Abstract
Abstract. Introduction. Portfolio optimization modelling is a study aimed at determining the best way to allocate an investor's funds between securities of different companies. The study uses a number of methods and approaches, such as: multicriteria optimization methods - optimization of criteria for minimum and maximum, modeling of the optimal stock portfolio with minimal risk, to determine the best way to allocate funds that will help investors achieve maximum return while minimizing risk. Purpose. There are a lot of publicly traded companies in the modern American stock market. Having chosen the companies that may grow in the future, the problem of allocating your funds among them remains relevant. Building an optimal portfolio of securities using various economic and mathematical methods solves the problem of allocating financial resources, focusing on the desired future profit and the level of risk exposure. Results. The model for this study was built using one of the methods of multi-criteria optimization - criteria convolution, taking into account portfolio diversification and the specified constraints. The optimization is based on the "Modern Portfolio Theory" of the prominent scientist Harry Markowitz. Conclusions. As a result, we built an optimal and diversified portfolio of shares, in which each company on the list represents at least 1%. All constraints have been met and the main conditions have been fulfilled - the portfolio minimizes risk and maximizes profit. With a minimum risk of 5.39%, we expect a return of 1.75%. Such results can be obtained if we use a convolution of the criteria where the preference is given to minimizing risk - 0.7. The largest contributors to the portfolio were the following companies: T-Mobile Us Inc., McKesson Corporation, The Kroger Co., Microsoft Corporation, and Apple Inc. These companies account for a significant portion of the portfolio - 65%. Given the focus on portfolio diversification and the ratio of risk to potential return, we can say that the portfolio is efficient and can be used in practice.
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