Internship Research

il y a 4 jours


Paris, France Capital Fund Management Temps plein

**Date**:22 oct. 2024 **Lieu**: Paris, 75, FR **Entreprise**:Capital Fund Management **ABOUT CFM** 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 and goals**: We are offering two opportunities for highly motivated interns to delve into the utilization of Large Language Models (LLMs) in both financial analysis and data coding frameworks. This internship combines two key focus areas, encouraging collaboration and cross-disciplinary learning. In the financial domain, interns will aim to replicate and extend findings from recent academic research by extracting fundamental insights from analyst reports and corporate communications using LLMs. The first task involves assessing LLMs' ability to perform financial statement analysis based on the study "Financial Statement Analysis with Large Language Models," focusing on balance-sheet data and income statements without narrative context. The second financial task includes extracting stock valuation data and parameters, such as discount rates, from analyst reports, informed by the paper "Valuation Fundamentals." **Required Skills and Qualifications**: - Pursuing a degree in Finance, Economics, Data Science, or a related field - Familiarity with financial analysis and data management tools - Basic understanding of LLMs and machine learning concepts - Proficiency in Python or similar programming languages for data analysis **Duration**: 4 to 6 months **Reference**: - Kim, A., Muhn, M., & Nikolaev, V. (2024). Financial statement analysis with large language models. _arXiv preprint arXiv:2407.17866_. - H Décaire, P., & Graham, J. R. (2024). Valuation fundamentals. _John Robert, Valuation Fundamentals (September 09, 2024)_. - Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J.,... & Zaremba, W. (2021). Evaluating large language models trained on code. _arXiv preprint arXiv:2107.03374_. **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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