AI for Portfolio Management — Pros and Cons
Ever since the emergence of artificial intelligence (AI) as a public resource, many industries and operations have greatly benefited from AI's powerful automation and analytical abilities, including healthcare, design and social media; to name a few.
When it comes to finance, one area that has greatly benefited is portfolio management, as financial professionals can now automate a lot of manual tasks in order to free up their time. But AI's role in financial management goes beyond just automation, and has evolved into a powerful tool for analysing vast amounts of data, and predicting market trends. This can be highlighted by Lumenai's AI-powered investment portfolio, GO Long, which outperformed a traditional 60/40 portfolio by an annualised +5.3%!
But as is the case with most new technologies, there are also some potential challenges and drawbacks that people should be aware of, and this is especially important when it comes to the world of portfolio management; as there’s less room for failure/error compared to other industries and operations.
So in order to help financial advisors see whether AI is ideal for managing a portfolio, this article will examine both its advantages and potential pitfalls. By providing a balanced perspective, investors and managers may see the benefit of incorporating AI into their operations.
Let’s get right into it!
Exploring the Benefits of AI in Portfolio Management
As financial markets continue to evolve, the use of artificial intelligence (AI) in portfolio management is becoming increasingly sought after for its powerful utilities.
Below are some of the most common benefits that are being seen with integrating AI into portfolio management:
Enhanced data processing and analysis
As already mentioned, integrating AI into portfolio management can significantly enhance the efficiency and accuracy of data analysis.
AI can rapidly process and analyse large volumes of information, and with great precision too. In theory, this utility can be used to identify investment opportunities and trends that human analysts can easily miss. Furthermore, AI can analyse historical data to make data-driven predictions, which at times can be shockingly accurate. This can be highlighted by a 2022 study titled "Do AI-powered mutual funds perform better", where the authors found that AI-powered mutual funds had superior stock selection capabilities compared to humans.
It is important to note, however, that although AI can at times correctly predict future price movements, this is by no means 100% guaranteed. Therefore, AI should not be seen as an infallible prediction tool, but one that can provide very insightful analyses for portfolio manager's to cross-reference with their own analyses and predictions.
As can be seen, AI (when integrated effectively) can provide a robust foundation for making informed investment decisions that can potentially lead to higher returns.
Risk assessment and management
Another use case for AI implementation is risk management, as it has the capabilities to identify and mitigate market, credit, and operational risks; to name a few.
By analysing patterns in data that are too minuscule or advanced for the human eye, AI algorithms can 'at times' predict market trends and potential risk scenarios. This not only enhances risk assessment processes, but also helps portfolio management to be more proactive; ensuring that any threats are countered before they can cause any financial damage.
Real-time market response and adjustment
AI can also benefit portfolio management by providing real-time responses to any changes in a market, which is incredibly useful for maintaining a competitive edge during times of volatility.
AI's ability to provide recommendations (based on new information) for how to best deal with fluctuations allows portfolio managers to quickly adjust their strategies. What's more, AI can automatically implement these changes on the manager's behalf if need be. This in turn allows for more resilient investment strategies, ensuring that portfolios can capitalise on any opportunities if they arise, or be protected against unforeseen market downturns.
The Drawbacks of AI Portfolio Management
Despite the many advantages of AI in portfolio management, certain limitations are increasingly becoming apparent; particularly when handling new financial instruments, and relying purely on data-driven decisions.
Below are some potential drawbacks of AI that financial professionals should be aware of:
Limitations in handling complex financial instruments
While great at processing data, AI appears to have struggled with the intangible and unpredictable nature of new financial assets such as cryptocurrency.
Alternative asset classes like crypto are influenced by factors that go beyond quantitative data, including regulatory considerations, quality of technology, the background and connection of founders, or how strong a community is; to name a few. All of this can be difficult for AI to interpret accurately.
Additionally, there have also been reports of AI tools like ChatGPT having provided crypto traders poor feedback and results. This can be seen in BitGet's survey on the use of AI in crypto investing, where 80% of respondents had negative experiences when using ChatGPT.
Lack of intuitive decision-making and over-reliance on quantitative data
Perhaps the most glaring drawback is the divide between human insight and AI, as portfolio management often requires intuitive decision-making. This is because AI's approach overwhelmingly focuses on quantitative analysis, which results in neglecting the subtle nuances that currently requires human experience and intuition.
According to Maven (a leading private equity and property manager), human intuition and experience is required in order to evaluate important factors such as a company's culture, interpersonal dynamics, attitudes to ethical issues and labour practices. These are examples of intangible factors that often impact a company’s performance, and something that machine learning cannot do accurately.
What this shows is the importance of integrating human oversight in the portfolio management process.
The Future of AI for Portfolio Management
As has been shown, while AI brings impressive computational capabilities to portfolio management, it is clear that human oversight is still needed; especially in areas requiring intuition, emotional intelligence, and ethical considerations.
In other words, AI cannot currently gauge the emotional and psychological climate of financial markets, nor the important intangibles that are commonly seen within successful companies. But by blending this human insight with AI's computational abilities, the evidence shows that portfolio management can be greatly elevated through this combination.
So what does the future hold?
Whilst there is no way to know for sure, a 2024 study by Sutiene et al titled "Enhancing portfolio management using artificial intelligence" emphasised that there is strong regulatory and industry preference for human oversight. Sutiene and his peers believe that future regulations may advocate for explainable AI, meaning that portfolio managers will be required to have effective oversight over any AI-driven portfolio.
Either way, it is in the best interest of portfolio managers to at the very least start exploring possible AI integrations, as failure to do so could result in them being left behind.
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