NETTLINX Fair Value & Analysis
IN · Market cap ₹361M
Fair value as of: Jul 22, 2026
From 8 valuation models · updated today
Share price −9.9% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹13.80 – ₹30.59 · fair‑value band ₹5.35 – ₹10.13 · the ₹14.88 price screens above the ₹8.11 fair value. As of Jul 22, 2026.
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NETTLINX (NETTLINX) currently trades at ₹14.88, while our model-based Fair Value estimate is ₹8.11, implying the stock looks roughly 45.5% overvalued today. We read business quality at 29/100 (below-average 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: medium).
Over the trailing twelve months, NETTLINX generated revenue of ₹209M at a net margin of -24.7%. Revenue declined 41.0% year over year. It earns a return on equity of -7.9%. Net debt stands at ₹82.6M. Fundamentals as of Jul 22, 2026
Our scenario range runs from ₹5.35 (bear case) to ₹10.13 (bull case); at ₹14.88, the current price sits above that range. The share trades about 54% below its 52-week high and 8% 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 22, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2021 – FY2025 · reported fiscal years
NETTLINX reported revenue of ₹209M in FY2025 versus ₹344M in FY2021, a compound −11.7%/yr. Reported net income was −₹51.7M 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.
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Not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.