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Neural networks in financial engineering: a study in methodology.

A N Refenes¹, A N Burgess, Y Bentz

¹London Bus. Sch.

IEEE Transactions on Neural Networks

|January 1, 1997

Summary

This study addresses the lack of statistical significance testing in neural network models for financial data. It proposes solutions for model misspecification and nonstationarity in financial engineering applications.

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Area of Science:

Quantitative Finance
Computational Statistics
Machine Learning

Background:

Neural networks excel at modeling financial data but lack rigorous statistical testing procedures.
This deficiency is critical in financial engineering, where stochastic processes and statistical significance are paramount.
Existing methods fail to adequately address model misspecification and parameter significance in neural financial models.

Purpose of the Study:

To review and propose solutions for statistical testing weaknesses in neural network models applied to financial data.
To enhance the reliability and interpretability of neural network models in financial applications.
To provide a framework for robust statistical validation of neural financial models.

Main Methods:

Exploration of typical applications: options pricing, cointegration, term structure of interest rates, and investor behavior models.
Description of alternative variable selection techniques.
Application of model misspecification tests.
Novel use of cointegration to address nonstationarity.
Development of predictive neural modeling approaches aligned with financial data requirements.

Main Results:

Demonstrated methods for variable selection and model misspecification testing in neural networks.
Introduced a cointegration-based approach to handle nonstationarity in financial time series.
Presented enhanced predictive neural modeling strategies suitable for financial data.
Highlighted the importance of statistical validation for financial applications of neural networks.

Conclusions:

Established procedures for statistical significance testing and model misspecification are crucial for neural financial modeling.
The proposed methods improve the robustness and interpretability of neural networks in finance.
This work facilitates more reliable and statistically sound applications of neural networks in financial engineering.