, they have yet to be fully evaluated by business academics on crosssectional data. This paper provides an overview of the NN modelling approach and compares the performance of NNs, relative to conventional OLS regression analysis, in predicting the crosssectional variation in corporate audit fees. The empirical results suggest that the NN models exhibit superior forecasting accuracy to their OLS counterparts, but that this differential reduces when the models are tested outofsample. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Commerce and Management Emerald Publishing

NEURAL NETWORKS AND BUSINESS FORECASTING AN APPLICATION TO CROSSSECTIONAL AUDIT FEE DATA

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1056-9219
DOI
10.1108/eb047370
Publisher site
See Article on Publisher Site

Abstract

Neural Network NN simulation models are being increasingly utilised in the business and management fields as forecasting, pattern recognition and classification tools. Their growing popularity appears to emanate from the ability of NNs to approximate complex nonlinear relationships, via their capacity to represent latent combinations of unobservable variables in hidden layers. Although there is a growing business literature on the ability of NNs to predict various corporate outcomes e.g., corporate failure, and to forecast time series data e.g., share prices, they have yet to be fully evaluated by business academics on crosssectional data. This paper provides an overview of the NN modelling approach and compares the performance of NNs, relative to conventional OLS regression analysis, in predicting the crosssectional variation in corporate audit fees. The empirical results suggest that the NN models exhibit superior forecasting accuracy to their OLS counterparts, but that this differential reduces when the models are tested outofsample.

Journal

International Journal of Commerce and ManagementEmerald Publishing

Published: Feb 1, 1998

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