What is INSM's forecasted revenue?

Insmed Incorporated is forecasted to generate $1.46B in next-four-quarter revenue, representing +28.3% year-over-year growth.

How accurate is the INSM revenue forecast model?

The model has 8.8% training MAPE (Mean Absolute Percentage Error). In a holdout test, it predicted $0.3B versus the actual $0.4B.

Insmed Incorporated INSM

Revenue Intelligence Report • 31 quarters of SEC filing data • Updated 2026-10-14 • Data through 2026-06-30

Insmed Incorporated's latest quarterly revenue is $425M for the period ending 2026-06-30. The model projects $1.5B across the next four quarters (+28.3% versus the preceding four quarters). The forecast uses Operating expense proxy data; model selection and estimation exclude the latest quarter, whose retrospective holdout error is 28.4%. These are statistical associations, not estimates of the causal effect of spending.

Investment Thesis

The ARDL forecast is based on reported revenue and Operating expense proxy data through 2026-06-30. Training MAPE measures fit to historical data; the separately reported holdout error measures a withheld quarter. Spending coefficients describe associations and do not establish causation.

Next 4 Quarters

$1.46B

+28.3% YoY

R&D Elasticity

-0.88x

Operating expense proxy Elasticity

1.06x

Training Fit

8.8% MAPE

Retrospective holdout (Q2 FY2026): The model predicted $304.4M vs the actual $425.5M — an error of 28.4%.
Retrospective one-quarter holdout: model fitting and selection exclude this quarter. Historical inputs use the latest available restatements, so this is not a preserved real-time forecast.

Note: The current filings use Operating expense proxy (CostsAndExpenses). Spending estimates refer to this expense measure, not separately reported SG&A.

⚠ Model limitation: One or more estimated spending associations are negative. These observational coefficients do not establish a causal return on spending.

Investor insight: Actual revenue ($425M) came in 28% above the spending-based forecast ($304M). This suggests that Insmed Incorporated's recent revenue growth is driven significantly by external demand factors — such as market pricing, product cycle tailwinds, or structural demand shifts — beyond what its R&D and SG&A spending alone would predict.

Revenue Forecast

Quarterly Detail

Quarter	Model Forecast	Actual	95% Range	YoY Growth	Status
Q2 FY2026 Period end 2026-06-30	$304.4M	$425.5M	$244.4M–$379.2M	+183.4%	✗ Outside range
Q3 FY2026 Period end 2026-09-30	$307.1M		$225.1M–$418.9M	+115.7%
Q4 FY2026 Period end 2026-12-31	$352.0M		$240.7M–$514.9M	+33.4%
Q1 FY2027 Period end 2027-03-31	$379.5M		$244.7M–$588.8M	+24.0%
Q2 FY2027 Period end 2027-06-30	$420.7M		$257.5M–$687.4M	-1.1%

Seasonal Factors

Multiplicative seasonal adjustment: These factors capture Insmed Incorporated's systematic quarterly revenue patterns relative to the trend model. A factor of 1.05 means that quarter typically runs 5% above the underlying trend; 0.95 means 5% below. Factors are computed as the median of (actual / fitted) across all available quarters.

Fiscal Quarter	Seasonal Factor	vs Trend	Interpretation	Obs.
FQ1 (Jan–Mar)	0.9372	-6.3%	-6.3% below trend	7
FQ2 (Apr–Jun)	0.9822	-1.8%	In line with trend	7
FQ3 (Jul–Sep)	1.0225	+2.2%	In line with trend	7
FQ4 (Oct–Dec)	1.0473	+4.7%	+4.7% above trend	7

Spending and Revenue

Reading this chart: Each line shows the cumulative revenue association with a 1% change in spending over subsequent quarters. These estimates describe historical associations and do not establish causation.

Time-Varying Spending Associations

Time-varying analysis: A penalized spline model (GAM) tracks the association between log spending and log revenue over 31 quarters. Its coefficients describe conditional historical associations, not causal returns on spending.
Current Operating expense proxy elasticity: -0.4422x • R&D elasticity: 1.7956x
The model-term chart shows relative absolute term magnitudes. Its percentages are not shares of revenue or causal contributions to growth.

Want this analysis for your portfolio?

I build custom revenue intelligence reports for investors and companies using SEC filing data, econometric modeling, and AI-powered insights.

Get in Touch