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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/14268

Title: COMPARING THE PERFORMANCE OF REGRESSION AND NEURAL NETWORKS AS DATA QUALITY VARIES: A BUSINESS VALUE APPROACH
Authors: Bansal, Arun
Kauffman, Robert J.
Weitz, Rob R.
Keywords: Business value of information technology
data quality
decision support systems
forecasting
information economics
neural networks
mortgage-backed securities
prepayment forecasting
risk management forecasting systems
systems design
Issue Date: 27-Feb-1993
Publisher: Stern School of Business, New York University
Series/Report no.: IS-93-34
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 MBS portfolios are drawn from the results.
URI: http://hdl.handle.net/2451/14268
Appears in Collections:IOMS: Information Systems Working Papers

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