Why ML/AI Can’t Yet Beat HNIs or Super Investors in the Stock Market

Hello Everyone,

Today I am going to talk about a topic that sits right between our worlds of ML/AI and investing.

We are living in an era where artificial intelligence and machine learning are reshaping industries, driving innovation, and transforming how we think about decision-making.

But when it comes to the stock market, there is a big question: Why haven’t AI and ML been able to consistently beat High Net-Worth Individuals or super investors?

The answer lies in understanding both the power and the limitations of AI, as well as the unique strengths that human investors bring to the table.

Let’s Talk About Intangibles

AI excels at crunching numbers and identifying patterns. But the stock market is influenced by factors that aren’t purely logical.

Humans — super investors — are great at understanding things that machines struggle with:

• Take intuition, for example. HNIs and super investors develop this skill over decades of experience. They can sense when a business has something special, even if the data doesn’t fully show it.
• Then there’s market sentiment. The market is often driven by fear, greed, and emotion. These aren’t easy to quantify.

Qualitative aspects like management quality, competitive dynamics, and even corporate culture also play a big role.

These are things super investors factor into their decisions, but AI finds challenging to analyze.

Access to Unique Information

Super investors often have access to insights that AI just can’t reach. They’re connected.

They might speak with company executives, visit factories, or get on-the-ground insights from industry experts.

This kind of information doesn’t show up in the data sets that AI relies on.

They also dive deep into research — things like annual reports, customer feedback, or even competitor actions.

While AI can process a lot of this, it can’t replicate the human ability to connect the dots in unique ways.

AI Models Have Their Limits

So, AI is only as good as the data it learns from. It uses historical data to find patterns and make predictions.

But the stock market is forward-looking. What worked in the past might not work in the future.

Unexpected events — like a pandemic, a geopolitical conflict, or a regulatory change — can throw AI models completely off course.

Sometimes, AI even gets too good at finding patterns — so good that it starts identifying things that aren’t meaningful.

This is called overfitting, and it can lead to poor decisions.

And let’s face it: markets move fast. Super investors adapt quickly to new conditions, while AI often lags because it needs time to learn and adjust.

Market Itself is a Challenge

We often hear about the efficient market hypothesis, which suggests that all known information is already priced into stocks.

While the market isn’t perfectly efficient, it’s competitive. AI models often use similar strategies, which means their edge diminishes over time.

Super investors, on the other hand, find unique opportunities — using a combination of art and science — that AI struggles to replicate.

Patience and Perspective

Let’s not underestimate the value of patience. Super investors often take a long-term view.

They focus on a company’s intrinsic value and ignore the noise of short-term market fluctuations. AI, by contrast, is often focused on optimizing short-term results.

Super investors also balance quantitative data with a broader understanding of macroeconomics, geopolitics, and business trends. This holistic approach is hard for AI to replicate.

Regulatory and Ethical Challenges

AI relies on the data it’s given, but not all data is perfect. There are regulations, biases, and gaps in datasets that can limit how well models perform.

And, sometimes, AI can even amplify risks — like causing flash crashes — because it focuses solely on numbers, not the bigger picture.

What Does This Mean for Us?

AI and ML are powerful tools. They help process massive amounts of data, make sense of complexity, and identify opportunities.

But the stock market isn’t just a numbers game. It’s a complex, dynamic system influenced by human behavior, relationships, and the ability to think long-term.

HNIs and super investors succeed because they combine knowledge, intuition, and experience with tools like AI — not by relying on AI alone.

As technologists, this reminds us to focus on building systems that support human decision-making rather than trying to replace it. And as investors, it shows us the value of understanding the art of investing, not just the science.

If you loved this story, please feel free to check my other articles on this topic here: https://ankit-rathi.github.io/tradevesting/

Ankit Rathi is a data techie and weekend quantvestor. His interest lies primarily in building end-to-end data applications/products and making money in stock market using Tradevesting methodology.