You’ve been lied to your whole life. Stock
fundamentals do NOT matter.
I am a data-driven investor, a Carnegie Mellon
alumnus, a Cornell graduate, and an engineer.
I’m not somebody that takes prevailing investing
wisdom and assumes it’s correct. I’ve never been
one to blindly trust axioms, such as “stocks
with a low PE ratio are good" (a fact that I’ve later refuted with strong
evidence).
However, when I analyzed the correlation between
a stock’s fundamentals and their returns that
year, I outright did not believe it. I double
and tripled-checked the queries, broke them down
piece-by-piece, and even used the most powerful
language model to exist, OpenAI’s o1-preview, to
confirm the accuracy. It’s right.
And what I discovered is shocking. For the vast
majority of stocks, fundamentals do not matter.
What are stock fundamentals?
Fundamentals are
a measure of how good or bad a company is. In
finance, they can mean a lot of things, but for
the purposes of this article, fundamentals will
refer to earnings data, such as revenue, net
income, and free cash flow.
These metrics matter because they tell the story
of how fundamentally strong a business is.
Unlike other asset classes (looking at you
cryptocurrency), stocks are backed by the
financial health of the company behind them.
Traditionally, we always thought this: companies
that were strong fundamentally had strong stock
prices later that year. We thought it was a
1-to-1 correlation – that if a stock increased
their revenue or their cash flow, their stock
price would increase at the same rate.
We were wrong.
What is my evidence that they do not matter?
Disclaimer: I am not infallible
I have strong evidence that these metrics
fundamentally do not matter. But I am not
infallible.
This is the part of the article where I want you
to debate me. I’m not a SQL expert or a data
scientist; I’m a software engineer. And thus, I
might be doing an improper analysis. If I am,
call me out, and embarrass me on the internet.
With that being said, I’ve taken statistics and
graduated from good schools. I’m generally
intelligent, and I wouldn’t be making such a
strong claim if I wasn’t reasonably confident in
my analysis.
So here’s what I did.
How I performed the analysis
I’ve created a text to SQL AI model that’s able
to interpret financial data. Here’s how it
works.
First, the large language model determines what
exactly we’re talking about. There are about 10
different language model prompts, and we need to
determine which prompt is the most relevant.
Classifying the request to the most relevant
prompt
Then, if we’re talking about financial analysis,
we will forward the request to that prompt.
The
Financial Analysis Prompt
is responsible for generating a SQL query
against the database. The data primarily comes
from the bulk downloads feature of the
SimFin API.
Specifically, I transformed and uploaded all of
the data into a BigQuery database so that I can
perform the analysis. Then, I use the Financial
Analysis prompt to generate my SQL queries and
execute it against the database. This is called
function-calling.
Once the query is generated, I retrieve the
results from the database. The results are
formatted and summarized, and sent back to the
user.
The flow for the Financial Analysis Prompt
In this case, we are asking the model to measure
the correlation between stock prices and the
stock’s fundamentals. This includes metrics
like:
-
Revenue: How
much money a company generated in total
-
Net income: How
much of that money was actual profit
-
Free cash flow:
How much cash the company generated
-
Return on assets: How efficient a company is at using its
assets to generate profit
To measure these correlations, we had a
back-and-forth conversation with the language
model. We asked it the following questions:
For stocks with a market cap below $5 billion,
what fundamentals was most correlated with
stock price in 2021
We did the same for stocks with a market cap of
$5 billion to $50 billion, $50 billion to $200
billion, and with a market cap above $200
billion. We also did the same for stocks in 2022
and 2023. We then analyzed the combined results
to come up with our analysis.
Finally, we’re using the Claude 3.5 Haiku model
for this analysis. This model is a great balance
between cost and performance, and should be more
than capable of helping us generate accurate SQL
queries.
If you want to see the full conversation we had
the model,
check out the following link.
For example, here’s the query for stocks with a
market cap below $5 billion for the year of
2022.
A query generated by the language model
Take a few minutes and analyze this query. Send
it to your data science friend, or maybe drag it
into Anthropic or Open’s UI and ask it to
analyze.
As far as I can tell, it is correct.
And, it tells a compelling story. Fundamentals
do not matter.
What were the results?
A summary of the results
If we read through the conversation across the
years and across market cap, we come to one
startling conclusion.
For most stocks, especially stocks with a market
cap below $200 billion, the fundamentals do not
matter. At all.
The correlation between fundamental metrics
and price for penny stocks
For example, for penny stocks, the correlation
between the fundamental metrics and price is
nearly 0. The highest correlation is 0.0184 and
the low is 0.0010. For all intents and purposes,
this is basically chance.
Mid-cap stocks had high variability, but
generally have low to no correlations as well.
For example, in 2021, EBITDA and Return on
Assets had a correlation of 0.2 between price in
2021, but a correlation of less than 0.001 in
2023.
