Artificial Intelligence in Financial Portfolio Management

Introduction

The term ‘portfolio’ in finance can be defined as an individual’s or a company’s whole financial assets (Paulson, 1991). There are a wide variety of investments that may be made in the stock market and other financial markets. The term ‘portfolio’ refers to all of the investments, which may or may not be held in one account. We can think of portfolio management as a method of putting money to work in accordance with the objectives, timetable, and level of risk tolerance. Portfolio management is the process of selecting and monitoring assets including stocks, bonds, and mutual funds (Graves & Sunstein, 1992; Long, 1990). Portfolio risk and projected return are measured and controlled via the process of portfolio optimization. Basically, portfolio optimization is a kind of diversification that reduces the risk of a portfolio by mixing assets with different risk profiles. However, optimization also takes into account the correlations between assets—the degree to which their values tend to move in tandem. Combining stocks from various groups with complementary price movements allows an optimizer to design a portfolio that gives the best return for each level of risk (Brinson et al., 1986; Kalayci et al., 2019).

In the context of artificial intelligence (AI), the phrase covers any technology that allows computers to do tasks formerly only performed by humans (Bullock et al., 2020; Zhao et al., 2020). As an example, Apple’s Siri uses NLP algorithms to comprehend English, whereas Amazon’s Alexa uses machine learning algorithms to beat a world champion in ‘Go’. AI is a popular issue right now since it has disrupted so many businesses in the last few years, including financial services. There has been a shift in some of the industry’s most fundamental procedures as a result of fintech, which places a focus on artificial intelligence. Asset allocation, trading, risk management, and other portfolio management functions might be substantially changed by artificial intelligence. In reality, many robo-advisors currently employ these technologies to provide clients with portfolios that perform better out-of-sample while also automatically rebalancing and managing risks with little transaction costs. Asset management firms are increasingly running trading and investing platforms with the use of artificial intelligence and statistical models. AI’s usage in asset management is requiring a more systematic analysis of the many approaches and applications involved, as well as the potential and difficulties they offer to the industry ().

This research gives an in-depth look at a broad variety of AI-based asset management applications, emphasizing the most pressing issues. Asset allocation choices are made to design a portfolio with certain risk and return characteristics as part of portfolio management. In this way, artificial intelligence may assist in the examination of fundamentals and generate new investing methods. Additionally, AI may overcome the flaws of conventional portfolio development methods. Artificial Intelligence has the ability to provide superior asset return and risk estimations and solve portfolio optimization issues with complicated constraints, resulting in portfolios with improved out-of-sample performance ().

AI applications are also widely used in the field of trading (). Artificial intelligence approaches are becoming more important in trading because of the increasing speed and complexity of deals. AI’s capacity to analyze massive volumes of data and produce trading signals is one of its most appealing features. A new sector called algorithmic (or algo) trading has emerged as a result of these signals, which can be taught to automatically execute transactions. Furthermore, by automatically assessing the market and then determining the ideal timing, size, and location for trading, AI approaches may push down transaction costs even further.