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Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach.
- Source :
- Journal of Management Information Systems; Summer93, Vol. 10 Issue 1, p11-32, 22p, 1 Diagram, 4 Charts
- Publication Year :
- 1993
-
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 mortgage-backed security portfolios are drawn from the results. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 07421222
- Volume :
- 10
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Management Information Systems
- Publication Type :
- Academic Journal
- Accession number :
- 9608053419
- Full Text :
- https://doi.org/10.1080/07421222.1993.11517988