504093 (504093) Fair Value & Analysis
Other · IN · Market cap ₹1.1B
Fair value as of: Jul 4, 2026
From 22 valuation models · updated today
Share price −4.8% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹252.20 – ₹387.13 · fair‑value band ₹110.44 – ₹184.06 · the ₹268.85 price screens above the ₹147.25 fair value. As of Jul 4, 2026.
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504093 (504093) currently trades at ₹268.85, while our model-based Fair Value estimate is ₹147.25 — implying the stock looks roughly 45.2% overvalued today. We read business quality at 55/100 (solid quality), in the Other sector. 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).
The stock trades on a trailing P/E of 34.3. Fundamentals as of Jul 4, 2026
Key figures & financial health
Figures from reported company fundamentals (EODHD) · as of Jul 4, 2026. TTM = trailing twelve months.
Revenue & earnings trend
FY2022 – FY2026 · reported fiscal years
504093 reported revenue of ₹2.7B in FY2026 versus ₹2.4B in FY2022, a compound +2.8%/yr. Reported net income was ₹34.9M in FY2026, compounding −21.9%/yr from FY2022.
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6 more Other stocks, each showing price versus our Fair Value estimate (as of Jul 4, 2026).
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| There MOH | €521.90 | €269.92 | -48% |
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| Federal Home Loan Mortgage Corporation FREJO | $16.84 | $64.93 | +286% |
| 533278 533278 | ₹438.60 | ₹720.31 | +64% |
| Nestl?? India Limited 500790 | ₹1,459 | ₹279.60 | -81% |
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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.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.