REFERENCE
Asawa, Y. (2021). Modern machine learning solutions for portfolio selection. IEEE Engineering Management Review, 50(1), 94–112.
https://ieeexplore.ieee.org/abstract/document/9627810.
Cajas, D. (2023). A graph theory approach to portfolio optimization. SSRN Working Paper.
https://dx.doi.org/10.2139/ssrn.4602019.
Ciciretti, V., & Bucci, A. (2023). Building optimal regime-switching portfolios. Economic Modelling and Finance, 64, S1062940822001723.
https://doi.org/10.1016/j.najef.2022.101837.
Deković, D., & Šimović, P. P. (2025). Hierarchical risk parity: Efficient implementation and real world analysis. Future Generation Computer Systems, 107, 107744.
https://doi.org/10.1016/j.future.2025.107744.
DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22(5), 1915–1953.
https://doi.org/10.1093/rfs/hhm075.
Dogan, A., & Birant, D. (2022). K-centroid link: A novel hierarchical clustering linkage method. Applied Intelligence, 52, 1–24.
https://doi.org/10.1007/s10489-021-02624-8.
Duarte, F. G., & De Castro, L. N. (2020). A framework to perform asset allocation based on partitional clustering. IEEE Access, 8, 110775–110788.
https://doi.org/10.1109/access.2020.3001944.
Ferretti, S. (2022). On the modeling and simulation of portfolio allocation schemes: An approach based on network community detection. Computational Economics, 62(3), 969–1005.
https://link.springer.com/article/10.1007/s10614-022-10288-w.
Ferri, R. (2010). All about asset allocation (2nd ed.). McGraw-Hill Education.
https://search.worldcat.org/title/663882714.
Hallin, M., & Trucíos, C. (2023). Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach. Econometrics and Statistics, 27, 1–5.
https://doi.org/10.1016/j.ecosta.2021.04.006.
Huang, W., & Gao, X. (2021). Evaluating hierarchical equal risk contribution portfolios in the Chinese stock market. Journal of Mathematical Finance, 12(1), 179–195.
https://doi.org/10.4236/jmf.2022.121011.
Jain, P., & Jain, S. (2019). Can machine learning-based portfolios outperform traditional risk-based portfolios? The need to account for covariance misspecification. Risks, 7(3), 74.
https://doi.org/10.3390/risks7030074.
Lim, T., & Ong, C. (2020). Portfolio diversification using shape-based clustering. Journal of Financial Data Science, 3(1), 111.
https://doi.org/10.3905/jfds.2020.1.054.
López de Prado, M. (2016). Building diversified portfolios that outperform out of sample. Journal of Portfolio Management, 42(4), 59–69.
https://doi.org/10.3905/jpm.2016.42.4.059.
Maillard, S., Roncalli, T., & Teïletche, J. (2010). The properties of equally weighted risk contribution portfolios. Journal of Portfolio Management, 36(4), 60–70.
https://doi.org/10.3905/jpm.2010.36.4.060.
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.
https://doi.org/10.2307/2975974.
Menvouta, E. J., Serneels, S., & Verdonck, T. (2023). Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets. Journal of Financial Data Science, 9, 100097.
https://doi.org/10.1016/j.jfds.2023.100097.
Millea, A., & Edalat, A. (2022). Using deep reinforcement learning with hierarchical risk parity for portfolio optimization. International Journal of Financial Studies, 11(1), 10.
https://doi.org/10.3390/ijfs11010010.
Nourahmadi, M., & Sadeqi, H. (2021). Hierarchical risk parity as an alternative to conventional methods of portfolio optimization: A study of Tehran Stock Exchange. Iranian Journal of Finance, 5(4), 1–24.
https://doi.org/10.30699/ijf.2021.289848.1242.
Nourahmadi, M., & Sadeqi, H. (2022). A machine learning-based hierarchical risk parity approach: A case study of a portfolio consisting of stocks of the top 30 companies on the Tehran Stock Exchange. Financial Research Journal, 24(2), 236–256.
https://doi.org/10.22059/frj.2021.319092.1007146.
