Journal of Management Information Systems

Volume 10 Number 1 1993 pp. 11-32

Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach

Bansal, Arun, Kauffman, Robert J, and Weitz, Rob R

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.

Key words and phrases: business value of information technology, data quality, decision support systems, forecasting, information economics, neural networks, mortgage-backed securities, prepayment forecasting, risk management forecasting systems