Leedarson IoT Technology Inc (605365) Fair Value & Analysis
Industrials · CN · Market cap 8.0B CNY
Analysis
Leedarson IoT Technology Inc (605365) currently trades at ¥15.34, while our model-based Fair Value estimate is ¥6.72 — implying the stock looks roughly 56.2% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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
Leedarson IoT Technology Inc. engages in the research and development, production, and sale of Internet of things (IoT) products, LED bulbs, fixtures, luminaires, light sources, and other products. The company provides sensors, controls, cameras, and smart appliances; smart lighting products; home security products, such as alarms, smoke alarms, water leakage alarms, video surveillance, and other products; energy management solutions; smart campus solutions, including air quality sensor, lighting products, smart curtain, scene control panel, wireless bridge and measurement connector, PIR sensor, temperature and humidity sensor, and air switch. It also offers smart kitchen appliances; hub/gateway, remote control, and range extender; and ZigBee, Bluetooth, Wi-Fi, Z-Wave, and combo modules. The company was formerly known as Xiamen Leedarson Lighting Group Co., Ltd. and changed its name to Leedarson IoT Technology Inc. in January 2020. The company was founded in 2000 and is based in Xia…
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.