Motivation and goals
Financial technologies (FinTech) consists in integrating data-driven approach to aid in the financial-market understanding and provide insights to the financial decision-making. Recently, with the availability of copious structured and non-structured data on a one hand, and the advances in machine learning techniques on the other hand, new computations models have been elaborated in academia and even emerged in the industrial domain. Some typical examples of widely used machine learning in finance include: modeling of asset prices; simulation for financial risk management; optimization and meta-heuristics models for capital allocation and financial planning, computational intelligence and machine DL for predictive analytics; multiple criteria decision aid for financial analysis.
Textual unstructured data in finance, either in the form of semi-structured documents or in the form of news articles are becoming available and accessible in multiple financial channels or even in social media. Studying the impact of such publications on the sentiment of the financial markets and on the behavior of market agents is a challenging NLP task for instance.
The aim of this workshop is to bring together researchers and practitioners from Natural Language Processing (NLP), Data Science (DS), Machine Learning (ML) and other Artificial Intelligence (AI) disciplines, and the financial domain.
Academic or industrial work on different NLP tasks such as sentiments analysis, information retrieval/filtering and text summarization, applied in financial scenarios including banking, insurance, and investment is welcome. Special interest in this workshop will be addressed to the insights provided by NLP in price prediction.
Workshop Schedule
- 13.30-14.00: H. Ghorbel. Do emotions and sentiments impact Forex trading—evidence from Reuters data.
- 14.00-14.30: Pedro Costa. Bert-Based Hierarchical Aggregation Model to Predict Trading.
Short CV of the organizer
Dr. Hatem Ghorbel is a professor of computer sciences at the HES-SO. He is the head of the Data Analytics Group at the HE-Arc Ingénierie. He has a strong academic experience in the field machine learning and is a leader of several theoretical and applied research projects. His domain of interest includes textual and scientific data analysis using statistical approaches, machine learning techniques, and linguistic modeling. He is the author of more than 30 scientific articles.