APOLOGIZE IF YOU RECEIVE MULTIPLE COPIES OF THIS CALL
FOR PAPERS.
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The Journal of Decision Support Systems
Special Issue on Data Mining for Financial Decision Making
GUEST EDITORS
Hui Wang, University of Ulster
Andreas S. Weigend, Weigend Associates LLC
CALL FOR PAPERS
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 apply this
knowledge to decision making.
Data mining integrates concepts from modern statistics,
intelligent information systems, machine learning, pattern
recognition, decision theory, data engineering and database
management, and provides powerful tools that can reveal
complex and hidden relationships in large amounts of data.
The approaches include neural networks, genetic programming,
and tree-based methods. Data mining already has a major
impact on business and finance.
Financial markets generate large volumes of data. Analysing
these data to reveal valuable information and making use of
the information in decision making present great
opportunities but grand challenges for data mining. The
rewards for finding valuable patterns are potentially
enormous, but so are the difficulties. There is evidence that
short-term trends do exist and some general patterns do occur
frequently. Important problems are: how to find the trends at
their early stages and how to time the beginning and ending
of trends, how to take into account in decision making the
found trends, the general patterns, and domain knowledge that
describes the intricately inter-related world of global
financial markets.
The focus of this special issue is on the use of data mining
techniques for decision making in financial markets.
Topics of interest include:
* Financial data selection and pre-processing for data mining
* Solutions to new problems in financial decision making
* New solutions for classical problems in financial decision
making
* Data and solutions visualisation for financial decision
making
* Successful case studies.
Areas include:
* Risk management including credit risk and market risk
* Asset allocation, dynamic trading and hedging
* Execution and liquidity models
* Behavioural finance, and other emerging areas.
Both original contributions and thoughtful survey papers are
welcome.
SUBMISSION INSTRUCTIONS
Electronic submissions are strongly encouraged. Postscript or
PDF copies of manuscripts may be emailed to
h.wang@ulst.ac.uk.
SUBMISSION DEADLINE: September 9, 2002
Details about the submission process and scope of the special
issue are available at http://www.weigend.com/dss and
http://www.elsevier.com/inca/homepage/sae/orms/dss/call1.htm
Hui Wang
School of Information and Software Engineering
University of Ulster at Jordanstown
Northern Ireland, BT37 0QB
United Kingdom
Tel: +44 28 90368981
Fax: +44 28 90366068
Email: h.wang@ulst.ac.uk
Andreas S. Weigend
Weigend Associates LLC
P.O.Box 20207
Stanford, CA 94309
U.S.A.
Tel: +1 917 697-3800
Fax: +1 815 327-5462
Email: dss@weigend.com
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