In order to evaluate the impact of k-means clustering on portfolio optimization, this study groups enterprises based on profitability, liquidity, and solvency indicators. The study confirms the positive correlation between risk, return, and risk-adjusted performance through an analysis of historical financial records. After the companies were divided into two groups, equal-weighted portfolios were created using these groupings. Although they produced higher returns, cluster 1 portfolios, which included more risky companies, also showed more volatility. Cluster 0 portfolios, on the other hand, offered less risk and more consistent results. Portfolios clustered by ROA, OCFM, and GPM outperformed the market benchmark and produced the highest returns adjusted for risk, according to Sharpe Ratio analysis. Furthermore, the study emphasizes that although solvency and liquidity metrics play a role in portfolio selection, increased liquidity does not always translate into improved risk-adjusted performance. In terms of methodology, Silhouette Analysis outperformed the Elbow technique in determining the optimal number of clusters. All things considered, the results show how data-driven clustering techniques may be used to align portfolio strategies to investors’ risk tolerances.

This paper aims to present a concentrated overview of innovative research in machine learning clustering techniques as applied to to different facets of financial markets and stock market investing. Research on techniques such as K-Means Clustering or Agglomerative Hierarchical Clustering and their derivatives play a pivotal role in augmenting the stock market research and investment strategies. Wheter it is time series clustering and prediction, portfolio selection and optimization, or risk management, machine learning clustering has potential to enhance already existing processes by improving performance, reducing time spent on repetitive tasks or mitigating human errors. A truly innovative tool in the investor’s toolset, it is imperative to not overlook its limitations, such as the necessity of selecting the appropriate technique for specific datasets, or the need for human supervision to maximize its utility and insights extracted.

Portfolio optimization plays a crucial role in investment decision-making by balancing risk and return objectives. With the aim of improving portfolio performance, while enhancing risk management, this literature review explores traditional and artificial intelligence-powered approaches for portfolio optimization. From the traditional methods of portfolio optimization, methods such as random matrix theory, shrinkage estimators, correlation asymmetries and partial correlation networks are presented. While, from the artificial intelligence realm, techniques such as machine learning efficient frontiers, performance-based regularization, neural network predictors and deep learning models for direct optimization of portfolio Sharpe ratio are highlighted. Intertwining the traditional methods, with artificial intelligence techniques, this review highlights relevant portfolio optimization research useful for academics and practitioners alike.

The taxonomic structure consists of 3 classes, 10 families, 12 genera and 13 species, of which 6 parasitic species belong to the Cestoda class (Skrjabinotaenia lobata, Catenotaenia cricetorum, Hydatigera taeniaeformis, Hydatigera taeniaeformis, Taenia pisiformis, Paranoplocephala omphaloides, Rodentolepis straminea), with a share of 46.1% of the total species, 5 species - to the Secernentea class (Syphacia obvelata, Syphacia stroma, Heligmosomoides polygyrus, Mastophorus muris, Strongyloides ratti) with a share of 38.5%, and 2 species – to the Adenophorea class (Trichuris muris, Capillaria hepatica), constituting 15.4% of the total identified species.