NIHARINF (NIHARINF) Fair Value & Analysis
IN · Market cap ₹59.3M
Fair value as of: Jul 5, 2026
From 14 valuation models · updated today
Share price −3.6% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹4.40 – ₹8.28 · fair‑value band ₹8.42 – ₹14.03 · the ₹5.70 price screens below the ₹11.22 fair value. As of Jul 5, 2026.
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NIHARINF (NIHARINF) currently trades at ₹5.70, while our model-based Fair Value estimate is ₹11.22 — implying the stock looks roughly 96.8% undervalued today. We read business quality at 56/100 (solid quality). Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: medium) — always confirm before acting.
Over the trailing twelve months, NIHARINF generated revenue of ₹217M at a net margin of 3.2%. Revenue declined 10.8% year over year. It earns a return on equity of 10.7%. Net debt stands at ₹15.2M. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹8.42 (bear case) to ₹14.03 (bull case); at ₹5.70, the current price sits below that range. The share trades about 34% below its 52-week high and 36% 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
NIHARINF reported revenue of ₹217M in FY2026 versus ₹95.2M in FY2022, a compound +22.8%/yr. Reported net income was ₹6.9M in FY2026, compounding +20.5%/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.