Computational Approaches To Solving Large-Scale Optimization Problems In Finance

Abstract

The growing complexity and scale of financial markets has forced the development of advanced computer tools for solving large-scale optimization issues. This study investigates the use of modern computational approaches, such as machine learning algorithms, metaheuristic techniques, and parallel computing, to optimise financial portfolios, risk management strategies, and trading algorithms. Traditional optimization methods frequently fail to handle financial data's high dimensionality and non-convexity, resulting in poor solutions that might have an influence on financial performance. This paper begins by discussing the limits of classical optimization techniques in finance, followed by a thorough examination of recent computer alternatives. The study then introduces a unique framework that combines machine learning models with metaheuristic algorithms, such as genetic algorithms and particle swarm optimization, to solve the issues of large-scale optimization in financial settings. The framework's performance is assessed using case studies in portfolio optimization and algorithmic trading, which show considerable increases in computing efficiency and solution quality. Furthermore, the study employs parallel computing techniques to speed up the optimization process, making it possible to tackle real-time financial problems in a dynamic market context. The findings indicate that the suggested computational techniques not only improve the robustness of financial decision-making, but also provide a scalable answer to the growing needs of financial markets. The study continues with talks on prospective applications and future prospects for computational optimization in finance, highlighting the significance of new technologies in defining the future of financial analysis and decision-making.