Some current research of derivative pricing is dedicated to artificial neural networks to generate market prices (see Breitner (2000 and 2001)) instead of analytical prices developed by Black, Scholes and Merton (1973) or Cox, Ross and Rubinstein (1979). Needed data are usually taken from commercial finance databases. This paper presents the software agent PISA[1] extracting quotes from webpages to generate cost free quote databases. Such databases provide time series for the training of neural networks. Extrapolating series with neural networks enables all kinds of forecasting. Interpolating data obtained enables, e. g., a comparison of derivative prices from different issuers and a synthesis of market price functions. This paper presents a comparison of selected programming languages to find the most suitable for the given tasks. The components of PISA are described in detail. The paper closes with examples for the extraction process.
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Paper provided by Institut für Wirtschaftsinformatik, Universität Hannover in its series IWI Discussion Paper Series with number
4.