  Currently statistical and artificial neural network methods dominate in
financial data mining. Alternative relational (symbolic) data mining methods
have shown their effectiveness in robotics, drug design and other applications.
Traditionally symbolic methods prevail in the areas with significant
non-numeric (symbolic) knowledge, such as relative location in robot
navigation. At first glance, stock market forecast looks as a pure numeric area
irrelevant to symbolic methods. One of our major goals is to show that
financial time series can benefit significantly from relational data mining
based on symbolic methods. The paper overviews relational data mining
methodology and develops this techniques for financial data mining.
