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.‎