Sealand Securities Co (000750) Fair Value & Analysis
Financial Services · CN · Market cap 23.8B CNY
Analysis
Sealand Securities Co (000750) currently trades at ¥3.79, while our model-based Fair Value estimate is ¥2.04 — implying the stock looks roughly 46.2% overvalued today. We read business quality at 93/100 (high quality), in the Financial Services 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: high).
About the company
Sealand Securities Co., Ltd., together with its subsidiaries, provides financial services in China. It operates through five segments: Wealth Management Business; Corporate Financial Services; Sales Trading and Investment Business; Investment Management Business; and Other Businesses. The company offers retail wealth management services, such as investment advisory, financial planning, asset allocation services, fund management, and investment trading risk control systems, as well as securities and futures brokerage business; corporate financial services, including bond financing, equity financing, debt financing, and mergers and acquisitions; asset management; and policy bank bond underwriting, financial markets, and proprietary investment. It also provides research services; online financial services; and credit services comprising margin trading and securities lending, stock pledge, and agreed-upon repurchase agreement. The company was formerly known as Guangxi Securities Firms a…
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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.