TRIVENIENT (TRIVENIENT) Fair Value & Analysis
IN · Market cap ₹57.8M
Fair value as of: Jul 6, 2026
From 6 valuation models · updated today
Share price −8.2% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹1.00 – ₹1.74 · fair‑value band ₹0.3600 – ₹0.6000 · the ₹1.01 price screens above the ₹0.4800 fair value. As of Jul 6, 2026.
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TRIVENIENT (TRIVENIENT) currently trades at ₹1.01, while our model-based Fair Value estimate is ₹0.4800 — implying the stock looks roughly 52.5% overvalued today. We read business quality at 51/100 (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: low).
Over the trailing twelve months, TRIVENIENT generated revenue of ₹4.6M at a net margin of 30.1%. Revenue declined 78.7% year over year. It earns a return on equity of 1.3%. The stock trades on a trailing P/E of 50.5. Fundamentals as of Jul 6, 2026
Our scenario range runs from ₹0.3600 (bear case) to ₹0.6000 (bull case); at ₹1.01, the current price sits above that range. The share trades about 42% below its 52-week high and 2% 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
TRIVENIENT reported revenue of ₹0 in FY2026 versus ₹105M in FY2022. Reported net income was ₹1.4M in FY2026, compounding −39.0%/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.