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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14349
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| Title: | QUANTIFYING THE VALUE OF MODELS AND DATA: A COMPARISON OF THE
PERFORMANCE OF REGRESSION AND NEURAL NETS WHEN DATA QUALITY VARIES |
| Authors: | Bansal, Arun
Kauffman, Robert J.
Weitz, Rob R. |
| Keywords: | business value of information technology
data quality
decision support systems
information economics
neural networks
risk management
risk management forecasting systems
systems design |
| Issue Date: | 5-Oct-1992 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-92-33 |
| Abstract: | Under circumstances where data quality may vary, knowledge about the
potential performance of alternate predictive models can enable a
decision maker to design an information system whose value is optimized
in two ways. The decision maker can select a model which is least
sensitive to predictive degradation in the range of observed data
quality variation. And, once the "right" model has been
selected, the decision maker can select the appropriate level of data
quality in view of the costs of acquiring it. This paper examines a
real-world example from the field of finance -- prepayments in
mortgage-backed securities (MBS) portfolio management -- to illustrate a
methodology that enables such evaluations to be made for two modeling
alternative: regression analysis and neural network analysis. The
methodology indicates that with "perfect data," the neural
network approach outperforms regression in terms of predictive accuracy
and utility in a prepayment risk management forecasting system (RMFS).
Further, the performance of the neural network model is more robust
under conditions of data quality degradation. |
| URI: | http://hdl.handle.net/2451/14349 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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