FINANCIAL PORTFOLIO OPTIMIZATION USING MACHINE LEARNING AND DATA ANALYTICS

Abstract

The concept of portfolio optimization has a deep-rooted history, dating back to the 1950s with the pioneering work of Harry Markowitz and his introduction of Modern Portfolio Theory. Markowitz's theory fundamentally transformed how investors approached diversification and risk management within their investment portfolios. Over the decades, significant advancements in portfolio optimization techniques have emerged, notably through The combination of artificial intelligence (AI) and machine learning (ML) algorithms represents a noteworthy integration. These technological innovations have profoundly influenced decision-making processes within the realm of finance, continuously shaping its landscape. This research paper aims to explore the intricate concept of financial portfolio optimization, focusing on the strategic selection of investments to attain optimal returns while minimizing associated risks. The paper delves into various methodologies and models employed in portfolio optimization, including but not limited to mean-variance optimization and the capital asset pricing model. Moreover, practical considerations, implementation challenges, and the ramifications of factors such as asset diversification and risk tolerance on portfolio performance are comprehensively discussed.