EconStor >
Deutsche Bank Research, Frankfurt am Main >
Research Notes, Deutsche Bank Research >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/40277
  
Title:Feature extraction with hybrid neural networks PDF Logo
Authors:Wegmann, Georg
Issue Date:2000
Series/Report no.:Research notes in economics & statistics 00-6
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.
Subjects:neural networks
fuzzy logic systems
entropy
option pricing
Document Type:Working Paper
Appears in Collections:Research Notes, Deutsche Bank Research

Files in This Item:
File Description SizeFormat
321617991.pdf10.36 MBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/40277

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.