CIFEr 2026 (IEEE Computational Intelligence in Financial Engineering and Economics) is an academic conference held in Tokyo, Japan on 2026-09-10. The paper submission deadline is 2026-05-15. Acceptance notifications are sent on 2026-07-15.
In recent years, the popularity of generative AI, including ChatGPT, has had a huge impact on machine learning and the fields that use it. The field most affected is undoubtedly natural language processing, but the ripple effect is spreading to other fields that deal with data. For example, in robot control research, generative AI is used to generate commands for robots. Generative AI is also becoming a presence that cannot be ignored in the fields of finance and economics. In particular, in recent years, there has been an increase in economic analysis research targeting text, and language models such as BERT have begun to be used in these studies. Given this, it is only a matter of time before generative AI is used as the next trend.
CIFEr is an international conference that has long focused on the application of information technology to finance and economics. Given this technological trend, we expect a large number of research papers on the application of these new technologies to finance to be presented at CIFEr. In addition, there are an increasing number of cases where alternative data, data that have not been used before, are applied to financial economics, and new technologies are required to process these data.
We expect the papers presented at the conference to include research on the use of data that has yet to be used before, as well as research on the application of new computer intelligence technologies, including generative AI, to the fields of finance and economics. In addition, there is a growing body of research that combines generative AI with long-standing technologies, such as multi-agent combinations, and we look forward to this type of challenging research.
Topics of Interest
Computational Intelligence Areas, Models, and Applications:
Machine Learning in Finance
Large Language Model
Big Data Finance and Economics
Neural Networks
Deep Learning Models in Finance
Data Mining
Text Mining
Probabilistic Modeling/Inference
Fuzzy Sets, Rough Sets, & Granular Computing
Intelligent Trading Agents
Trading Room Simulation
Time Series Analysis
Non-linear Dynamics
Financial Analytics
Financial Data Mining
Evolutionary Computation
Digital Financial Reporting
Semantic Web and Linked Data
Multi-objective Optimisation
Agent Based Modelling and Simulation
Co-evolutionary Techniques
Artificial Life
Evolutionary Game Theory
Particle Swarm Optimisation
Cognitive Systems
Recommendation
Modelling and Problem Representations
Operators
Application Areas: Finance:
Asset allocation strategies
Trading systems
Algorithmic trading
Trade execution systems
Risk management
Pricing of structured securities
Behavioural finance
Evolutionary finance
Portfolio optimisation
Arbitrage
Exotic options
Cryptocurrencies
Blockchain and applications
Front and back office operations
Financial prediction and forecasting
Application Areas: Economics:
Agent based computational economics
Market modelling
Energy and electricity markets
Blockchain economics
Application Areas: Business:
Business analytics
Recommender systems
E-commerce
Advertising and marketing
Crowds and market models
Demand forecasting
Distribution and supply chain