KEBODA TECHNOLOGY Co (603786) Fair Value & Analysis
Consumer Cyclical · CN · Market cap 19.7B CNY
Analysis
KEBODA TECHNOLOGY Co (603786) currently trades at ¥46.48, while our model-based Fair Value estimate is ¥38.10 — implying the stock looks roughly 18.0% overvalued today. We read business quality at 94/100 (high quality), in the Consumer Cyclical 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
KEBODA TECHNOLOGY Co., Ltd. manufacture and sale of automotive electronics and related products for automotive industry in China. The company offers automotive electronics products comprising lighting control systems, such as LED headlamp controllers, LED tail light controllers, RGB nodes, Intelligent interior light, reading lights, etc.; and domain and motor control system products, including dynamic chassis controller, air suspension controller, electromagnetic suspension controller, chassis domain control, air blower controller, etc. It also provides energy management system products, which includes eFuse, PDLC controller, 12V DCDC converter, overvoltage protection switch, USB/HUB/PD/DP, etc.; and intelligent actuator products consisting of grille actuators, electronic water valve actuators, electric vehicle charging door actuators, and other products. In addition, the company offers automotive electrical products, including cigarette lighters, power outlets, washing systems, aut…
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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.