Majeed Simaan

Majeed Simaan is a tenure-track assistant professor of Finance and Financial Engineering at the School of Business at Stevens Institute of Technology (SIT). He holds a Ph.D. in Finance from Rensselaer Polytechnic Institute (RPI) 2018. His research interests revolve around Risk Management, with a focus on Asset allocation and Pricing. He is well versed in quantitative and computational finance-related research areas, such as financial networks (interconnectedness), machine learning, and textual analysis. His research has been presented globally and published in the European Journal of Operational Research, Quantitative Finance, Journal of Futures Markets, International Review of Economics and Finance, Operations Research Letters, and the Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence. Recently, he published two book chapters on Machine Learning for Asset Management. Prior to joining SIT, he worked as a part-time data scientist for Financial Network Analytics (FNA) during the summer of 2018. Additionally, Simaan pursued graduate training in the area of Mathematical Finance at the London School of Economics (LSE) for one year before joining RPI for his Ph.D. studies. While in London, he worked as a part-time Quantitative Analyst for Pantheon Ventures. He is also an active member of the R programming community, promoting a free software environment for statistical computing and data science. He holds both BA and MA in Statistics from the University of Haifa with a specialization in actuarial science.

Research

Research interests revolve around Risk Management, with a focus on Asset allocation and Pricing. Applications cover quantitative and computational finance-related tools, such as financial networks (interconnectedness), machine learning, and textual analysis.

Education

• Doctor of Philosophy (Ph.D.) Rensselaer Polytechnic Institute (2013 — 2018)
• London School of Economics (2012 — 2013)
• Tel Aviv University (2011 — 2012)
• Master's degree University of Haifa

Selected Publications

BOOK CHAPTER

• Simaan, M.; Boudt, K.; Cela, M. (2020). In Search of Return Predictability: Application of Machine Learning Algorithms in Tactical Allocation. Machine LearninMachine Learning for Asset Management: New Developments and Financial Applicationsg and Asset Management. Hoboken: ISTE Ltd 2020. Published by ISTE Ltd and John Wiley & Sons, Inc.

CONFERENCE PROCEEDING

• Simaan, M. (2016). Investigating bank failures using text mining. IEEE Symposium Series on Computational Intelligence (SSCI). https://ieeexplore.ieee.org/abstract/document/7850006.

JOURNAL ARTICLE

• Cui, Z.; Simaan, M. (2021). The Opportunity Cost of Hedging under Incomplete Information: Evidence from ETF/Ns. Journal of Futures Markets.
• Clark, B.; Edirisinghe, C.; Simaan, M. (2021). Estimation Risk and Implicit Value of Index-Tracking. Quantitative Finance.
• Simaan, M. (2021). Working with CRSP/COMPUSTAT in R: Reproducible Empirical Asset Pricing. The R Journal.
• Clark, B.; Feinstein, Z.; Simaan, M. (2020). A machine learning efficient frontier. Operations Research Letters (5 ed., vol. 48, pp. 630-634).
• Simaan, M.; Gupta, A.; Kar, K. (2020). Filtering for risk assessment of interbank network. European Journal of Operational Research.
• https://doi.org/10.1016/j.ejor.2019.06.049.
• Simaan, M.; Simaan, Y. (2019). Rational explanation for rule-of-thumb practices in asset allocation. Quantitative Finance.
• Simaan, M.; Simaan, Y.; Tang, Y. (2018). Estimation error in mean returns and the mean-variance efficient frontier. International Review of Economics & Finance.