YAANENT (YAANENT) Fair Value & Analysis
IN · Market cap ₹309M
Fair value as of: Jul 6, 2026
From 15 valuation models · updated today
Share price +4.9% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹75.00 – ₹128.60 · fair‑value band ₹32.15 – ₹68.71 · the ₹99.70 price screens above the ₹42.86 fair value. As of Jul 6, 2026.
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YAANENT (YAANENT) currently trades at ₹99.70, while our model-based Fair Value estimate is ₹42.86 — implying the stock looks roughly 57.0% overvalued today. We read business quality at 53/100 (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: high).
Over the trailing twelve months, YAANENT generated revenue of ₹244M at a net margin of 3.2%. It earns a return on equity of 15.8%. Net debt stands at ₹55.6M. The stock trades on a trailing P/E of 39.6. Fundamentals as of Jul 6, 2026
Our scenario range runs from ₹32.15 (bear case) to ₹68.71 (bull case); at ₹99.70, the current price sits above that range. The share trades about 26% below its 52-week high and 47% above its 52-week low, currently above its 200-day average.
Key figures & financial health
More key figures
Figures from reported company fundamentals (EODHD) · as of Jul 6, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2022 – FY2026 · reported fiscal years
YAANENT reported revenue of ₹244M in FY2026 versus ₹12.1M in FY2022, a compound +111.9%/yr. Reported net income was ₹7.8M 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.