Confidence in data mining model predictions: a financial engineering application

This paper describes a generally applicable robust method for determining prediction intervals for models derived by non-linear regression. Hypothesis tests for bias are applied. The concept is demonstrated by application to a standard synthetic example, and is then applied to prediction intervals f...

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Bibliographic Details
Main Authors: Healy, J.V., Dixon, M., Read, B.J., Cai, F.F.
Format: Conference Proceeding
Language:English
Subjects:
Communications technology
Data engineering
Data mining
Mathematics
Neural networks
Predictive models
Pricing
Reliability engineering
Robustness
Testing
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Summary:This paper describes a generally applicable robust method for determining prediction intervals for models derived by non-linear regression. Hypothesis tests for bias are applied. The concept is demonstrated by application to a standard synthetic example, and is then applied to prediction intervals for a financial engineering example viz. option pricing using data from LIFFE for 'ESX' European style options on the FTSE 100 index. Unbiased estimates of the standard error are obtained. The method uses standard regression procedures to determine local error bars and avoids programming special architectures. It is appropriate for target data with non-constant variance.
DOI:10.1109/IECON.2003.1280355