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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14268
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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
data quality
decision support systems
forecasting
information economics
neural networks
mortgage-backed securities
prepayment forecasting
risk management forecasting systems
systems design |
| 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. |
| URI: | http://hdl.handle.net/2451/14268 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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