KGDENIM (KGDENIM) Fair Value & Analysis
IN · Market cap ₹399M
Fair value as of: Jul 5, 2026
From 13 valuation models · updated today
Share price −8.5% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹12.72 – ₹22.69 · fair‑value band ₹1.83 – ₹3.05 · the ₹15.57 price screens above the ₹2.44 fair value. As of Jul 5, 2026.
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KGDENIM (KGDENIM) currently trades at ₹15.57, while our model-based Fair Value estimate is ₹2.44 — implying the stock looks roughly 84.3% overvalued today. We read business quality at 41/100 (below-average quality). 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).
Over the trailing twelve months, KGDENIM generated revenue of ₹420M at a net margin of 0.7%. Revenue grew 60.9% year over year. Net debt stands at ₹2.6B. The stock trades on a trailing P/E of 129.8. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹1.83 (bear case) to ₹3.05 (bull case); at ₹15.57, the current price sits above that range. The share trades about 37% below its 52-week high and 24% above its 52-week low, currently below its 200-day average.
Key figures & financial health
More key figures
Figures from reported company fundamentals (EODHD) · as of Jul 5, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2022 – FY2026 · reported fiscal years
KGDENIM reported revenue of ₹420M in FY2026 versus ₹5.6B in FY2022, a compound −47.7%/yr. Reported net income was ₹3.1M in FY2026.
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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.
Not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.