A Radial Basis Function Approach To Earnings Forecast

Authors:
Robert G. Biscontri
Affiliations:
Department of Accounting and Finance, Asper School of Business, University of Manitoba, Winnipeg, Manitoba, Canada
Venue:
International Journal of Intelligent Systems in Accounting and Finance Management
Year:
2012

Citing 9
Cited 0

Radial basis functions for multivariable interpolation: a review

Algorithms for approximation
Neural network models for time series forecasts

Management Science
Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction

Fuzzy Sets and Systems
Advanced Methods in Neural Computing

Advanced Methods in Neural Computing
A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Computers and Operations Research
Forecasting Economic Data with Neural Networks

Computational Economics
Quantitative Analysis for Management

Quantitative Analysis for Management
Neural network earnings per share forecasting models: A comparison of backward propagation and the genetic algorithm

Decision Support Systems
The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks

International Journal of Intelligent Systems in Accounting and Finance Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.