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SPECIAL ISSUE | COMPUTATIONAL
INTELLIGENCE IN FINANCE AND ECONOMICS |
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Important Dates Submission Deadline December 31st, 2017 Notification of Review
Results March 15th, 2018 Submission of Revised
Manuscripts April 15th, 2018 Submission of Final
Manuscript June 15th, 2018 Special Issue Publication Mid-October 2018 (November 2018 Issue) |
CALL FOR PAPERS IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence in Finance and Economics Submission
Deadline: December 31st, 2017 Guest Editors Nanyang
Technological University, Singapore DBmind
Technologies Inc., U.S.A. Carlos III de
Madrid University, Spain Coventry
University, U.K. Aims and Scope Real-world problems in the financial domain often involve complexity,
noise, uncertainty and vagueness. It is difficult to handle such problems by
the conventional analytical and numerical paradigms in addition to the need
to handle large volumes of data at a proper speed. Over the last decades, Computational Intelligence (CI) has been
gaining traction in the domain as both a problem-solving strategy and a new
analysis tool. Conventional methodologies in financial engineering,
predictive analytics and business analytics have various limitations and
usually need significant user/expert engagement in modelling and application.
CI has established a new research track as well as a new stream of
methodologies to be used in financial and economic analysis. Capacity of CI
is much higher than traditional approaches such as econometrics and time
series analysis. CI can handle not only expert knowledge but also knowledge
extracted automatically from data with by utilizing sophisticated algorithms
and computational instruments (e.g. neural networks, fuzzy logic, control
systems). One of the great challenges in conventional models and instruments in
finance and economics is the volume of assumptions and corresponding
requirements such as (but not limited to) normal distribution and convexity
in problems like portfolio optimization, option pricing, algorithmic trading
and risk management. After the 2008 financial crisis, it has been once again
realized that traditional methods are not only incapable of recognizing
irrationally elevated numbers, but these models are also very abstract considering
the complexity of systems led by human judgment. There is an emerging need
and a growing interest in CI solutions for financial engineering and economic
analysis considering the gap arisen from simplifications and abstractions in
traditional instruments such econometric analysis or financial methodologies
with various assumptions (e.g. normality). CI opens the way for completely new approaches like agent-based
computational economics, in which market dynamics are modeled by evolving a
large population of interacting heterogeneous agents. Thanks to the advances in areas like
evolutionary computation or fuzzy systems we can now model autonomous agents
who have the capabilities to adapt, to learn, to have chances and to
strategically interact with others and the surroundings. Genetic programming or grammatical
evolution open the possibilities for automatic trading rule identification,
and artificial neural networks have major roles in domains like early bankruptcy
prediction. From a theoretical perspective, financial management and economics
has established a powerful basis for understanding the monetary and economic
phenomenon. By utilizing the capabilities of CI with proper integration of
fundamental theories, a vast majority of common problems may be managed much
efficiently and accurately. This special issue is dedicated to high-quality
scholarly works and industrial solutions proposing original CI applications
in finance and economics and/or addressing theoretical and practical
challenges through solid empirical evidences. Topics of Interest
include The aim of the special issue is to bring together the latest advances
from both the theoretical and the application side at the intersection of
computational intelligence in finance and economics. Authors are encouraged
to submit high-quality original manuscripts in domains including (but not
limited to): Financial Engineering & Economics Applications • Agent-Based
Computational Economics • Asset Pricing • Business Analytics • Algorithmic
Trading • Electricity/Energy
Markets • Big Data Finance
& Economics • Machine Learning
for Financial Analysis and Forecasting • Financial Data
Mining • Financial
Engineering • Financial Time
Series Forecasting and Analysis • Economic and Financial
Decision Making under Uncertainty • Artificial Immune
Systems • Portfolio
Management and Optimization • Market Simulation • Risk Management • Credit Risk
Modelling • Commodity Markets • Pricing and
Valuation • Term Structure
Models • Trading Strategies • Pricing of
Structured Securities • Asset Allocation • Trading Systems • Hedging Strategies • Risk Arbitrage • Sentiment Analysis
and Behavioral Finance • Low Frequency /
High Severity Event Modelling • Plasticity of
Artificial Systems in Economics and Finance • Exotic Options Computational intelligence techniques considered include (but not
limited to): • Deep Learning and
Artificial Neural Networks • Evolutionary
Computation • Fuzzy Sets, Rough
Sets, & Granular Computing • Financial Data
