SHAHISHIP (SHAHISHIP) Fair Value & Analysis
IN · Market cap ₹193M
Fair value as of: Jul 6, 2026
From 1 valuation models · updated today
Share price +2.3% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹12.47 – ₹24.71 · fair‑value band ₹15.23 – ₹31.42 · the ₹13.30 price screens below the ₹20.94 fair value. As of Jul 6, 2026.
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SHAHISHIP (SHAHISHIP) currently trades at ₹13.30, while our model-based Fair Value estimate is ₹20.94 — implying the stock looks roughly 57.4% undervalued today. We read business quality at 32/100 (below-average 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: low) — always confirm before acting.
Over the trailing twelve months, SHAHISHIP generated revenue of ₹64.6M at a net margin of -28.2%. Revenue declined 48.3% year over year. Net debt stands at ₹87.5M. Fundamentals as of Jul 6, 2026
Our scenario range runs from ₹15.23 (bear case) to ₹31.42 (bull case); at ₹13.30, the current price sits below that range. The share trades about 49% below its 52-week high and 11% 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 6, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2022 – FY2026 · reported fiscal years
SHAHISHIP reported revenue of ₹64.6M in FY2026 versus ₹94.0M in FY2022, a compound −9.0%/yr. Reported net income was −₹18.2M 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.