Canadian Pacific Kansas City Limited Common Shares vs Innodata Inc — how do they compare? Canadian Pacific Kansas City Limited Common Shares trades at $84.17 (market cap $73.98B), while Innodata Inc trades at $62.17 (market cap $2.10B). The key difference: Canadian Pacific Kansas City Limited Common Shares is far larger — about 35.2× Innodata Inc's market cap, and Canadian Pacific Kansas City Limited Common Shares pays a 0.9% dividend while Innodata Inc pays none. Which is the better fit depends on your goals.
| CP | INOD | |
|---|---|---|
Market Cap | $73.98B | $2.10B |
Volume | 1,669,828 | 989,493 |
Sector | Industrials | Technology |
52-Week High | $96.69 | $121.50 |
52-Week Low | $68.88 | $34.45 |
Enterprise Value | $91.36B | $1.86B |
Dividend Yield | 0.9% | — |
Typical Hold Time | — | 19 Days |
Signals from Pluang's Aura AI — not financial advice
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INOD trades at $63.42, down 4.26% today but maintains strong fundamental momentum with three consecutive quarterly earnings beats. The company shows robust profitability with 42.7% gross margins and 37.7% ROE, while technical indicators suggest a bullish trend despite recent pullback. Recent developments include expansion into motion-capture AI labs and strategic board appointments, positioning the company for continued AI infrastructure growth.
The outlook remains positive with analyst consensus favoring buy ratings (66.7%) and projected revenue growth to $317M in 2026. Key risks include premium valuation metrics (P/E 47.4) and customer concentration concerns, but strong cash flow generation and expanding AI service offerings provide growth catalysts for investors seeking exposure to the data engineering sector.
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Canadian Pacific Kansas City operates a freight railway connecting Canada, the United States, and Mexico. The company was formed through the combination of Canadian Pacific and Kansas City Southern.
Read more on CP →Innodata is a global data engineering company that provides solutions for training AI models. It helps enterprises solve complex data challenges through high-quality data annotation and digital transformation.
Read more on INOD →