Emiel Lemahieu
About Emiel Lemahieu
Emiel Lemahieu is a Quantitative Analyst currently employed at InvestSuite and Ghent University. He has authored a working paper on portfolio optimization and has a strong academic background in finance and economics.
Work at InvestSuite
Emiel Lemahieu serves as a Quantitative Analyst at InvestSuite, a position he has held since 2020. In this role, he focuses on applying quantitative methods to enhance investment strategies. He has contributed to a joint project between InvestSuite and the University of Ghent, which centers on financial machine learning. This collaboration aims to leverage advanced analytical techniques to improve financial decision-making processes.
Education and Expertise
Emiel Lemahieu holds a Master of Science in Finance Analytics from King's College London, which he completed from 2019 to 2020. He also studied at The City University of New York, specifically at Baruch College, for an exchange program in 2018. His academic background includes a Bachelor of Science in Applied Economic Sciences: Business Engineering and a Master of Science in Business Engineering, both from Ghent University, where he graduated Summa Cum Laude. This educational foundation supports his expertise in quantitative analysis and financial modeling.
Background
Emiel Lemahieu has a diverse professional background that includes teaching and financial analysis. Prior to his current role at InvestSuite, he worked as a Math Teacher at FluoTopics from 2017 to 2019. He gained practical experience as an intern at CapitalatWork Foyer Group and BNP Paribas Fortis, where he focused on financial analysis and data management. Since 2021, he has also been a researcher at Ghent University, further enhancing his analytical skills and knowledge in finance.
Research Contributions
Emiel authored a working paper titled 'Data-driven portfolio drawdown optimization with generative ML models' in collaboration with Professor Kris Boudt. His research is supported by the Baekeland mandate on a project titled 'A generalized iVaR approach for learning smooth-ride investment portfolios.' These contributions reflect his commitment to advancing the field of quantitative finance through innovative research and practical applications.