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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

Okan Duru

Nanyang Technological University,

Singapore

 

Robert Golan

DBmind Technologies Inc.,

U.S.A.

 

David Quintana

Carlos III de Madrid University,

Spain

 

Vasile Palade

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)

 

Guest Editors

 

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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.

 

 

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Robert Golan

DBmind Technologies Inc., USA

 

Robert Golan is an Information/Rules Architect who is ghands onh 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fs 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.

 

 

 David Quintana Montero

David Quintana

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.

 

 

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Vasile Palade

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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