Article ID: | iaor1994631 |
Country: | United States |
Volume: | 10 |
Issue: | 1 |
Start Page Number: | 11 |
End Page Number: | 32 |
Publication Date: | Jun 1993 |
Journal: | Journal of Management Information Systems |
Authors: | Bansal Arun, Kauffman Robert J., Weitz Rob R. |
Keywords: | statistics: regression, neural networks, measurement |
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 and analysis. There are two main results: (1) Linear regression outperformed neural nets in terms of forecasting accuracy, but the opposite was true when the authors 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.