MIL (MIL) Fair Value & Analysis
IN · Market cap ₹297M
Fair value as of: Jul 5, 2026
From 10 valuation models · updated today
Share price +26.9% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹21.10 – ₹41.47 · fair‑value band ₹51.97 – ₹148.95 · the ₹29.74 price screens below the ₹100.46 fair value. As of Jul 5, 2026.
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MIL (MIL) currently trades at ₹29.74, while our model-based Fair Value estimate is ₹100.46 — implying the stock looks roughly 237.8% undervalued today. We read business quality at 43/100 (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: medium) — always confirm before acting.
Over the trailing twelve months, MIL generated revenue of ₹854M at a net margin of -3.8%. Revenue grew 5.7% year over year. It earns a return on equity of -27.1%. Net debt stands at ₹612M. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹51.97 (bear case) to ₹148.95 (bull case); at ₹29.74, the current price sits below that range. The share trades about 31% below its 52-week high and 44% 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
FY2022 – FY2026 · reported fiscal years
MIL reported revenue of ₹841M in FY2026 versus ₹743M in FY2022, a compound +3.2%/yr. Reported net income was −₹32.0M 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.