Publications

A special issue of Decision Support Systems Journal on Data Mining for Financial Decision Making (Vol. 37, no. 4), has been published by Elsevier.

Details can be found on Elsevier Special Issues page.

As information-intensive organizations transform themselves from passive collectors to active explorers and exploiters of data, they face a serious challenge: How can they benefit from increased access to information to better understand their markets, customers, suppliers, operations, and internal business processes?

Responding to this challenge, the field of data mining has emerged. It focuses on the process of discovering valid, comprehensible, and potentially useful knowledge from large data sets with the goal to act on this knowledge in decision making.

Data mining integrates concepts from modern statistics, intelligent information systems, machine learning, pattern recognition, decision theory, data engineering, and database management. It provides powerful tools that can reveal complex and hidden relationships in large amounts of data. Data mining already has a major impact on business and finance.

Financial markets generate large volumes of data. Analyzing these data to reveal valuable information and making use of the information in decision making present great opportunities and grand challenges for data mining. The rewards for finding valuable patterns are potentially huge, but so are the difficulties. Important problems range from questions on discovering trends at their early stages to turning insights into actions.

Submitted papers were reviewed with the standard practice of this journal. This special issue consists of 10 papers, of interest to academics in finance, statistics, economics, information systems, and other disciplines, as well as to data mining practitioners, financial analysts, strategists, and decision-makers from the financial industries.

Hui Wang and Andreas Weigend, guest editors