RGRL (RGRL) Fair Value & Analysis
IN · Market cap ₹56.2M
Fair value as of: Jul 6, 2026
From 14 valuation models · updated today
Share price −2.6% over the past month.
Price vs Fair Value (12 months)
12‑month range ₹0.9900 – ₹7.11 · fair‑value band ₹0.4300 – ₹0.8100 · the ₹1.48 price screens above the ₹0.6200 fair value. As of Jul 6, 2026.
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RGRL (RGRL) currently trades at ₹1.48, while our model-based Fair Value estimate is ₹0.6200 — implying the stock looks roughly 58.1% overvalued today. We read business quality at 36/100 (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, RGRL generated revenue of ₹14.1M at a net margin of 9.9%. It earns a return on equity of 0.3%. The stock trades on a trailing P/E of 37.0. Fundamentals as of Jul 6, 2026
Our scenario range runs from ₹0.4300 (bear case) to ₹0.8100 (bull case); at ₹1.48, the current price sits above that range. The share trades about 80% below its 52-week high and 56% 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
RGRL reported revenue of ₹13.9M in FY2026 versus ₹14.2M in FY2022, a compound −0.6%/yr. Reported net income was ₹1.4M in FY2026, compounding −26.3%/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.