Artificial Intelligence Applications in Portfolio Optimization for Retail Investors in Emerging Markets

The incorporation of artificial intelligence in the management of portfolios has radically changed the approaches to investments especially to retail investors in the emerging markets. The present review explores the existing situation of AI applications in portfolio optimization, being interested in machine learning methods, deep learning, and robo-advisory systems. The study summarizes conclusions of 40 peer-reviewed articles published since 2015 and examines how AI technologies can help retail investors to solve the distinctive problems of the emerging economies, such as market instability, information asymmetry, and the lack of professional financial services. The major findings include that deep learning models (including Long Short-Term Memory networks and Convolutional Neural Networks) are found to be better at prediction accuracy than the traditional statistical techniques, and have been used in asset allocation, risk management, and automated portfolio rebalancing. This report unveils that the robo-advisory platforms have grown exponentially, and the market estimates the growth of the market by 11.83 billion dollars in 2024 to 62.64 billion dollars in 2034. Nevertheless, there are still major obstacles, such as the problem of data quality, the lack of transparency in algorithms, and the insufficient development of regulatory frameworks that do not follow recent technological progress. This study is relevant to the understanding of the way AI democratizes advanced investment policies and what aspects need attention to achieve a fair distribution and responsible application in the new market conditions.