Research Interests:
Publications
10. Cai, Z., Cui, Z., &Simaan, M. (2024). Partial index tracking enhanced mean–variance portfolio. International Journal of Finance & Economics,1–19
9. Lassance, N., Martin-Utrera, A. & Simaan, M. (2024) The Risk of Expected Utility under Parameter Uncertainty Management Science
8. Bonini, S., Shohfi, T. & Simaan, M. (2023) Buy the Dip? European Financial Management
Featured on Bloomberg Markets
7. Khashanah, K., Simaan, M. & Simaan, Y. (2022) Do We Need Higher-Order Comoments to Enhance Mean-Variance Portfolios? Evidence from a Simplified Jump Process. International Review of Financial Analysis,102068.
6. Clark, B., Edirisinghe, C., & Simaan, M. (2022). Estimation risk and the implicit value of index-tracking. Quantitative Finance, 22(2), 303-319.
5. Cui, Z., & Simaan, M. (2021) The opportunity cost of hedging under incomplete information: Evidence from ETF/Ns. Journal of Futures Markets, 41(11), 1775-1796.
4. Clark, B., Feinstein, Z. & Simaan, M. (2020) A Machine Learning Efficient Frontier. Operations Research Letters, 48(5), 630-634.
3. Simaan, M., Gupta, A., & Kar, K. (2020) Filtering for Risk Assessment of Interbank Network. European Journal of Operational Research, 280(1), 279-294.
2. Simaan, M., & Simaan, Y. (2019) Rational Explanation for Rule-of-Thumb Practices in Asset Allocation. Quantitative Finance, 19(12), 2095-2109.
1. Simaan, M., Simaan, Y., & Tang, Y. (2018) Estimation error in mean returns and the mean-variance efficient frontier. International Review of Economics & Finance, 56, 109-124.
Book Chapters
2. Clark, B., Siddique, A. & Simaan, M. (2023) Pricing Model Complexity: The Case for Volatility Managed Portfolios. book chapter in Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices. Edited by A. Capponi and C.A. Lehalle. Cambridge University Press. (link to SSRN)
1. Boudt, K., Cela, M., & Simaan, M. (2020) In search of return predictability: Application of machine learning algorithms in tactical allocation. Machine Learning for Asset Management: New Developments and Financial Applications, 35-73.
Other Publications
2. Simaan, M. (2021) Working with CRSP/COMPUSTAT in R: Reproducible Empirical Asset Pricing. The R Journal.
featured on CRSP (link)
1. Gupta, A., Simaan, M., & Zaki, M. J. (2016) Investigating Bank Failures Using Text Mining. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence.
Working Papers
Huang, S., Simaan, M. & Tang, Y. Measuring Bank Complexity Using XAI
Makridis, C. & Simaan, M. Balancing Returns and Responsibility: Evidence from Shrinkage-based Portfolios
Lu, C, Ndiaye, P. & Simaan, M. Improved Estimation of the Correlation Matrix using Reinforcement Learning and Text-Based Networks
Presented at the 2022 FMA Annual Meeting
Bonini, S., Shohfi, T., Zhou, G. & Simaan, M. The Value of Data: Analyst Vs. Machine
Presented at the 2022 FMA Annual Meeting
Presented at the European FMA 2022 Conference
Presented at the Applied FMA 2022 (slides)
An earlier/different version was presented at FMA 2020 and Eastern Finance Association 2019
Clark, B., Francis, B., & Simaan, M. Pricing Banks: Risk and Return in an Opaque Industry
Accepted for presentation at Diversity Emerging Scholar via the FMA Initiative Program 2020
An earlier version was presented at Eastern Finance Association 2018, FMA Annual Meeting 2017
Gupta, A., Lu, C., Simaan, M., & Zaki, M. J. When Positive Sentiment is Not so Positive: Textual Analytics and Bank Failures
An earlier version was presented at FMA 2017 and INFORMS 2016
Simaan, M. Reproducible Research in Portfolio Selection
Work in Progress
Lu, C & Simaan, M. Valuing Non-Myopic Views in Equity Premium Forecasting
Cai, Z., Cui, Z., Lassance, N. & Simaan, M. The Economic Value of Mean Squared Error: Evidence from Portfolio Selection
Cai, Z., Cui, Z., & Simaan, M. Interpretable Portfolio Learning: Evidence from Shapley Values
Huang, S., Luvishis, E., Maksymenko, L., & Simaan, M. Hedging Linear Risk via Non-Linear Machine Learning