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 MBS portfolios are drawn from the results.Information Systems Working Papers Serie