New Oriental Education & Technology Group (EDU) Fair Value & Analysis
Consumer Defensive · US · Market cap $7.5B
Analysis
New Oriental Education & Technology Group (EDU) currently trades at $45.04, while our model-based Fair Value estimate is $51.49 — implying the stock looks roughly 14.3% undervalued today. We read business quality at 90/100 (high quality), in the Consumer Defensive sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
About the company
New Oriental Education & Technology Group Inc. engages in the provision of private educational services under the New Oriental brand in the People's Republic of China. The company operates through four segments: Educational Services and Test Preparation Courses; Private Label Products and Livestreaming E-Commerce; Overseas Study Consulting Services; and Educational Materials and Distribution. The company offers test preparation courses to students taking language and entrance exams used by educational institutions in the United States, the Commonwealth countries, and the People's Republic of China. It also provides non-academic tutoring courses; intelligent learning systems and devices to offer a digital learning experience for students; and overseas studies consulting services. In addition, the company offers online education services through the Koolearn.com platform. Further, it develops and edits educational materials for language training and test preparation. In addition, the …
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.