Xueda (Xiamen) Education Technology Group (000526) Fair Value & Analysis
Consumer Defensive · CN · Market cap 3.2B CNY
Analysis
Xueda (Xiamen) Education Technology Group (000526) currently trades at ¥26.85, while our model-based Fair Value estimate is ¥35.22 — implying the stock looks roughly 31.2% undervalued today. We read business quality at 94/100 (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: high) — always confirm before acting.
About the company
Xueda (Xiamen) Education Technology Group Co., Ltd. provides education and training services in China. It provides secondary vocational education, higher vocational education, integration of industry and education, vocational skills training, industrial colleges, internships, learning ability improvement, academic planning, comprehensive quality evaluation, language training, and other training services. The company also offers PPTS business management system, BI business analysis and other systems, and intelligent teaching service system. In addition, it is involved in the trade, equipment leasing, and technical services, as well as real estate leases. The company was formerly known as Xiamen Unigroup Xue Co., Ltd. and changed its name to Xueda (Xiamen) Education Technology Group Co., Ltd. in April 2021. Xueda (Xiamen) Education Technology Group Co., Ltd. was founded in 1984 and is headquartered in Beijing, China.
Open the full interactive analysis →
Similar stocks
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.