In the year AD 2025, the first question we’re often asked as a quantitative momentum fund is:
Is Alpha-Elite an AI fund?
Before we answer that, it’s worth stepping back to ask a broader question:
Is artificial intelligence an existential threat to us—and to the hedge fund industry as a whole?
Revolution or Hype?
First, we need to distinguish how AI is actually used in finance.
Financial advisors, as one example, use it to sift through huge amounts of data and tailor solutions for clients. Some hedge funds, on the other hand, leverage AI to analyse company fundamentals, earnings calls, or masses of other financial information.
In all these cases, AI excels as a data processing assistant—not as the core of an investment strategy.
Unfortunately, the AI hype has also led to the phenomenon of “AI Washing” – where some unscrupulous participants in the industry simply pretend to be using AI, when in reality they are simply using basic quantitative techniques.
Then there are hedge funds built entirely around AI-driven strategies.
These use machine learning (ML) for algorithmic trading, pattern recognition, and predictive analytics. Techniques like random forests, ensemble learning, and neural networks attempt to uncover nonlinear relationships between data and price movements. Some even deploy reinforcement learning to evolve their strategies over time.
Sounds unbeatable, right? Not so fast.
The Reality: Mixed Results and Missed Expectations
Academic research and industry data paint a more sobering picture.
Yes, AI can help with risk management, trade execution, operational efficiency and processing vast information flows. Its speed may also give short-term traders an edge.¹ ² ³ ⁴
But when it comes to actual large-scale fund management returns, AI-driven funds have struggled to beat traditional benchmarks.⁵ ⁶ ⁷
A 2021 review in the International Journal of Data Science and Analytics ⁸ analysed 27 peer-reviewed studies on AI in equity investing. It found that the Eurekahedge AI Hedge Fund Index ⁹ significantly underperformed the S&P 500 and MSCI World Indexes from 2011 to 2020.
AI versus the basic stock market…
…and what happens when Alpha-Elite Quantitative Momentum is added.
Their conclusion?
“There is no conclusive evidence of any ML-driven investment funds delivering spectacular returns at scale. All market data indicates substantial underperformance compared to benchmark indices.”
More revealing, Boczynski, Cuzzolin, and Sahakian wrote:
“The picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures).”
Our own observations mirror these findings.
AI is an invaluable tool in the investment process—especially for fundamental research, portfolio design, and operational support. But as a standalone investment strategy? The evidence is thin.
Our Experience with AI
So, have we tried AI ourselves in our momentum stock selection process?
Yes we have.
We experimented extensively with AI/ML to explore novel stock selection metrics. Using tools like XGBoost and SMOTE in Python / Jupyter notebooks on Google Colab, we analysed price and volume data from the Nasdaq 100 and S&P 100 to identify potential improvements.
The result? AI didn’t deliver. Despite rigorous testing, we found that AI could not generate any significantly new momentum signals. At best, it produced slight tweaks on already well-known metrics.
Why? Because AI lacks what still sets humans apart: creativity.
Intelligence does not equate to creativity.
And sometimes, creativity = weirdness.
Fortunately, we have weirdness in abundance.
Why Alpha-Elite Stays Human-Powered
We don’t see AI as a threat to our strategy, nor do we believe it offers a consistent edge in large-scale fund management.
Here’s why: AI models rely on historical data. If multiple funds use AI for momentum strategies, they’ll likely pick the same stocks, leading to overcrowded trades and reduced returns. The market becomes a hall of mirrors, reflecting the same signals.
Our quantitative momentum approach is grounded in rigorous research and the scientific method. Yet, human creativity remains our core. It’s what sets us apart in a world of algorithms.
The Twist: AI Could Fuel Our Wins
Here’s the fun part. Even if AI-driven funds proliferate, Alpha-Elite can still come out on top. If countless investors use AI to select stocks, their buying will create price movements. Our Allocation Engine, designed to detect momentum, will spot these trends and ride the wave. AI’s momentum behaviour becomes our opportunity.
The Human Element Still Wins
AI is a phenomenal tool, but it’s not a silver bullet for hedge fund success.
Our quantitative momentum strategy is rooted in the scientific method: observation, hypothesis, testing, iteration.
But at the core of Alpha-Elite is human ingenuity—the bold questions, the creative leaps, and yes, the occasional weird idea that works.
That’s not something you can automate.
References:
1. Lopez de Prado, M. (2019) ‘Can Machines “Learn” Finance?’, The Journal of Financial Data Science, 1(1), pp. 10–21.
2. Zhang, Z., Zohren, S. and Roberts, S. (2020) ‘Deep Learning for Portfolio Optimization’, The Journal of Financial Data Science, 2(4), pp. 8–20.
3. Gu, S., Kelly, B. and Xiu, D. (2021) ‘Artificial Intelligence and Systematic Trading’, The Journal of Financial Economics, 141(2), pp. 641–666.
4. Agarwal, V. and Ren, H. (2023) ‘Hedge Funds: Performance, Risk Management, and Impact on Asset Markets’, Oxford Research Encyclopedia of Economics and Finance. Oxford University Press.
5. Bachelier, L. and Sornette, D. (2020) ‘Do Hedge Funds Use AI Effectively?’, The Journal of Portfolio Management, 46(4), pp. 56–70.
6. Harvey, C.R. and Liu, Y. (2021) ‘The Limits of Machine Learning in Hedge Fund Performance’, The Journal of Financial Data Science, 3(2), pp. 30–46.
7. Fabozzi, F.J. and López de Prado, M. (2022) ‘Artificial Intelligence in Asset Management: Hype or Reality?’, The Journal of Financial Data Science, 4(1), pp. 10–29.
8. Buczynski, W., Cuzzolin, F. and Sahakian, B. (2021) ‘A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life’, International Journal of Data Science and Analytics, 11(3), pp. 221–242.
9. https://platform.withintelligence.com/performance/indices/11793