asset allocation using nested clustered optimization algorithm: a novel approach to risk management in portfolio

‎given the widespread increase in classical and emerging models for asset allocation in investment portfolios available in the capital market‎, ‎investors find it challenging to easily compare classical methods and machine learning techniques to identify the optimal investment combination‎. ‎the aim of this research is to compare asset allocation based on the nested clustering algorithm (nco) with classical portfolios‎. ‎this study has been conducted in a practical and descriptive-analytical manner‎, ‎with the statistical population consisting of all companies listed on the tehran stock exchange and the iran farabourse from 2013 to 2022‎. ‎after screening‎, ‎adjusted daily data from 88 companies were selected as the final sample for statistical analysis‎. ‎in this context‎, ‎the kruskal-wallis test was used to examine the hypotheses‎, ‎and python‎, ‎spss‎, ‎and excel software were utilized‎. ‎based on the overall performance evaluation criteria for portfolios (sharpe ratio‎, ‎sortino ratio‎, ‎maximum drawdown‎, ‎value at risk‎, ‎and expected shortfall)‎, ‎the results of the hypothesis tests in this research indicate that the methods based on the nested clustering optimization algorithm outperform their classical counterparts significantly‎. ‎therefore‎, ‎it can be concluded that portfolios based on machine learning algorithms perform better than classical portfolios‎.‎