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Aims & Scope

In the last decade, the increasing amount of data produced in the financial sector has focused the attention of scholars and practitioners on their application, using Big Data Analytics techniques and Artificial Intelligence (AI) models to address interesting economic questions. An increasing number of people has experienced an exponential growth of interest in the stock market, in which assets worth billions of dollars are traded every day, with investors acting on the market with the desire to achieve a profit over their investment horizon. These analyses are increasingly used in the finance sector to deal with, but are not limited to, the following issues: (i) the actual economic problem involves lots of variables; (ii) the impact of the variables is highly nonlinear or involves interaction terms among the variables (high dimensionality of function class); and (iii) forecasting is economically more important than statistical inference. Hence, the increasing amount of data produced in the financial sector led scholars and practitioners to focus their research interest on Big Data Analytics and Artificial Intelligence techniques for the analysis of the financial domain, where accurate predictions allow investors to make informed decisions, reduce the risk of herding behavior and the bursting of financial bubbles.

To address several open research issues regarding the analysis of the financial domain, this track aims at soliciting contributions highlighting challenges, state-of-the-art, and solutions to a set of currently unresolved key questions to model complex interactions and relations, and their dynamics and impact on big financial data analytics and complex financial behaviors across financial markets, regulation, and risk management. 

Topics of interest include but are not limited to:

1. Graph representation learning, mining learning on graph structures from financial data;
2. Social Network Analysis for supporting financial applications (e.g., Stock Forecasting, Credit Score, Portfolio Optimization, Robot Advisor, Trading);
3. Application of Large Language Models (LLMs) to analyze unstructured information for supporting financial applications (e.g., Stock Forecasting, Credit Score, Portfolio Optimization, Robot Advisor, Trading);
4. Multi-modal models for supporting financial applications (e.g., Stock Forecasting, Credit Score, Portfolio Optimization, Robot Advisor, Trading);
5. Modeling natural, online, social, economic, cultural, and political factors in finance;
6. Environmental, social, governance (ESG) event discovery, evaluation, and impact assessment;
7. Anomaly Detection in financial data to support different applications;
8. Representation learning, natural language processing, and time series prediction in financial domain;
9. Multi-agent systems and game-theoretic analysis of financial domain;
10. Security, and privacy of AI & ML systems in the financial domain;
11. eXplainable Artificial Intelligence (XAI) methodologies in the financial domain.

For any other questions regarding this Track, please get in touch with the Chairs (This email address is being protected from spambots. You need JavaScript enabled to view it.).