Mining • Hybrid Systems • Metaheuristics • Support Vector
Machines • Swarm Intelligence • Probabilistic
Modeling/Inference • Intelligent
Trading Agents • Trading Room
Simulation • Time Series
Analysis • Non-linear
Dynamics • Rules and XBRL for
Financial Engineering Applications • Semantic Web and
Linked Data for Computer & Engineering Applications & Models Submission Process The IEEE CIM requires all prospective authors to submit their
manuscripts in electronic format, as a PDF file. The maximum length for
Papers is typically 20 double-spaced typed pages with 12-point font,
including figures and references. Submitted manuscript must be typewritten in
English in single column format. Authors of Papers should specify on the
first page of their submitted manuscript up to 5 keywords. Additional
information about submission guidelines and information for authors is
provided at the IEEE CIM website. Submissions must be sent to https://easychair.org/conferences/?conf=ieeecimsicifer2018 Important Dates Submission
Deadline December
31st, 2017 Notification
of Review Results March
15th, 2018 Submission
of Revised Manuscripts April
15th, 2018 Submission
of Final Manuscript June
15th, 2018 Special
Issue Publication Mid-October
2018 (November
2018 Issue)
Okan Duru Nanyang
Technological University, Singapore Okan Duru is the Assistant Professor of Maritime Logistics and
Finance at Nanyang Technological University, Singapore with a research focus
on computational intelligence for predictive analytics in financial and
economic phenomenon. He received his PhD degree from Kobe University, Japan
on the long-term modelling of shipping freight rates. His research interests
include fuzzy time series, dynamic model mining, learning algorithms for
economic analysis with a special focus on transport economics and finance. He
is also a member of Society for Computational Economics which is officially
recognized by the American Economic Association (AEA). Okan Duru is
particularly interested in intersecting topics between pure economics and
computer science including economic forecasting, business analytics for
economic analysis, predictive analytics and utilizing instrumental capacity
of computer intelligence in common economic and financial problems. Okan Duru
is the current vice-chair for the IEEE Technical Committee on Computational
Finance and Economics.
DBmind
Technologies Inc., USA Robert Golan is an Information/Rules Architect who is “hands on” with
Service/Data Modeling while applying the Industry Standards. Robert is a
specialist in the architecture, design, & development of Cloud based SOA
Web/Restful Services for MDM/Data Warehouses with Business Intelligence and
Business Rules/BPM integrations. Unstructured, Semi-structured, &
Structured Data integrations with taxonomies, ontologies, vocabularies and
Canonical/ Logical/Physical models are his specialty. The Data Sciences have
been his focus since graduate studies where Robert has applied techniques
from Machine Learning, Artificial/Computation Intelligence, Big Data, and
Data Mining. Robert is a pioneer in the application of Computational Intelligence
for Financial Engineering with emphasis on Advanced Algorithmic Trading
strategies and Risk Management. Robert's research abilities coupled with his
work experience, give him an outstanding ability to evaluate and apply new
technologies and products. Robert has over thirty years of experience in
designing, developing, and maintaining information technology systems while
applying the needed information governance mechanisms. Project management,
team leadership, and mentoring have been an integral part of Robert's project
work. He has an extensive background with operating systems, communications,
databases, and the internet. Robert’s domain knowledge cuts across the
Financial, Pharmaceutical, HealthCare, Insurance, Energy, High Tech, and
Agriculture industries. Robert Golan is the current chair for the IEEE
Technical Committee on Computational Finance and Economics. Carlos III de
Madrid University, Spain David Quintana holds Bachelor degrees in Business Administration and
Computer Science. He has an M.S. in Intelligent Systems from Universidad
Carlos III de Madrid and a Ph.D. in Finance from Universidad Pontificia
Comillas (ICADE). He is currently and Interim Associate Professor with the
Department of Computer Science at Universidad Carlos III de Madrid. There, he
is part the bio-inspired algorithms group EVANNAI. His current research
interests are mainly focused on applications of computational intelligence in
finance and economics.
Coventry
University, U.K. Vasile Palade is a Reader in Pervasive Computing in the Faculty of
Engineering and Computing and a member of the Cogent Computing Applied
Research Centre at Coventry University. He previously held academic and
research positions at the University of Oxford, UK (Departmental Lecturer in
the Department of Computer Science), University of Hull, UK (Research Fellow
in the Department of Engineering) and the University of Galati, Romania
(Associate Professor in the Department of Computer Science and Engineering).
He is the author of more than 120 papers in journals and conference
proceedings as well as books on machine learning/computational intelligence
and applications. His research interests include machine learning with
various applications.
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