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Journal of Computational Intelligence in Finance
Final Call for Papers
Special Issue on
"Financial News Analysis using Distributed Data Mining"
The Journal of Computational Intelligence in Finance, a peer-reviewed
technical journal, published by Finance & Technology Publishing, is
seeking papers for review and publication on "Financial News Analysis
using Distributed Data Mining".
The Journal of Computational Intelligence in Finance publishes applied
research and practical applications of high quality that are based on
sound theoretical, empirical or quantitative analysis. For practitioners
and applied researchers, JCIF serves as the journal of record on
"the application of advanced computational technologies and analytical
techniques for financial modeling, investing and trading, and risk
management."
Papers published in JCIF are eligible for the "Distinguished Essay on
Computational Intelligence in Finance" award, which is selected by the
Editorial Board each year.
EDITORIAL BOARD
Randall B. Caldwell, Editor-in-Chief
Emilio Barucci - University of Florence, Italy
Richard J. Bauer, Jr. - St. Mary's University, Texas
Neil Burgess - London Business School
Oscar Castillo - UABC University, California
Jerry Connor - London Business School
Eric de Bodt - Universite Catholique de Louvain, France
James F. Derry - Mgmt. Engineering Productivity Systems, Ohio
Athanasios Episcopos - National Bank of Greece
Andrew Flitman - Monash University, Australia
Susan Garavaglia - Dun and Bradstreet, New Jersey
Ramo Gencay - University of Windsor, Canada
Sabyasachi Ghoshray - Florida International University
Lee Giles - NEC Research Institute, New Jersey
Christian Haefke - University of California at San Diego
Ypke Hiemstra - Vrije Universiteit, The Netherlands
Jason Kingdon - Searchspace Limited, University College London
Ralph Neuneier - Siemens AG Corporate Research Center, Germany
Zoran Obradovic - Washington State University
Marimuthu Palaniswami - University of Melbourne
Carlos E. Pedreira - Catholic University, Rio
Stuart H. Rubin - Central Michigan University
David B. Skalak - IBM, New York
Leon Sterling - University of Melbourne
Manoel F. Tenorio - Purdue University, Indiana
Halbert White - University of California at San Diego
Lei Xu - The Chinese University of Hong Kong
SPECIAL TOPIC
Financial News Analysis using Distributed Data Mining
PAPERS DUE
September 15, 1998
ACCEPTANCE NOTIFICATION
November 30, 1998
FINAL REVISED MANUSCRIPTS DUE
January 15, 1999
PUBLICATION DATE
March 1999
GUEST EDITORS
Zoran Obradovic Stuart H. Rubin
Associate Professor Associate Professor
Elec. Eng. & Comp. Sci. Dept. of Comp. Sci.
Washington State University Central Michigan University
Pullman, WA 99164-2752, USA Mt. Pleasant, MI 48859, USA
zoran(a)eecs.wsu.edu rubin(a)cps.cmich.edu
FOR THE LATEST AND MOST COMPLETE INFORMATION ON THIS CALL, SEE:
http://ourworld.compuserve.com/homepages/ftpub/call.htm
MOTIVATION
Recent technological developments, the rapid growth of the World Wide Web,
and maturing corporate intranet structures have led to the rapid
dissemination of huge amounts of financial news and information (newspaper
articles, financial services information, corporate publications, stock
exchange news, peer-reviewed financial journal articles, etc.). However,
cost and time constraints prohibit an exhaustive search through or download
of all potentially relevant financial news and information available on the
Internet, for later analysis and processing. One possible solution is to
distribute information sampling over a large number of locations in order
to classify local data, construct a pool of relevant information, and
generate useful rules that might be further analyzed or processed at a
central location. This requires intelligent and dynamic domain decomposition,
as well as flexible software agents for symbolic information processing.
