Data Mining in Finance


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Data Mining in Finance: Advances in Relational and Hybrid Methods
by Boris Kovalerchuk and Evgenii Vityaev,
Kluwer Acad. Publ, 2000

The Kluwer International Series in Engineering and Computer Science , Vol.  547  

ISBN: 0-7923-7804-0
Kluwer's prepublication flyer, 2000 (pdf)

Foreword by Gregory Piatetsky-Shapiro

1. The Scope and Methods of the Study
2. Numerical Data Mining Models with  Financial    Applications
3. Rule-Based and Hybrid Financial Data Mining
4. Relational Data Mining (RDM)
5. Financial Applications of Relational Data Mining
6. Comparison of Performance of RDM and other methods in financial applications
7. Fuzzy logic approach and its financial applications

Getting the book:   

5. Financial Applications of Relational Data Mining


  • Introduction
  • Transforming numeric data into relations
  • Hypotheses and probabilistic “laws”
  • Markov chains as probabilistic “laws” in finance
  • Learning
  • Method of forecasting
  • Experiment 1
  • Experiment 2
  • Interval stock forecast for portfolio selection
  • Predicate invention for financial applications: calendar effects
  • Conclusion