1. Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach.
- Author
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Bansal, Arun, Kauffman, Robert J., and Weitz, Rob R.
- Subjects
DATA quality ,PORTFOLIO management (Investments) -- Mathematical models ,PREDICTION models ,REGRESSION analysis ,INFORMATION technology research ,ARTIFICIAL neural networks ,MATHEMATICAL models - 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]
- Published
- 1993
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