SCOPE
All papers submitted must focus on the data mining of financial news and
information, and on applications of interest to financial analysis or
decision-making, investing or trading. Of particular interest are algorithms
and techniques that both incorporate computational intelligence and are
unique or especially relevant to financial tasks. Authors are invited to
submit papers on the design of automated, scalable, distributed knowledge
discovery systems for financial information mining on the Internet, to address
issues related to:
- search strategies
- knowledge representation
- reasoning mechanisms
- learning algorithms
Suggested topics include but are not limited to the following:
- intelligent software agent structures
- sampling strategies for mining the World Wide Web
- distributed search algorithms for mining financial news
- collaborative and heuristic search methods
- statistical text mining
- incremental knowledge discovery methods
- financial news and information quality
- dynamic domain decomposition
- expert and decision-based strategies
- knowledge representation issues
- knowledge-base design and segmentation
- communications and query issues
- Knowledge Interchange Format (KIF)
- Knowledge Query and Manipulation Language (KQML)
- semantic nets and frames
- combining natural language processing and statistical text mining
- algorithms for organizing, filtering and summarizing textual information
- state space methods and issues
- novel learning and reasoning algorithms
ABSTRACTS
Submit 150 to 300 word abstract including full name(s) and affiliation(s) of
the author(s), complete mailing address and telephone numbers of all authors.
Authors should provide a brief biographic sketch of themselves. Send abstracts
to:
Post: JCIF, P.O. Box 764, Haymarket, VA 20168 USA
E-mail: ftpub(a)compuserve.com
Fax: 1-703-753-2634
PAPERS
Papers must not have been previously published or currently submitted for
publication elsewhere. All material must be provided in the English language.
Submit three copies of each paper. Papers must be formatted for 8.5x11-inch
page format. Authors should provide a brief biographic sketch of themselves.
Each copy submitted should include a page that contains the title of the paper,
the full name(s) and affiliation(s) of the author(s), complete mailing address
and telephone numbers of all authors, and a 150 to 300 word abstract. The
Journal reserves the right to edit all material to meet space requirements
and to make grammatical and typographical corrections.
The final text should be 4000 to 6000 words in length, contain no more than
15 references, and be provided as follows:
(1) Hardcopy: printed and double-spaced, with notations for the location
of graphics, mathematical equations, given thereon, as necessary,
(2) Softcopy:
The REQUIRED MEDIA FORMAT is IBM PC 3.5", 1.44MB.
The PREFERRED FILE FORMAT is Word for Windows 3.1/95 (Word 6/7/97). Other
acceptable software file formats are the following:
Tex and LaTex using the required media format (Scientific Workplace 3.0
preferred, use standard features)
The PREFERRED GRAPHICS formats are Windows 3.1/95 (*.bmp,*.wmf,*.eps).
For other graphics formats, submit high-quality, camera-ready hardcopy, or
make an inquiry.
Include pseudo-code or source code in separate figures (listings). Extensive
code listings may be published on the Web site because of space limitations
in the journal.
Text citations must use the following format: last name(s) of author(s),
publication date and suffix (as necessary) in brackets. Example:
(a) direct reference: Watkins and McCoy [1993a]
(b) indirect reference: [Watkins and McCoy 1993a]
References must be listed alphabetically by the last name of the first
author according to the following formats:
Journal Article: authors' names, publication date and suffix (as
necessary) in brackets, article title (in double quotations),
periodical title (in italics), volume and number, pages cited.
Book: authors' names, publication date and suffix (as necessary)
in brackets, book title (in italics), publisher, publisher location,
pages cited.
Chapter in Book/Proceedings: authors' names, publication date and
suffix (as necessary) in brackets, chapter title (in double
quotations), editors' names, book title (in italics), publisher,
location, pages cited.
Send all manuscripts by Post to:
Editors, JCIF, P.O. Box 764, Haymarket, VA 20168 USA
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Leonard N. Stern School of Business
New York University
Computational Finance (CF99)
January 6, 1999 (Tutorials)
January 7 - 8 (Conference)
The sixth international conference Computational Finance (CF99)
will be held at NYU's Leonard N. Stern School of Business. CF99
is sponsored by the New York University Salomon Center, the
Center for Research on Information Systems and the Department
of Statistics and Operations Research.
