Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach
研究了数据质量变化时，线性回归和神经网络在抵押贷款支持证券组合预测中的表现，发现线性回归预测更准，但神经网络在商业价值和数据鲁棒性上更优。
: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 fteld of ftnance 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 fmancial risk management of mortgage-backed security portfolios are drawn from the results.