What ROE Doesn't Tell You
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…
This article explores how financial ratios, such as Return on Equity (ROE), current ratio, and debt-to-equity, provide only a partial view of a company's financial health when analyzed in isolation. The author used a Kaggle dataset containing these pre-calculated ratios for the top 200 US companies to learn and perform financial analysis with Python.
Initially, the author sorted the companies by ROE and found McKesson Corporation leading the pack. However, this was misleading due to McKesson's aggressive stock buybacks, which artificially inflated the ROE by shrinking shareholder equity. The author then analyzed liquidity (current ratio) and leverage (debt-to-equity), discovering that a company's performance could vary significantly depending on how these metrics are interpreted.
By plotting ROE against debt-to-equity ratios, the author illustrated that high ROE companies often had high leverage, contradicting the assumption that high ROE always indicates a superior business. This project highlighted the importance of considering multiple financial ratios and understanding the underlying business context before drawing conclusions.
Ultimately, the takeaway is that ratios are simplifications of complex financial situations and should be used judiciously, with domain knowledge guiding their interpretation.
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