NEURAL NETWORKS AND BUSINESS FORECASTING: AN APPLICATION TO CROSS‐SECTIONAL 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 non‐linear 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 cross‐sectional 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 cross‐sectional 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 out‐of‐sample.