Data Mining in Finance

Advances in Relational and Hybrid Methods

  • Boris Kovalerchuk
  • Evgenii Vityaev

Part of the The International Series in Engineering and Computer Science book series (SECS, volume 547)

About this book

Introduction

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data.
Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space.
Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.

Keywords

Finance Symbol algorithms artificial intelligence data mining fuzzy intelligence knowledge knowledge discovery learning logic programming machine learning mathematics neural networks programming

Authors and affiliations

  • Boris Kovalerchuk
    • 1
  • Evgenii Vityaev
    • 2
  1. 1.Central Washington UniversityUSA
  2. 2.Institute of MathematicsRussian Academy of SciencesRussia

Bibliographic information

  • DOI https://doi.org/10.1007/b116453
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-7923-7804-4
  • Online ISBN 978-0-306-47018-9
  • Series Print ISSN 0893-3405
  • About this book
Industry Sectors
Electronics
Telecommunications
Aerospace
Pharma