MODIS (MODIS) Price Target & Share Price Forecast

Not a guess. A distribution.

1-Year Price Target (median)₹256-25.3%

As of , the MODIS (MODIS) 1-year price target is ₹256-25.3% from the current price of ₹343. The 80% confidence range is ₹164₹398, with a 19.7% probability of finishing above today's price.

MODIS 2027
₹256
-25.3%
MODIS 2029
₹139
-59.5%
MODIS 2031
₹77
-77.6%

Probability-weighted price target and forecast for MODIS (MODIS) across 2027, 2029, and 2031. Built from a 10,000-trial Monte Carlo simulation on 0.6 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 0.6 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)
25.3%
80% range₹164–₹398
P(price ↑)20%
P(price 2×)0%
3-Year Forecast
2029
₹0
Median (P50)
59.5%
80% range₹65–₹300
P(price ↑)7%
P(price 2×)0%
5-Year Forecast
2031
₹0
Median (P50)
77.6%
80% range₹29–₹208
P(price ↑)3%
P(price 2×)0%

MODIS price probability fan

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

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

Probability of key outcomes

What are the odds MODIS 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 MODIS price target & forecast are calculated

We ran 10,000 simulated price paths for MODIS (MODIS) using Geometric Brownian Motion (GBM) — the same probability framework used in institutional risk-management systems. The simulation uses MODIS's actual 0.6-year historical volatility (34.6%) and mean log return (-24.1%/year), so it reflects real market behaviour, not assumptions.

Each of the 10,000 trials projects a unique MODIS 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 MODIS 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 MODIS could realistically land — so you can plan for the spread of outcomes, not bet on a wish.

MODIS price target & forecast — probability table

HorizonPessimistic (P10)Median (P50)Optimistic (P90)P(↑ from today)P(2× return)
1 year (2027)₹164₹256₹39819.7%0.2%
3 years (2029)₹65₹139₹3006.7%0.4%
5 years (2031)₹29₹77₹2082.6%0.2%

Generated 23/6/2026, 8:12:15 am. Refreshed every 6 hours from 0.6y of NSE history.

MODIS price target & forecast — FAQs

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

Based on a 10,000-trial Monte Carlo simulation using historical volatility, MODIS's 5-year median (P50) forecast is ₹77. The 80% confidence band is ₹29₹208. The probability of the price being above today's ₹343 in 5 years is 2.6%.

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 MODIS double in 5 years?

The probability of MODIS reaching 2× the current price (₹685) within 5 years is 0.2%, 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 0.6 years of historical data.

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