Neural networks in financial engineering: a study in methodology

Neural networks have shown considerable successes in modeling financial data series. However, a major weakness of neural modeling is the lack of established procedures for performing tests for misspecified models, and tests of statistical significance for the various parameters that have been estima...

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Bibliographic Details
Published in:IEEE transactions on neural networks 1997-11, Vol.8 (6), p.1222-1267
Main Authors: Refenes, A.-P. N., Burgess, A.N., Bentz, Y.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Data engineering
Economic indicators
Exact sciences and technology
Input variables
Neural networks
Performance evaluation
Power engineering and energy
Predictive models
Pricing
Stochastic processes
Testing
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Summary:Neural networks have shown considerable successes in modeling financial data series. However, a major weakness of neural modeling is the lack of established procedures for performing tests for misspecified models, and tests of statistical significance for the various parameters that have been estimated. This is a serious disadvantage in applications where there is a strong culture for testing not only the predictive power of a model or the sensitivity of the dependent variable to changes in the inputs but also the statistical significance of the finding at a specified level of confidence. Rarely is this more important than in the case of financial engineering, where the data generating processes are dominantly stochastic and only partially deterministic. Partly a tutorial, partly a review, this paper describes a collection of typical applications in options pricing, cointegration, the term structure of interest rates and models of investor behavior which highlight these weaknesses and propose and evaluate a number of solutions. We describe a number of alternative ways to deal with the problem of variable selection, show how to use model misspecification tests, we deploy a novel way based on cointegration to deal with the problem of nonstationarity, and generally describe approaches to predictive neural modeling which are more in tune with the requirements for modeling financial data series.
ISSN:1045-9227
1941-0093
DOI:10.1109/72.641449