Mahsa Safavi Iranji; Majid Zanjirdar; Mojgan Safa; Hossein Jahangirnia
Abstract
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. ...
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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.