Nourahmadi, M., & Sadeqi, H. (2023). Portfolio diversification based on clustering analysis. Iranian Journal of Accounting, Auditing and Finance, 7(3), 1–16.
https://doi.org/10.22067/ijaaf.2023.43078.1092.
Nourahmadi, Marziyeh, rasti, Fatemeh, & SADEGHI, HOJATOLLAH. (2021). A Review of Research on Financial Time Series Clustering: A Bibliometrics Approach. ADVANCES IS FINANCE AND INVESTMENT, 2(2 ), 23-57. SID.
https://sid.ir/paper/391770/en
Nourahmadi, M. , Rahimi, A. and Sadeqi, H. (2024). Designing a Stock Recommender System Using the Collaborative Filtering Algorithm for the Tehran Stock Exchange. Financial Research Journal, 26(2), 318-346. doi: 10.22059/frj.2023.360955.1007479
Raffinot, T. (2017). Hierarchical clustering-based asset allocation. Journal of Portfolio Management, 44(2), 89–102.
https://doi.org/10.3905/jpm.2018.44.2.089.
Raffinot, T. (2018). The hierarchical equal risk contribution portfolio. SSRN Electronic Journal.
https://dx.doi.org/10.2139/ssrn.3237540.
Rostami, M. , Rasti, F. and Abbasi, E. (2025). Copula-Based Risk Modeling: A Comparative Analysis of MCAViaR and Gaussian Copulas for Global Indices. Journal of Mathematics and Modeling in Finance, 5(2), 77-106. doi: 10.22054/jmmf.2025.86227.1187
Safavi Iranji, M., Zanjirdar, M., Safa, M., & Jahangirnia, H. (2024). Asset allocation using nested clustered optimization algorithm: A novel approach to risk management in portfolio. Journal of Mathematical Modeling in Finance, 4(2), 137–157.
https://doi.org/10.22054/jmmf.2025.82388.1149.
Sajadi, S. M. A., Barak, S., & Fereydooni, A. (2024). Online portfolio selection using macroeconomic pattern matching. SSRN Electronic Journal.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4960922.
Schwendner, P., Papenbrock, J., Jaeger, M., & Krügel, S. (2021). Adaptive seriational risk parity and other extensions for heuristic portfolio construction using machine learning and graph theory. Journal of Financial Data Science, 3(4), 65–83. DOI: 10.3905/jfds.2021.1.078.
Sen, J., &
Dutta, A. (2023). Portfolio optimization for the Indian stock market. In Encyclopedia of Data Science and Machine Learning (pp. 1904–1951). IGI Global.
https://www.igi-global.com/chapter/portfolio-optimization-for-the-indian-stock-market/317595.
Sen, J., &
Mehtab, S. (2021). A comparative study of optimum risk portfolio and eigen portfolio on the Indian stock market. International Journal of Business Forecasting and Marketing Intelligence, 7(2), 143–193.
https://doi.org/10.1504/IJBFMI.2021.120155.
Seyfi, S. M., Sharifi, A., & Arian, H. (2021). Portfolio value-at-risk and expected shortfall using an efficient simulation approach based on Gaussian mixture model. Mathematics and Computers in Simulation, 190, 1056–1079.
https://doi.org/10.1016/j.matcom.2021.05.029.
Sjöstrand, D., Behnejad, N., & Richter, M. (2020). Exploration of hierarchical clustering in long-only risk-based portfolio optimization (Doctoral dissertation). Copenhagen Business School, Copenhagen.
https://research.cbs.dk/files/62178444/879726_Master_Thesis_Nima_Daniel_15736.pdf.
Venugopal, M., & Sophia, S. (2020). Examining Sharpe ratio, ASR, Sortino, Treynor and information ratio in Indian equity mutual funds during the pandemic. International Journal of Management, 11(11).
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3780440.
Zhao, Y., & Karypis, G. (2002). Evaluation of hierarchical clustering algorithms for document datasets. In Proceedings of the International Conference on Information and Knowledge Management (pp. 515–524).
https://doi.org/10.1145/584792.584877.