The rise of so-called quantitative funds—mutual funds run by algorithms and computer models—has led investors to ask: Are machines better than humans at delivering consistently good investment returns?

An advantage might be that the machines are not subject to the biases that human fund managers are. Antonio Miguel and Yihao Chen, authors of the April 2021 study “Do Machines Beat Humans? Evidence from Mutual Fund Performance Persistence,” sought to answer that question by comparing the two types of mutual funds:

• Human-managed funds are run by traditional fund managers, who use experience, judgment, and research in their investing decisions.
• Machine-managed (or quantitative) funds are run by algorithms and quantitative models, often with minimal human intervention.

The researchers focused on performance persistence across quintiles. They asked, do funds that perform well in one period continue to perform well in the future? Their dataset included 270 quantitative funds covering the period 2000–19, representing nearly 6% of the funds. Risk-adjusted performance was measured against the Carhart four-factor model (beta, size, value, and momentum). The following is a summary of their key findings:

Both Humans and Machines Struggle With Consistency

While both types of funds sometimes outperform the market, it’s rare for either to persistently outperform more than is randomly expected—past winners (whether human or machine) don’t reliably stay on top.

There’s No Clear Winner

There was no strong evidence that machine-managed funds consistently outperformed human-managed funds, or vice versa, in delivering persistent outperformance. There was performance persistence in bottom–performing funds (both human and machine), while there were reversals at the top of the performance scale.

Skill Still Matters

Both human and machine managers can demonstrate skill, but it’s hard to predict which managers (or models) will continue to do well in the future.

Investors Chase Performance

Capital flows were responsive to past performance, demonstrating that fund flows are sensitive to both raw returns and four–factor alpha—investors believe performance persists.

Key Takeaways for Investors

The most important takeaway from the research findings is that quantitative funds do not provide a challenge to market efficiency. However, it is important to note that the use of quant funds does avoid the risk of a fund manager engaging in “style drift,” causing the investor to lose control of their asset allocation and, thus, the risk of their portfolio. Other investor takeaways are:

• Avoid recency bias and don’t chase past performance. Whether a fund is managed by a person or a computer, past outperformance is not a guarantee of future success.
• Fees and other costs matter. Because neither machines nor humans have a clear, persistent edge, investors should focus on low-cost (including low turnover and patient trading) funds that have the most exposure to the risks they want to take (be they an asset class or factor).
• Diversification is the prudent strategy. Avoid concentrating risks.
• Stay disciplined and adhere to a well-thought-out asset-allocation plan. Investors should resist the urge to jump from one “hot” fund to another, whether it’s run by a star manager or a fancy algorithm.

Larry Swedroe is the author or co-author of 18 books on investing, including his latest, Enrich Your Future. He is also a consultant to RIAs as an educator on investment strategies.