PRISMX (PRISMX) Fair Value & Analysis
IN · Market cap ₹263M
Fair value as of: Jul 5, 2026
From 12 valuation models · updated today
Share price −4.8% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹0.5000 – ₹0.7400 · fair‑value band ₹0.7100 – ₹1.22 · the ₹0.6000 price screens below the ₹0.9700 fair value. As of Jul 5, 2026.
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PRISMX (PRISMX) currently trades at ₹0.6000, while our model-based Fair Value estimate is ₹0.9700 — implying the stock looks roughly 61.7% undervalued today. We read business quality at 57/100 (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, PRISMX generated revenue of ₹112M at a net margin of 54.3%. Revenue declined 39.6% year over year. It earns a return on equity of 7.7%. Net debt stands at ₹22.4M. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹0.7100 (bear case) to ₹1.22 (bull case); at ₹0.6000, the current price sits below that range. The share trades about 23% below its 52-week high and 30% 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
FY2021 – FY2025 · reported fiscal years
PRISMX reported revenue of ₹112M in FY2025 versus ₹90.2M in FY2021, a compound +5.6%/yr. Reported net income was ₹60.9M in FY2025, compounding +107.1%/yr from FY2021.
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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.