Neural network earnings per share forecasting models: a comparative analysis of alternative methods

Neural network earnings per share forecasting models: a comparative analysis of alternative methods

0.00 Avg rating0 Votes
Article ID: iaor2009426
Country: United States
Volume: 35
Issue: 2
Start Page Number: 205
End Page Number: 237
Publication Date: Apr 2005
Journal: Decision Sciences
Authors: , ,
Keywords: decision theory, forecasting: applications
Abstract:

In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables.

Reviews

Required fields are marked *. Your email address will not be published.