Computational Finance has emerged as a genuinely cross-
disciplinary research meeting. CF99 is the sixth in a series of
conferences that have been sponsored by the California
Institute of Technology and the London Business School. In the
past, this conference was called Neural Networks in the Capital
Markets (NNCM). The expanding set of computational tools has
moved this meeting from its original emphasis on neural network
techniques to a broad spectrum of different methodologies.
With several hundred attendees, this fully refereed conference
has become an international forum where original research in
advanced computational applications in finance is presented and
discussed. CF99 brings together decision-makers and strategists
from the financial industries, with academics from finance,
statistics, economics, information systems and other
disciplines. In the last few years, the conference has seen
papers covering many different computational techniques
including: statistical machine learning, Monte Carlo
simulation, data mining, knowledge discovery, bootstrapping,
genetic algorithms, nonparametric methods, information theory
and fuzzy logic. Applications in many different areas are
welcome, including but not limited to: risk management, asset
allocation, dynamic trading and hedging strategies,
forecasting, numerical solutions of derivative PDEs, exotic
options and trading cost control.
Studies may cover any major international financial market
including equity, foreign exchange, bond, commodity and
derivatives. The conference emphasizes in-depth analysis and
comparative evaluation with established approaches.
CF99 will have two distinguished keynote speakers,
Gifford Fong, Chairman, and CEO of Gifford Fong Associates, and
David E. Shaw, PhD, Chairman and CEO of D. E. Shaw & Co., Inc.
The conference also features several invited speakers sharing
their expertise from both the academic and applied perspectives.
CF99 begins with a full day of tutorials designed to inform the
diverse group of participants on a selection of the latest tools
and research results. This year's tutorials feature the following speakers:
Professor Stephen Figlewski, Finance Department, Stern School of Business,
New York University
Professor Halbert White, Economics Department, University of California,
San Diego
Professor David A. Hsieh, Finance Department, Fuqua School of Business,
Duke University
Professor Benjamin van Roy, Engineering Economic Systems Department,
Stanford University
The conference will have several talk and poster sessions for
accepted papers. A selection of the presentations will be
invited to appear in a volume published by Kluwer Academic
Publishers.
Submissions to CF99:
Authors who wish to present papers should submit four copies
along with full contact information, including e-mail
addresses, to:
CF99 / Andreas Weigend
Information Systems Department
Leonard N. Stern School of Business
New York University
44 W 4th St., MEC 9-171
New York, NY 10012, USA
E-mail: cf99(a)stern.nyu.edu
Web: www.stern.nyu.edu/cf99
All submissions must be received by August 31st.,
1998. Full papers are preferred, but extended
abstracts clearly stating the results are
acceptable. Only original, relevant research work
will be accepted.
Registration material will be put up on the Web at
www.stern.nyu.edu/cf99 in August.
Deadline for early registration is December 1, 1998.
Conference Chairs:
General Chair
Y. S. Abu-Mostafa, Caltech
Organizational Chair
A. S. Weigend, NYU Stern
Program Co-chairs
B. LeBaron, University of Wisconsin
A. W. Lo, MIT Sloan
Organizing Committee:
A. Atiya, Cairo University
J. Cowan, University of Chicago
R. Gencay, University of Windsor
M. Jabri, Sydney University
J. E. Moody, Oregon Graduate Institute
C. E. Pedreira, Catholic Univ. PUC-Rio
A.-P. N. Refenes, London Business School
M. Steiner, Universitaet Augsburg
D. Tavella, Align Risk Analysis
A. Timmermann, U.of Calif., San Diego
H. White, Univ. of California, San Diego
L. Xu, Chinese University of Hong Kong
The Stern School:
Founded in 1900, the Stern School has grown into one of the
most highly ranked business schools in the world. A talented
and diverse student body benefits in many ways from Stern's
long-standing excellence, top faculty and its central New York
City location. Stern offers several specializations in
computational finance that include a highly quantitative MBA
financial engineering track, an MS in statistics with
specialization in financial engineering, and PhD programs in
the fields of finance, statistics and information systems.
Further conferences, symposia and workshops at Stern for 1999
include Derivatives: What's New?; Market Risk: Advances and
Challenges; and Data Mining in Finance.
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