What ROE Doesn't Tell You: A Beginner's Look at 200 US Companies' Financial Health
My first Python data project, and what it taught me about reading ratios like an analyst, not a spreadsheet.
Why I Started Here
I have a formal background in finance and accounting, but I'm brand new to Python. So instead of learning Python through generic tutorials, I decided to learn it by doing the kind of analysis I actually understand: financial statement ratios. I used a Kaggle dataset covering pre-calculated ratios ROE, current ratio, debt-to-equity, and more for the top 200 US companies, and worked through it in Google Colab.
Step One: Just Look at the Data
Before doing anything clever, I loaded the CSV with pandas and checked the basics: what columns existed, how many rows, what data types, and quick summary statistics (mean, min, max) across all the ratios. This is a habit worth keeping regardless of the tool you don't analyze what you haven't looked at first.
Ranking by ROE and a Reality Check
Return on Equity (ROE) is one of the most commonly cited profitability metrics: it tells you how efficiently a company turns shareholder equity into profit. So naturally, my first move was to sort all 200 companies by ROE and see who came out on top.
The answer: McKesson Corporation, by a wide margin.
If you don't know McKesson's story, this looks like a slam-dunk a company squeezing enormous returns out of shareholder capital. But here's where the accounting background matters more than the code: McKesson has spent years aggressively buying back its own stock, which shrinks shareholder equity sometimes down to very small or even negative levels. Since ROE is calculated as Net Income divided by Equity, a shrunken denominator can inflate the ratio dramatically, even when the underlying business performance hasn't changed all that much.
In other words: a high ROE isn't automatically a sign of a great business it can just as easily be a sign of financial engineering.
Widening the Lens: Liquidity and Leverage
To avoid over-indexing on one ratio, I pulled two more views:
- Current Ratio (short-term liquidity can a company cover its near-term obligations?)
- Debt-to-Equity (leverage how much debt is the company carrying relative to equity?)
Sorting by Current Ratio surfaced a completely different leaderboard from ROE NVIDIA, Activision Blizzard, Texas Instruments, Intuitive Surgical, Arista Networks, TransDigm Group, DexCom, Monster Beverage, Vertex Pharmaceuticals, and Zoetis topped the list, all with current ratios well above 3 (NVIDIA highest at over 7). None of these were anywhere near the ROE top 10. That alone is worth sitting with: the "best" company depends entirely on which question you're asking. A company can be a poor ROE performer and still be extremely safe on liquidity, or vice versa profitability and short-term financial safety are simply answering different questions.
Looking at these alongside ROE gives a much more honest picture than any single number in isolation. A company with a sky-high ROE but also very high debt-to-equity is telling a very different story than one with high ROE and conservative leverage even if the ROE figure looks identical on paper.
Testing the McKesson Theory Across All 200 Companies
The McKesson observation raised a question: is this a one-off, or a pattern? To check, I plotted every company's ROE against its Debt-to-Equity ratio in a single scatter chart, rather than just looking at the top 10.
The result: most of the 200 companies cluster tightly in the bottom-left corner of the chart low debt-to-equity, low-to-moderate ROE, no visible relationship between the two. But a handful of companies break sharply away from that cluster, sitting far to the right with debt-to-equity ratios in the thousands. The most extreme point on the entire chart debt-to-equity near 4,300 and ROE above 12 is almost certainly McKesson.
So it's not a broad, systemic trend across all 200 companies most businesses don't show any strong link between leverage and ROE. But the few genuine outliers on the chart are exactly the companies worth double-checking before taking their ROE at face value, since extreme ROE and extreme leverage do show up together for them. McKesson isn't unique in kind, just the most extreme example of a small group.
Either way, this is the more rigorous version of the same instinct: don't trust one ratio, and don't trust one company's story as proof of a pattern until you've checked it across the full dataset.
The Takeaway
The biggest lesson from this project wasn't a Python lesson it was a reminder that ratios are shorthand, not truth. ROE, on its own, answers "how much profit relative to equity?" but says nothing about why the equity is the size it is. The moment you pull in debt levels, buyback history, or industry context, the story can change completely.
For anyone with a finance background moving into data work: this is exactly the kind of judgment that a model or a script can't replicate on its own knowing which number to be suspicious of, and why, is domain knowledge, not code.
What's Next
This was project one of an ongoing series where I'm using Python to explore different corners of finance next up: either rounding out this dataset further, or moving into a completely different area like credit risk, budgeting trends, or market data.
Built with Python (pandas, matplotlib) in Google Colab. Dataset: "Financial Statement Data for Top 200 US Companies" via Kaggle.
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