Single Blog Title

This is a single blog caption
Do AI-Powered Funds Perform Better

Do AI-Powered Funds Perform Better?

The next ten years will see tremendous advancement as more businesses adopt artificial intelligence (AI). With the aggregate potential cost savings for financial institutions from AI applications estimated at $447 billion by 2023, companies in financial services are finding new ways to incorporate the tech into their services.

AI in the field of finance, will be accelerated by technological advancement, increased user acceptance, shifting regulatory frameworks, and the benefits it provides to businesses in financial services such as task automation, fraud detection, and delivering personalised saving and investment recommendations for tech savvy consumers. From an investment perspective, AI-Powered funds have caught the attention of many due to their perceived benefits but have these performed better than their human-managed peer funds?

In this article, we’ll take a close look at the history and performance of artificial intelligence (AI)-powered mutual funds.

Introduction of AI-Powered Funds

Machine learning (ML) is the study and development of, “learning” methods—methods that use data to enhance performance without human intervention. It is considered to be a component of artificial intelligence (AI).

The first artificial intelligence (AI)-powered public fund, AIEQ, was introduced on 18 October 2017. The fund adopts machine learning technologies to actively select stocks in portfolio choices. The AIEQ became one of the most popular funds in 2017 and raised more than $70 million within a few weeks of time. As of August 9, 2022, its assets under management were about $120 million. 

AI-Powered Funds Benefits

Whilst algorithmic trading has been widely used for a long time to optimise and automate order submissions and executions, AI-Powered funds make decisions in the earlier stages of portfolio selection and use proprietary techniques to perform real-time prediction and greatly enhance the flexibility and timeliness of traditional quantitative funds*. Given the efficiencies they provide, it is no surprise that the investment management industry has explored using AI to manage portfolios. The potential advantages of AI include:

  • Superior computational ability to analyse mass data in a short period of time.
  • Avoidance of cognitive biases to which humans are susceptible — basically AI is more rational.

There is also the possibility that AI could “rescue” the active management sector, which has experienced a steady decline in its capacity to produce alphas. In fact, since the turn of the millennium, only about 2% of active managers have been able to show statistically significant results^.

The Performance of AI-Powered Funds

In the study, Do AI-Powered Mutual Funds Perform Better?, authors Rui Chen and Jinjuan Ren, assessed the performance of AI-powered mutual funds which were published in the Finance Research Letters edition from August 2022. Their data sample, which covered the 26-month period from November 2017 to December 2019, came from the CRSP Survivor-Bias-Free U.S. Mutual Fund Database. They identified three types of funds: AI-powered funds (15), quantitative funds (300), and discretionary funds (611). The AI-powered funds used machine learning technologies to actively choose stocks for their portfolios. The quantitative funds use fixed rules and numerical methods to generate computer-driven models and make investment decisions. Here is a summary of what they discovered:

  • In 25 of the 26 months in the sample period, the performance of AI-powered funds was statistically comparable to the performance of the whole market.
  • AI-powered mutual funds produced negligible risk-adjusted returns, had only slightly better stock picking abilities (only by equal weight), and had no aptitude for market timing.
  • Due to lower turnover—31 percent versus 72 percent—which resulted in cheaper transaction costs and merely better asset picking abilities, AI-powered mutual funds did outperform their human-managed peers.
  • The stock holdings of AI funds were lower (149 versus 197) and more concentrated.
  • Some common behavioural biases (such as the disposition effect) were avoided by AI-powered funds.


Although the sample size was small, the study showcased that whilst AI-powered funds did outperform actively managed funds run by humans (primarily due to lower trading costs, superior stock-picking capability and their ability to avoid cognitive biases), there was no statistically significant evidence that they were able to outperform the collective wisdom of the market on a risk-adjusted basis.




Contact Us