Internship research - Graph inference in financial markets
Paris, 75, FR
ABOUT CFM
Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.
We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.
Overview
Graphs are powerful tools for modeling complex systems.
By leveraging graph inference techniques, we can unravel the intricate web of relationships and dependencies that exist within financial markets. The primary aim of this project is to explore, implement, and analyze various methods for learning graph structures in both synthetic models and financial datasets.
Goals
The intern will delve into graph learning methods, with a particular focus on techniques such as score matching and minimum probability flow, recently employed in diffusion models. These methods will be evaluated for their efficacy in capturing dependencies within graph structures.
The initial phase will involve creating toy models that simulate financial networks. These controlled environments will allow for thorough testing of inference algorithms under various scenarios, helping to identify the strengths and limitations of each approach.
Potential applications of this research span numerous areas within finance. Efficient graph models can enhance risk management strategies,
improve the understanding of inter-company dependencies, and contribute to the development of alpha-generating strategies.
Finally, the intern will apply the developed techniques to real financial data, aiming to validate the models' effectiveness in practical scenarios. This will involve addressing challenges like data sparsity and noise, which are prevalent in real-world datasets.
Profile
Student in second year of master's degree in physics, computer science, statisical mechanics or equivalent.
Duration
5 to 6 months - Flexible starting date
EQUAL OPPORTUNITIES STATEMENT
We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.
CFM is a signatory of the Women Empowerment Principles.
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