KIRANPR (KIRANPR) Fair Value & Analysis
IN · Market cap ₹150M
Fair value as of: Jul 5, 2026
From 15 valuation models · updated today
Share price +5.0% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹21.00 – ₹35.06 · fair‑value band ₹2.97 – ₹4.86 · the ₹30.08 price screens above the ₹3.80 fair value. As of Jul 5, 2026.
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KIRANPR (KIRANPR) currently trades at ₹30.08, while our model-based Fair Value estimate is ₹3.80 — implying the stock looks roughly 87.4% overvalued today. We read business quality at 62/100 (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, KIRANPR generated revenue of ₹8.7M at a net margin of 14.2%. Revenue grew 37.0% year over year. It earns a return on equity of 4.2%. The stock trades on a trailing P/E of 120.3. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹2.97 (bear case) to ₹4.86 (bull case); at ₹30.08, the current price sits above that range. The share trades about 18% below its 52-week high and 45% 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 5, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2022 – FY2026 · reported fiscal years
KIRANPR reported revenue of ₹8.7M in FY2026 versus ₹7.8M in FY2022, a compound +2.9%/yr. Reported net income was ₹1.2M in FY2026, compounding +30.7%/yr from FY2022.
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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.