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.
Prior to joining SIT, I worked as a part-time data scientist for Financial Network Analytics (FNA) during the of summer 2018. While in London, I worked as a part-time Quantitative Analyst for Pantheon Ventures. I am also an active member of the R programming community, promoting a free software environment for statistical computing and data science.
- Financial Engineering Research Committee Member
- Finance PhD Committee Member
- Brownbag Member
- Finance search committee Member
- FMA – Financial Management Association Member
- GARP – Global Association of Risk Professionals Fellow
- EFA – Eastern Finance Association Member
- 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..
- Simaan, M. (2016). Investigating bank failures using text mining. IEEE Symposium Series on Computational Intelligence (SSCI).
- 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.
- 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.