HOTLSILV (HOTLSILV) Fair Value & Analysis
IN · Market cap ₹180M
Fair value as of: Jul 5, 2026
From 24 valuation models · updated today
Share price −1.7% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹8.48 – ₹14.93 · fair‑value band ₹14.98 – ₹24.96 · the ₹11.06 price screens below the ₹19.97 fair value. As of Jul 5, 2026.
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HOTLSILV (HOTLSILV) currently trades at ₹11.06, while our model-based Fair Value estimate is ₹19.97 — implying the stock looks roughly 80.6% undervalued today. We read business quality at 58/100 (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: high) — always confirm before acting.
Over the trailing twelve months, HOTLSILV generated revenue of ₹261M at a net margin of 6.2%. Revenue declined 0.8% year over year. It earns a return on equity of 5.0%. Net debt stands at ₹73.5M. Fundamentals as of Jul 5, 2026
Our scenario range runs from ₹14.98 (bear case) to ₹24.96 (bull case); at ₹11.06, the current price sits below that range. The share trades about 26% below its 52-week high and 32% 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
HOTLSILV reported revenue of ₹261M in FY2026 versus ₹180M in FY2022, a compound +9.7%/yr. Reported net income was ₹16.2M in FY2026, compounding +10.6%/yr from FY2022.
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Frequently asked questions
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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.