High Templar Tech Limited (HTT) Fair Value & Analysis
Financial Services · US · Market cap $481M
Fair value as of: Jun 25, 2026
Analysis
High Templar Tech Limited (HTT) currently trades at $3.09, while our model-based Fair Value estimate is $6.18 — implying the stock looks roughly 100.0% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services 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
High Templar Tech Limited operates as a consumer-oriented financial technology service company in the People's Republic of China. The company provides aircraft leasing services; technology development and services; and research and development services. It also offers social security insurance including pension insurance, unemployment insurance, work-related injury insurance, and medical insurance for its employees. In addition, the company provides financial technology business management system comprising a core service system group, a user identification and risk management system group, a payment and finance group, system platform support, a BI system group, and an intelligent monitoring system to financial institutions. Further, it offers full-process technical services and full-scenario precision marketing services. The company was formerly known as Qudian Inc. and changed its name to High Templar Tech Limited in December 2025. High Templar Tech Limited was founded in 2014 and…
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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.