Stern School of Business >
IOMS: Information Systems Working Papers >
Please use this identifier to cite or link to this item:
|Title: ||COMPARING THE PERFORMANCE OF REGRESSION AND NEURAL NETWORKS AS DATA QUALITY VARIES: A BUSINESS VALUE APPROACH|
|Authors: ||Bansal, Arun|
Kauffman, Robert J.
Weitz, Rob R.
|Keywords: ||Business value of information technology|
decision support systems
risk management forecasting systems
|Issue Date: ||27-Feb-1993|
|Publisher: ||Stern School of Business, New York University|
|Series/Report no.: ||IS-93-34|
|Abstract: ||Under circumstances where data quality may vary (due to inaccuracies or lack of timeliness,
for example), knowledge about the potential performance of alternate predictive models can help a
decision maker to design a business value-maximizing information system. This paper examines a real-world
example from the field of finance to illustrate a comparison of alternative modeling tools. Two
modeling alternatives are used in this example: regression analysis and neural network analysis. There
are two main results: (1) Linear regression outperformed neural nets in terms of forecasting accuracy,
but the opposite was true when we considered the business value of the forecast. (2) Neural net-based
forecasts tended to be more robust than linear regression forecasts as data accuracy degraded.
Managerial implications for financial risk management of MBS portfolios are drawn from the results.|
|Appears in Collections:||IOMS: Information Systems Working Papers|
Items in Faculty Digital Archive are protected by copyright, with all rights reserved, unless otherwise indicated.