IEEE Computational Intelligence Magazine

SPECIAL ISSUE ON COMPUTATIONAL INTELLIGENCE IN FINANCE AND ECONOMICS

Submission Deadline: December 31st, 2017


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

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

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.