Data Mining for Financial Applications

Authors

Boris Kovalerchuk, Central Washington University
Evgenii Vitaev, Russian Academy of Sciences

Document Type

Book Chapter

Department or Administrative Unit

Computer Science

Publication Date

2005

Abstract

This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

Comments

This book chapter was originally published in Data Mining and Knowledge Discovery Handbook. The full-text chapter from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Recommended Citation

Kovalerchuk B., Vityaev E. (2005) Data Mining for Financial Applications. In: Maimon O., Rokach L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_57

Journal

Data Mining and Knowledge Discovery Handbook

Rights

© Springer Science+Business Media, Inc. 2005