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|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|
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