6th IEEE International Workshop on Dynamic Data Science and Big Data Analytics in Finance (DDS-BDAF 2026)

Goal of the Workshop 

The DDS-BDAF workshop aims to foster cooperation among academics, researchers, and practitioners in dynamic data science and big data applications in finance. This workshop will enable exchange of ideas on latest academic/industrial experience for novel forecasting models, dynamic modeling of big data in finance, developing novel filtering, smoothing and forecasting algorithms for algorithmic trading, machine learning and risk management. Current advances in the use of large language models in finance and decentralized finance are also of focus in the 6th edition of this workshop.

Workshop theme

Today, principles of computational finance are combined with advanced mathematical structures and dynamic data science to form useful financial models, strategies and products that are tested and implemented with the use of novel quantitative techniques such as smoothing/filtering/forecasting in both traditional and decentralized finance worlds. Use of computing technology is pervasive throughout this process.

Computational Finance is an area referred to under a variety of names, for example, financial engineering, quantitative finance, and mathematical finance. In all cases there is an effort that involves financial, mathematical, quantitative and computational thinking to build, test and implement models that are at the center of financial activities. In the last decade, Computational Finance has influenced the market extensively with enormous impact on wealth building, employment opportunities, and tremendous economic growth. This field forms an ever-expanding part of the financial sector, in numerous ways today. Use of high-performance computers for research in computational finance has grown steadily in the last decade especially due to large volume of data to be analyzed (in both traditional and decentralized finance). Emergence of generative AI and large language models (LLMs) has revolutionized the field to a great extent for predictive analytics. 

Machine Learning (ML), Computational Intelligence (CI) models have become an essential part in finance industry for many decision processes including algorithmic trading. Supervised learning is the most widely utilized form of machine learning. Its goal is to predict the response from the associated features. Regularization puts extra constraints on a machine learning model and enhance the predictive performance of the dynamic models, and these constraints and penalties are designed to encode specific kind of prior knowledge. Algorithmic trading uses these concepts to place a trade and generate profits at a speed and frequency that is impossible for a human trader. As the models and techniques are developed and published, algo trading is becoming a tool for common investors for online trading, which otherwise has been a profitable trading strategy for professional traders. This workshop will further this direction of research. 

Scope of the Workshop:

Broad topics include the following non-exhaustive list:

• Advances in financial modelling
• Big data Analytics in Finance
• Forecasting Financial market (stock price, stock price movement)
• Financial Risk forecasting
• Financial credit score
• Portfolio Management
• Algorithmic, high frequency trading
• Derivatives Pricing
• Decentralized and digital finance
• Cryptocurrencies (trends and mining transactions etc.)
• Large Language Models (LLMs) in finance
• Electricity Market’Bridging Health and Finance through Dynamic Data Science
• Integrating Financial Models in Health Research