Majeed Simaan

Majeed Simaan is an Assistant Professor of Finance and Financial Engineering at the School of Business, Stevens Institute of Technology. He holds a Ph.D. in Finance from Rensselaer Polytechnic Institute (RPI), completed in 2018, and joined Stevens as a tenure-track faculty member thereafter. His academic work focuses on risk management, asset allocation, and pricing, with applications in quantitative and computational finance.

Research Interests: His primary research revolves around Financial Risk Management (FRM), with emphasis on asset allocation, portfolio optimization, and financial networks. He integrates machine learning, textual analysis, and network modeling to enhance traditional financial models. His work addresses estimation risk, model interpretability, and the application of reinforcement learning in portfolio construction. He is also a strong advocate for reproducible research and open-source software, particularly using R for empirical finance.

The recent publications highlight a strong trend toward integrating machine learning and data science into financial decision-making. Topics include volatility modeling, portfolio efficiency, index tracking, and behavioral aspects of investing such as the 'buy the dip' strategy. The research spans theoretical modeling, empirical validation, and practical implementation, often leveraging public datasets and open tools.

Scientific Awards and Recognition:

• Certified Financial Risk Manager (FRM) from GARP (August 2023)
• Research funded by the National Science Foundation (NSF) via CRAFT
• Featured in Bloomberg Markets and other media outlets for research on investor behavior

Advising and Grants: Dr. Simaan mentors Ph.D. students through research courses such as FE 960 and MGT 960. His research has been supported by competitive grants, including funding from the NSF. He has served on key institutional committees including the Finance PhD Committee, Research Committee, and Teaching Effectiveness Evaluations Committee, reflecting his active role in academic governance.

Labs and Research Teams: While no formal lab is named, his work is associated with computational and data-driven finance research, often involving collaboration on machine learning applications in finance. He maintains an active presence on RPubs and GitHub, sharing reproducible research vignettes on topics like financial networks, volatility modeling, and tactical asset allocation.