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Guangdong TianYiMa Information Industry Co (301178) Fair Value & Analysis

Technology · CN · Market cap 4.1B CNY

Price¥44.89
Fair Value¥5.41
Upside-87.9%
Quality94/100
Evidence: Medium Range ¥5.40 – ¥5.41

Analysis

Guangdong TianYiMa Information Industry Co (301178) currently trades at ¥44.89, while our model-based Fair Value estimate is ¥5.41 — implying the stock looks roughly 87.9% overvalued today. We read business quality at 94/100 (high quality), in the Technology sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).

About the company

Guangdong TianYiMa Information Industry Co.,Ltd. provides software and information technology services in China. It offers digital rural, smart community, smart party building, municipal social governance, grassroots social governance, social work platform, comprehensive management for social security, unattended for rail transit, rail transit asset inspection, one-stop for the whole chain of data elements, and integrated for all scenarios of government solutions. The company is also involved in classic cases, such as urban social governance, digital village, comprehensive management of social security, smart community grid, smart party building, administrative convenience services, grassroots social governance grid, rail transit asset inspection platform, and rail transit unattended platformGuangdong TianYiMa Information Industry Co.,Ltd. was founded in 1998 and is headquartered in Shantou, China.

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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.