Majeed Simaan (msimaan)

Majeed Simaan

Assistant Professor

School of Business


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.

Institutional Service

  • Undergraduate Studies Committee Member
  • School of Business Research Committee Member
  • Financial Engineering Research Committee Member
  • Finance PhD Committee Member
  • Financial Engineering Research Committee Member
  • Finance PhD Committee Member
  • Brownbag Member
  • Committee for Teaching Effectiveness Evaluations Member
  • Finance search committee Member

Professional Service

  • Global Association for Risk Professionals Member

Professional Societies

  • AFA – American Finance Association Member
  • FMA – Financial Management Association Member
  • NFA – Northern Finance Association Member
  • GARP – Global Association of Risk Professionals Member
  • EFA – European Finance Association Member
  • EFA – Eastern Finance Association Member

Selected Publications

Book Chapter

  1. Clark, B.; Siddique, A.; Simaan, M. (2023). Pricing Model Complexity: The Case for Volatility Managed Portfolios. Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices. . Cambridge University Press.
  2. 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

  1. Simaan, M. (2016). Investigating bank failures using text mining. IEEE Symposium Series on Computational Intelligence (SSCI).

Journal Article

  1. Bonini, S.; Shohfi, T.; Simaan, M. (2023). Buy the Dip?. European Financial Management.
  2. Khashanah, K.; Simaan, M.; Simaan (2022). Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process. International Review of Financial Analysis. Hoboken.
  3. Cui, Z.; Simaan, M. (2021). The opportunity cost of hedging under incomplete information: Evidence from ETF/Ns. Journal of Futures Markets (11 ed., vol. 41, pp. 1775-1796). Wiley.
  4. Clark, B.; Edirisinghe, C.; Simaan, M. (2021). Estimation Risk and Implicit Value of Index-Tracking. Quantitative Finance.
  5. Simaan, M. (2021). Working with CRSP/COMPUSTAT in R: Reproducible Empirical Asset Pricing. The R Journal.
  6. Clark, B.; Feinstein, Z.; Simaan, M. (2020). A machine learning efficient frontier. Operations Research Letters (5 ed., vol. 48, pp. 630-634).
  7. Simaan, M.; Gupta, A.; Kar, K. (2020). Filtering for risk assessment of interbank network. European Journal of Operational Research.
  8. Simaan, M.; Simaan, Y. (2019). Rational explanation for rule-of-thumb practices in asset allocation. Quantitative Finance.
  9. Simaan, M.; Simaan, Y.; Tang, Y. (2018). Estimation error in mean returns and the mean-variance efficient frontier. International Review of Economics & Finance.


Financial Risk Management
QF-435: Risk Management for Capital Markets
FE-535: Introduction to Financial Risk Management
FA-636: Advanced Financial Risk Analytics

Financial Engineering
FE-530: Introduction to Financial Engineering

Money and Banking
BT-440: Introduction to Banking and Credit

Ph.D. Level
FE 960 - Research in Financial Engineering
MGT 960 - Research in Finance