A RADIAL BASIS FUNCTION APPROACH TO EARNINGS FORECAST

The fundamental management problem of decision making in a climate where future values of important variables are unknown and can at best be estimated using traditional statistical techniques is addressed. The incorporation of forecast models into management decision‐support systems is critical for the overall success of organizational accounting information systems, where managers require confidence in the information that they use. The neural network paradigm has been described as a promising nonparametric approach, negating the required, and sometimes restrictive, statistical assumptions. The application of the neural network paradigm to the area of earnings forecasting is presented. A radial basis function (RBF) approach is developed and tested empirically using data from the Hong Kong Hang Seng 100 Index and macroeconomic data, mimicking an actual business valuation/forecast exercise. Results show that the RBF approach is superior to regression and financial analysts in earnings forecast. Copyright © 2012 John Wiley & Sons, Ltd.