RSL (RSL) Price Target & Share Price Forecast

Not a guess. A distribution.

1-Year Price Target (median)₹209+61.4%

As of , the RSL (RSL) 1-year price target is ₹209+61.4% from the current price of ₹130. The 80% confidence range is ₹133₹331, with a 91.2% probability of finishing above today's price.

RSL 2027
₹209
+61.4%
RSL 2029
₹549
+323.5%
RSL 2031
₹1,425
+998.4%

Probability-weighted price target and forecast for RSL (RSL) across 2027, 2029, and 2031. Built from a 10,000-trial Monte Carlo simulation on 0.3 years of NSE historical data — so you see the full range of where the price could realistically land, weighted by likelihood. No analyst opinions. Just statistics.

Spot Price · Today
₹0
Based on 0.3 years of daily NSE data ·0.0% annualised volatility
5-yr median forecast
₹0
P(price ↑ in 5y)
0%
1-Year Forecast
2027
₹0
Median (P50)
61.4%
80% range₹133–₹331
P(price ↑)91%
P(price 2×)28%
3-Year Forecast
2029
₹0
Median (P50)
323.5%
80% range₹251–₹1,203
P(price ↑)99%
P(price 2×)89%
5-Year Forecast
2031
₹0
Median (P50)
998.4%
80% range₹516–₹4,006
P(price ↑)100%
P(price 2×)98%

RSL price probability fan

Each band shows where 10,000 simulated paths land. The wider the fan, the more uncertainty.

Probability Fan
RSL simulated paths · 60 months · 10,000 trials
P10–P90 (80%)P25–P75 (50%)Median (P50)

Probability of key outcomes

What are the odds RSL hits common targets within the simulated horizon?

0%
P(↑ 1Y)
Above today's price in 1 year
0%
P(↑ 5Y)
Above today's price in 5 years
0%
P(2×)
Doubles within 5 years
0%
P(↓)
Falls below today in 5 years

How the RSL price target & forecast are calculated

We ran 10,000 simulated price paths for RSL (RSL) using Geometric Brownian Motion (GBM) — the same probability framework used in institutional risk-management systems. The simulation uses RSL's actual 0.3-year historical volatility (35.6%) and mean log return (54.1%/year), so it reflects real market behaviour, not assumptions.

Each of the 10,000 trials projects a unique RSL share price path day-by-day for 5 years. The percentile bands (P10/P50/P90) show the full distribution of outcomes — your real price target range, not a single guess.

Why this RSL forecast differs from analyst price targets: Analyst targets are point estimates from subjective valuation models. Monte Carlo price-target forecasts are probability distributions from actual market data. They tell you the range and likelihood of where RSL could realistically land — so you can plan for the spread of outcomes, not bet on a wish.

RSL price target & forecast — probability table

HorizonPessimistic (P10)Median (P50)Optimistic (P90)P(↑ from today)P(2× return)
1 year (2027)₹133₹209₹33191.2%28.1%
3 years (2029)₹251₹549₹1,20399.0%88.7%
5 years (2031)₹516₹1,425₹4,00699.9%98.4%

Generated 23/6/2026, 3:55:51 am. Refreshed every 6 hours from 0.3y of NSE history.

RSL price target & forecast — FAQs

What is the RSL (RSL) price target / share price forecast for 2031?

Based on a 10,000-trial Monte Carlo simulation using historical volatility, RSL's 5-year median (P50) forecast is ₹1,425. The 80% confidence band is ₹516₹4,006. The probability of the price being above today's ₹130 in 5 years is 99.9%.

How is Monte Carlo different from analyst price targets?

Analyst targets are point estimates based on subjective valuation models. Monte Carlo simulations produce a probability distribution from actual historical volatility — showing the full range of where the price could realistically land, weighted by likelihood. No opinions, just statistics.

Can RSL double in 5 years?

The probability of RSL reaching 2× the current price (₹259) within 5 years is 98.4%, based on this simulation.

Is this prediction accurate?

No simulation can predict the future — but Monte Carlo gives you a calibrated range of outcomes weighted by historical probability. It accounts for volatility better than any single price target. Use it as a decision-support tool, not a guarantee.

How often is this forecast updated?

Every 6 hours, based on the latest NSE close prices and 0.3 years of historical data.

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