Bernardo Dias Raimundo
Guest Professor at NOVA Information Management School (NOVA IMS)
Biographical Note | Pedagogical Experience| Main Publications | Research Areas
Bernardo Dias Raimundo is a Ph.D. student in Information Management and Guest Lecturer at NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa.
His research focuses on decision-making under uncertainty, with particular interest in model uncertainty and estimation risk. His work draws methodological parallels between forecast combination and portfolio theory, applying methods such as shrinkage estimation, robust optimisation, and convex programming. He also explores the integration of statistical methods with economic reasoning and the development of interpretable approaches to problems of risk and allocation.
He holds an MSc in Statistics and Information Management, with a specialisation in Risk Analysis and Management, from NOVA IMS, and a BSc in Economics from ISEG (Lisbon School of Economics & Management), Universidade de Lisboa.
Prior to academia, he worked in internal audit and consulting for both financial and non-financial institutions.
Master’s (2nd Cycle):
- Banking and Insurance Economics, NOVA IMS
- Credit Risk Management, NOVA IMS
- Principles of Corporate Finance, NOVA School of Law
Postgraduate:
- Deep Learning Methods in Finance, NOVA IMS
- Portfolio Management, NOVA IMS and ISCTE Executive Education
Articles
- Raimundo, B., & Bravo, J. M. (2026). “Forecasting Meets Portfolio Theory: A Bibliometric Approach to Decision-Making under Uncertainty.” Humanities and Social Sciences Communications, 13, 915. https://doi.org/10.1057/s41599-026-07252-6
Book Chapters
- Raimundo, B., & Bravo, J. M. (2024). “Credit Risk Scoring: A Stacking Generalization Approach.” In A. Rocha, H. Adeli, G. Dzemyda, F. Moreira, & V. Colla (Eds.), Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, 799. Springer, Cham. https://doi.org/10.1007/978-3-031-45642-8_38
Conference Proceedings
- Raimundo, B., & Bravo, J. M. (2024). “Enhancing Portfolio Optimization with Machine Learning Methods: A Comparative Study Using Commodity Markets Data.” MCIS 2024 Proceedings, 39. https://aisel.aisnet.org/mcis2024/39
- Decision-Making under Uncertainty
- Forecasting and Forecast Combination
- Portfolio Theory and Management
- Estimation Risk and Model Uncertainty
- Data Science Methods in Finance
- Robust Optimisation