Tesla vs GM
Business Performance & Financial Analytics Case Study
A financial analytics case study comparing Tesla and GM across profitability, liquidity, leverage and efficiency ratios — framed as an investment thesis, not a slide deck.
Tesla · GM
Companies
15+
Ratios
Thesis
Output
Investor
Lens
Business challenge
Comparing two automakers at very different stages — a high-growth EV company and an incumbent — requires more than headline revenue. It needs a structured financial comparison that tells a defensible story.
What I built
I analyzed Tesla and GM across profitability (gross, operating, net margin, ROI, ROE), leverage (debt-to-equity, debt-to-EBITDA, interest coverage), liquidity (current, quick) and efficiency (inventory turnover), plus EPS and P/E — then synthesized the results into an investment thesis with clear business storytelling.
Why it matters
Investors and operators need to understand not just who is bigger, but who is more efficient, better capitalized and better positioned. This case study frames the comparison as an investment thesis using standard financial ratios.
How it's designed
Interface
How the intelligence flows
- 01Collect — financial statements and market data for both firms.
- 02Compute — profitability, leverage, liquidity and efficiency ratios.
- 03Compare — normalized side-by-side across years.
- 04Synthesize — investment thesis and business narrative.
How data is modeled
- Structured financial dataset: income statement, balance sheet, cash flow line items.
- Derived ratio tables computed per company per year.
- Comparison views normalizing metrics across the two firms.
Technologies
Key features
- Revenue growth and EV-market comparison.
- Margin analysis — gross, operating, net.
- Return metrics — ROI and ROE.
- Leverage — debt-to-equity, debt-to-EBITDA, interest coverage.
- Liquidity — current and quick ratios.
- Efficiency — inventory turnover; valuation — EPS and P/E.
- Investment thesis framing.
Challenges & trade-offs
Comparing unlike companies
A hyper-growth EV maker and a mature automaker aren't apples-to-apples — the analysis had to contextualize each ratio rather than rank blindly.
From numbers to narrative
The value was in synthesis — turning a wall of ratios into a defensible investment thesis.
Code snippet
def financial_ratios(fin: pd.DataFrame) -> pd.Series:
return pd.Series({
"gross_margin": fin.gross_profit / fin.revenue,
"operating_margin": fin.operating_income / fin.revenue,
"net_margin": fin.net_income / fin.revenue,
"roe": fin.net_income / fin.equity,
"debt_to_equity": fin.total_debt / fin.equity,
"current_ratio": fin.current_assets / fin.current_liabilities,
})
compare = pd.concat([financial_ratios(tesla), financial_ratios(gm)], axis=1)
compare.columns = ["TSLA", "GM"] # -> investment thesisHow it ships
- Analysis notebooks and Tableau visualizations.
Security
- — Uses public financial data only.
Scalability
- — Reusable ratio framework applicable to any two comparable firms.