The correlation between fundamental metrics
and price in mid-cap stocks in 2021
By and large, fundamental metrics had virtually
no correlation between the stock’s price that
year.
With the exception of one group of stocks.
Are there any exceptions to this rule?
Once the size of these stocks start to reach a
certain threshold, we start noticing a certain
tend.
These metrics begin to matter.
For example, for stocks with a market cap above
$200 billion, the correlation between the stock
price and metrics like net income, return on
asset, and revenue is between 0.3 and 0.4 in
2022.
The correlation between fundamental metrics
and price in mega-cap stocks in 2022
In 2023, it’s even higher, 0.43 and 0.8 for
certain metrics.
The correlation between fundamental metrics
and price in mega-cap stocks in 2023
These insights are extremely informative and
tell a compelling story – fundamentals matter
more the larger the stock is. Penny stocks and
small caps seem to be driven by external
factors, such as news sentiment or speculation.
In contrast, the mega-cap stocks are driven by
how fundamentally strong the business is. These
insights are unconventional and go against
traditional investing wisdom, which is why I’m
encouraging refutations, rebukes, and arguments
in my comments.
What are the limitations to this study?
Before you start using a dart board and picking
stocks to buy at random, I do want to say this –
these results have significant limitations.
Allow me to explain.
We did this analysis on an aggregate of stocks
For these stocks, we took the aggregate
correlation by market cap. We didn’t do any
additional analyses on other characteristics.
For example, for micro cap stocks, we may have
found the biotechnology stocks had an extremely
low correlation, but payment stocks had a
moderately high correlation. Or, maybe stocks
with a revenue below $1 billion had a low
correlation and stocks with a revenue above $5
billion had a high correlation.
Another example – maybe this analysis had a low
correlation for stocks that went up but a high
correlation for stocks that went down. We
wouldn’t know, unless we did the analysis.
Thus, while aggregate data provides a broad
overview, analyzing subsets based on industry,
revenue size, or other criteria can reveal more
nuanced relationships, offering valuable
insights for investors.
Correlation does not equal causation
In addition to the aggregate view of this data,
an important fact with data analysis is that
correlation does not equal causation.
I’m about to contradict myself just a little
bit. Just because the data shows low
correlations between fundamentals and stock
price doesn’t
necessarily mean
fundamentals have zero impact.
Low correlation isn’t the same as no connection
— it’s possible that fundamentals still play a
role, but their effect gets overshadowed by
things like market sentiment or external events.
This analysis is a starting point, not the full
picture, so don’t ignore fundamentals entirely —
they may still matter in ways that are just
harder to see at first glance.
This analysis relies HEAVILY on SimFin data
This study relies heavily on SimFin data, and
while it’s reliable, every data source has its
limits. SimFin captures a broad range of
financial metrics, but it’s not perfect, and
some nuances might be missed.
This analysis is built on that foundation, so if
the data has any gaps, they’ll affect the
findings here. Keep that in mind before making
decisions based on this alone — it’s a useful
tool, but it’s best viewed as one part of a
bigger picture.
Concluding Thoughts
When I invest, I don’t rely on traditional
investing wisdom. I rely on cold-hard data.
In this case, the data told me a story that I
thought I would never hear – for the vast
majority of US stocks, the fundamentals have
virtually no correlation between the stock’s
price.
This effect fades as the stocks market caps get
higher. But it is still present amongst the vast
majority of US stocks.
As someone who religiously relied on stock
fundamentals to make my decisions, this shocked
me. I always thought that fundamentals mattered
more than anything. But the data is telling me
that I’m wrong.
Now, this study has significant limitations. The
queries the model generated may be wrong or the
data may be inaccurate. Some of the insights may
be true today but false for 2025 and beyond.
We should not rely on these insights for the
sole basis of making financial
decisions.
But at the very least, these insights are
interesting. They go against the grain and force
you to think about your assumptions before
assuming its true.
“If a stock is below a $10 billion market cap,
does its revenue increasing
really matter?” you might
ask yourself. “Maybe I should look more at the
executive team and their experience instead of
just the fundamentals”.
If I am wrong with my analyses or conclusions,
I’m not one to intentionally spread
misinformation.
I want you to challenge me.
Post a comment and explain where I went wrong.
Write on LinkedIn or Medium, and break down my
arguments step-by-step. Create a YouTube video,
and embarrass me on the national stage.
Do what you have to do.
But as far as I can tell, my analyses seems
sound. And you can replicate it for yourself.
NexusTrade is a free, AI-Powered algorithmic
trading platform that has these features
implemented. You can do the analysis yourself
with your own database and your source, or you
can use NexusTrade and get started right away.
I think this demonstrates clearly the power of
AI in the financial industry. Wouldn’t you
agree?
