Why most capital allocation is sub-optimal
The standard process — rank projects by IRR, fund the list until capital runs out — leaves enormous value on the table. It ignores how risky each project is, how correlated they are with each other, and how much downside the portfolio can absorb before it's in trouble. The result is portfolios that look acceptable on a pitch deck but underperform what the same capital, allocated with proper position sizing, would have produced.
The fundamental insight from quantitative finance — that the right amount to commit to a bet depends on its edge and its variance, not just its expected return — almost never makes it into capital allocation decisions. Capvium applies that math directly: each project gets a recommended commitment level that maximizes long-run growth subject to your drawdown tolerance.
How position-sized capital allocation works
Capvium applies position-sizing math to the capital-project portfolio problem. The output is a defensible recommendation: how much to commit to each project given its edge, its variance, and the rest of the portfolio's exposure.
Expected value, not point estimates
Each project is modeled as a distribution of outcomes — capex, schedule, oil price, throughput — rather than a single number. The expected value is the probability-weighted average across that distribution. Two projects with identical NPV but different risk profiles get treated very differently by the allocator.
Position-size each project for the portfolio it lives in
The allocator commits capital in proportion to each project's edge and variance — and to how it moves with the rest of the portfolio. Sizing is deliberately conservative, because bankruptcy is not an acceptable outcome for a capital portfolio.
Correlation-aware optimization
Two projects exposed to the same oil price, the same long-lead vendor, or the same regional permit risk aren't independent. The optimizer accounts for correlations explicitly — which often surfaces hidden concentration risk that the IRR ranking missed.
Bound the worst case, not just the average
You set the maximum acceptable portfolio drawdown — say, 20% of equity. The allocator finds the highest-growth allocation that keeps the worst-case portfolio outcome above that floor. Bounded risk, optimized growth.
Why Capvium
- Quantitative-finance math, applied to physical capital. Position sizing, downside control, and correlation modeling — borrowed from where the math is most rigorous and adapted for capital projects.
- Defensible recommendations. Every allocation comes with explicit assumptions, sensitivity analysis, and a clear walk-away rationale. No black-box outputs.
- Built for the capital committee. Outputs are the priority list, the trade-off table, and the "what changes if we relax this constraint" answer — not a 200-row analyst dashboard.
- Built by capital project practitioners. Founded by engineers with deep, hands-on experience allocating capital across complex industrial portfolios.
See your portfolio with proper position sizing
Upload your project list — get a position-sized, correlation-aware allocation with explicit downside bounds in under a minute.
Open the Dashboard →What it looks like in practice
Illustrative scenario — not a customer engagement. An infrastructure investor has $400M of dry powder and seven candidate projects: two solar farms, a wind asset, a battery storage facility, a transmission line, and two gas peaker upgrades. The IRR ranking puts the gas peakers first — they have the highest unlevered IRR. The conventional allocation would commit 60% of capital to them.
Capvium runs the same data through position-sizing math with correlation modeling. The two gas peakers are highly correlated with natural gas price — funding both concentrates exposure. The optimizer recommends one peaker (not two), expands the battery storage commitment because it's anti-correlated with the renewables, and sizes the transmission line conservatively because its returns are highly variance-sensitive to permit timing. Expected portfolio return gives up a little; the worst-case portfolio outcome and the portfolio's concentration risk both improve materially.
This complements the broader portfolio question covered in project portfolio optimization with AI and the project-level inputs come from AI capital project risk management. EPCs evaluating which jobs to bid use the same engine — see AI for EPC contractors.
Frequently asked questions
What does capital allocation software actually do?
It takes a list of investable projects, your available capital and constraints, and your risk tolerance, and returns a quantified recommendation: how much to commit to each, in what order, with what expected return and downside. Implementation details are shared under NDA.
How is this different from standard portfolio math?
Classical growth-optimal position sizing is too aggressive for portfolios where bankruptcy is unacceptable and projects have fat-tailed downside — which describes most capital portfolios. We apply a risk-bounded variant calibrated for capital-project portfolios; the specific calibration parameters aren't disclosed publicly.
How does it integrate with our existing financial models?
Project-level NPV, schedule, and risk inputs can be uploaded from your existing models, or maintained directly in the platform. The optimization runs on whatever inputs you provide — the value is in the math layer that sits on top of them.