Discovering golden nuggets: data mining in financial application

With the increase of economic globalization and evolution of information technology, financial data are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2004-11, Vol.34 (4), p.513-522
Main Authors: Zhang, D., Zhou, L.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Cybernetics
Data mining
Decision making
Delay
Economic forecasting
Economics
Evolution
Exact sciences and technology
Focusing
Globalization
Information systems
Information systems. Data bases
Information technology
Investments
Memory organisation. Data processing
Operational research and scientific management
Operational research. Management science
Portfolio theory
Predictive models
Software
Strategic planning
Studies
Trends
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Summary:With the increase of economic globalization and evolution of information technology, financial data are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. Data mining techniques have been used to uncover hidden patterns and predict future trends and behaviors in financial markets. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. In this paper, we describe data mining in the context of financial application from both technical and application perspectives. In addition, we compare different data mining techniques and discuss important data mining issues involved in specific financial applications. Finally, we highlight a number of challenges and trends for future research in this area.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2004.829279