Artificial Intelligence Approach to Portfolio Management: Enhancing Decision-Making, Efficiency, and Alpha Generation

2. Review Of Literature

(Beccalli et al., 2020) seeks to investigate ethical concerns related to the utilization of Artificial Intelligence (AI) in the financial management of portfolios, and their managerial consequences. Traditional quantitative investing in the past involved making portfolio allocation decisions based on a well-outlined investment strategy. Financial portfolio managers create and implement investment strategies to optimize expected returns for clients' portfolios. AI-enhanced algorithms now allow intelligent computers to autonomously adjust and improve investing strategies by analyzing historical data. Artificial intelligence can have a substantial impact on the outcomes of portfolio management methods, leading to ethical problems around human versus machine duty, accountability, and risk. Managers need to implement new methods for monitoring performance, evaluating competence, and allocating incentives while overseeing AI software developers in this field.

The ultimate goal of artificial intelligence (AI) is to augment or even completely replace human intelligence in almost every area where it is now used. Many different fields are making use of AI as a result of both internal and external factors, such as technical progress. Among these, the use of AI in the financial sector has bright prospects. This article outlines the fundamental methods and specific situations used in artificial intelligence (AI) applications across several domains, with a focus on the financial sector. Using spectral clustering (SC) linked to a stock complex network as an example, this article also builds a portfolio using AI in the field of portfolio management. In this scenario, the AI-constructed portfolio beats the more conventional ones (Zhang & Chen, 2017).