Valuation Discrepancy of Coffee Chains in the US and China: A Capital Market Perspective
Bibliographic record
Abstract
This paper investigates the valuation discrepancy between major coffee chains in the United States and China, focusing on Starbucks and Luckin Coffee. Although both firms demonstrate strong revenue growth, comparable business models, and digital innovation, their valuation multiples differ markedly in global capital markets. Starbucks commands significantly higher P/E and EV/EBITDA multiples compared to Luckin. This study applies a trading comparables framework to estimate Luckin’s implied value and explore the gap relative to market valuation. Furthermore, the research integrates structural variables including investor composition, regulatory transparency, listing venue, and ESG performance to explain the valuation divergence. Results suggest that such institutional and market factors—not just firm-specific fundamentals—play a pivotal role in determining cross-border valuation outcomes. In doing so, the study highlights the limitations of traditional valuation models when applied across regulatory environments and offers recommendations for refining comparative analysis in global investment contexts. The findings contribute practical insights for investment banking professionals involved in IPO pricing, equity research, and international M&A advisory, particularly in the context of emerging-market issuers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".