GARNETINT (GARNETINT) Fair Value & Analysis
IN · Market cap ₹1.2B
Fair value as of: Jul 5, 2026
From 14 valuation models · updated today
Share price +7.6% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹44.75 – ₹130.95 · fair‑value band ₹22.80 – ₹38.30 · the ₹60.59 price screens above the ₹30.55 fair value. As of Jul 5, 2026.
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GARNETINT (GARNETINT) currently trades at ₹60.59, while our model-based Fair Value estimate is ₹30.55 — implying the stock looks roughly 49.6% overvalued today. We read business quality at 51/100 (solid 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, GARNETINT generated revenue of ₹48.7M at a net margin of 97.5%. Revenue declined 76.3% year over year. It earns a return on equity of 11.8%. Net debt stands at ₹9.2M. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹22.80 (bear case) to ₹38.30 (bull case); at ₹60.59, the current price sits above that range. The share trades about 55% below its 52-week high and 44% 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
GARNETINT reported revenue of ₹11.0M in FY2026 versus ₹417M in FY2022, a compound −59.7%/yr. Reported net income was ₹47.5M 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.