Feature extraction with hybrid neural networks

Georg Wegmann

No 00-6, Research Notes from Deutsche Bank Research

Abstract: Neural networks (NN) and fuzzy logic systems (FLS) are used successfully for financial forecasting, credit rating and portfolio management. In search for more sophisticated modeling techniques a mixture of NN and FLS has proved to be worth consideration. We propose the novel constructive approach by which a neuro fuzzy network is built up with the help of a constrained optimizer. The mathematical motivation for such hybrid networks is presented, using the Kolmogorov theory of metric entropy. As an application of the proposed approach we build a neuro fuzzy network model which is able to explain the prices of call options written on the S&P 500 stock index. While option pricing theory typically requires a highly complex statistical model to capture the empirical pricing mechanism, our results indicate that this algorithm leads to more parsimonious functional specificationes which have a superior out-of-sample performance.

Keywords: neural networks; fuzzy logic systems; entropy; option pricing (search for similar items in EconPapers)
Date: 2000
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:dbrrns:006

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