TRITON (TRITON) Fair Value & Analysis
IN · Market cap ₹414M
Fair value as of: Jul 6, 2026
From 15 valuation models · updated today
Share price −18.1% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹0.5900 – ₹3.47 · fair‑value band ₹0.2200 – ₹0.4100 · the ₹2.12 price screens above the ₹0.3100 fair value. As of Jul 6, 2026.
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TRITON (TRITON) currently trades at ₹2.12, while our model-based Fair Value estimate is ₹0.3100 — implying the stock looks roughly 85.4% 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, TRITON generated revenue of ₹93.3M at a net margin of 10.1%. It earns a return on equity of 182.2%. Net debt stands at ₹25.0M. The stock trades on a trailing P/E of 25.9. Fundamentals as of Jul 6, 2026
Our scenario range runs from ₹0.2200 (bear case) to ₹0.4100 (bull case); at ₹2.12, the current price sits above that range. The share trades about 39% below its 52-week high and 259% 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
FY2021 – FY2025 · reported fiscal years
TRITON reported revenue of ₹153M in FY2025 versus ₹3.2B in FY2021, a compound −53.3%/yr. Reported net income was −₹237K in FY2025.
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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.