Portfolio Construction Calculator
The formula
What the portfolio construction tells you
The power law as arithmetic: a fund's MOIC is the weighted sum of position multiples — 8% winners at 15× plus 22% at ~1× clears about 2.4×.
Calculation: Portfolio MOIC = Σ (class weight × class multiple) — equal position sizing assumed. Enter your inputs and every substitution is shown below the result.
Reading the result
The "winners needed" line is the planning tool: if entry prices cannot plausibly produce that hit rate, the strategy is a story.
Rules of thumb: ~6% of startups return ~60%+ of all venture dollars
Limitations and best practice
Equal weighting hides the real lever — reserves doubled into winners are where funds actually compound.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Why not simply diversify wider?
Do most funds follow this math?
Source & methodology
Gompers, Kovner & Lerner (2020); NBIA statistics.
Portfolio Construction explained: formula, worked example and honest limits
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. There are plenty of pages about Portfolio Construction; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
What makes Portfolio Construction worth calculating is not precision but position: it tells you which side of a threshold you are on, and by how much. A rough figure that settles the direction beats an elaborate one that never commits.
Business runs on numbers that are cheap to compute and expensive to ignore. Portfolio Construction in Business & Entrepreneurship is one of those: Portfolio MOIC = Σ (class weight × class multiple) — equal position sizing assumed, and the gap between founders who check and founders who hope shows up exactly here.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here expected portfolio MOIC = 1.41 ×, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Winner multiple first, at ≈6.4 % of the headline for a ten-percent error either way in that input. Work down the page and by the end you could re-derive the result in a spreadsheet from memory.
Inside the Portfolio Construction calculation: formula and every input
The engine implements a single stated model: Portfolio MOIC = Σ (class weight × class multiple) — equal position sizing assumed. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
% of positions that become winners (%) (example value 5) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Enter it exactly as your document states it — quoted annual, monthly and effective rates are three different numbers.
Winner multiple (×) (example value 18) is a measured number in the unit named by the label; the formula does not convert for you. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
% returning about 1× (%) (example value 22) is a measured number in the unit named by the label; the formula does not convert for you. Rates are the most mis-transcribed input on any page like this; copy it from the document rather than retyping from memory.
Remaining positions' multiple (×) (example value 0) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
The default Portfolio Construction calculation, written out in full
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: % of positions that become winners (%), Winner multiple (×), % returning about 1× (%), Remaining positions' multiple (×). The computed expected portfolio MOIC comes out at 1.41 ×, which the interpretation band labels “The power law is not carrying this book”.
- MOIC = 5%×18 + 22%×1 + 73%×0.4 = 1.41×
Winners' contribution — 0.90 ×. This is the number experienced users quote when the headline alone would mislead.
Middle (1×) contribution — 0.22 ×. Read next to the headline, this is the figure that shows the shape of the trade-off.
Tail contribution — 0.29 ×. If your inputs change, this is usually the first companion figure to move.
Winner share needed for a 3× fund — 13.82 %. Compare it against the main result of 1.41 × before drawing conclusions.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
The Portfolio Construction corridor: your result across a realistic band
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Winner multiple setting | Expected portfolio MOIC |
|---|---|
| 80 % of the example value | 1.23 × |
| 90 % of the example value | 1.32 × |
| example inputs | 1.41 × |
| 110 % of the example value | 1.50 × |
| 120 % of the example value | 1.59 × |
Across 5 settings of Winner multiple, the result spans 1.23 × to 1.59 ×. A decision that only works at one row of this table is a decision waiting for an input to move. Reproduce it in the form above by editing Winner multiple alone; the substitution steps will follow your figures exactly.
What actually moves your Portfolio Construction
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the expected portfolio MOIC. For Portfolio Construction the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Winner multiple +10 % → Portfolio Construction = 1.50 × · −10 % → 1.32 × (the expected portfolio MOIC shifts ≈6.4 %)
- Nudge % of positions that become winners by ±10 % and Portfolio Construction lands between 1.32 × and 1.50 × (sensitivity ≈6.2 %)
- Nudge Remaining positions' multiple by ±10 % and Portfolio Construction lands between 1.38 × and 1.44 × (sensitivity ≈2.1 %)
- Nudge % returning about 1× by ±10 % and Portfolio Construction lands between 1.40 × and 1.43 × (sensitivity ≈0.9 %)
Winner multiple is the dominant lever here: ten percent either way moves the result by about 6.4 %, from 1.32 × to 1.50 ×. Spend your verification effort there first, and % of positions that become winners is effectively tied with it (≈6.2 % for the same nudge), so verify the pair together. Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
Is your Portfolio Construction good — reading the result bands
Nobody acts on a bare figure — they act on a figure with a comparison attached. The default case lands in the “The power law is not carrying this book” band, and bands are deliberately wide because real situations overlap. The sensitivity list shows how far a modest input error moves you along that ladder: for Winner multiple, ±10 % covers 1.32 × to 1.50 ×. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Set Portfolio Construction beside its neighbours and it usually plays a specific role: this figure establishes the size of something, and the neighbouring tools establish whether that size is acceptable. Skipping the first step makes the second one arbitrary.
How people get Portfolio Construction wrong
Equal weighting hides the real lever — reserves doubled into winners are where funds actually compound. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reading Portfolio Construction from a single month. Business figures have seasonality and noise; compare across a full cycle before believing the trend.
This model has its own particular trap: Winner multiple carries ≈6.4 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
A final trap is presentation: reporting Portfolio Construction to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Before you trust your Portfolio Construction: a short checklist
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Compare the result with one independent method or published benchmark.
- Check Winner multiple before anything else: it is the measured dominant input, ≈6.4 % of Portfolio Construction per ten percent.
- Note the date and the inputs beside the number so the figure can be audited later.
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
A routine for decision-grade Portfolio Construction
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — one document, one pass. Open the statement, label or record that holds % of positions that become winners (%), winner multiple (×), % returning about 1× (%), remaining positions' multiple (×) and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. Comparisons are cheap: run the identical calculation for the alternative and look at the gap.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Expected portfolio MOIC (1.41 × for the example values), and note the verdict band. If a result feels obviously right, that is the moment to check the inputs hardest.
Step 3 — isolate your variables. Change one field, observe, revert. The live sensitivity list does the ranking for the default inputs; verify it against your own, because dominance shifts when the starting point shifts. Numbers are cheap to produce and expensive to retract; the extra minute is the cheapest insurance available.
Step 4 — ask what would have to be true. Take the result you want and work backwards through the inputs until you find the assumption that produces it; then decide honestly whether that assumption is defensible. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 5 — schedule the next run. In Business & Entrepreneurship the inputs drift quietly. Put a reminder a quarter out, re-enter the same fields, and read the change rather than the level. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Who actually uses Portfolio Construction — and when
Most Startup Metrics disagreements are arithmetic disagreements in disguise. When Expected portfolio MOIC of 1.41 × sits on the table with its working shown, the conversation stops being about who sounds more confident and starts being about which input is wrong. Precision is a promise you make about your data; make sure your inputs can keep it.
Used properly this is a five-minute filter that saves weeks. Compute Portfolio Construction, reject what fails, and only then bring in the expensive analysis for the survivors. Estimates improve most when the worst case is written down in ink.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Business & Entrepreneurship the expensive mistakes are rarely sudden; they are small changes in % of positions that become winners (%) that nobody recalculated for a year. Note the assumption you are least sure about; that is where the real risk lives.
Portfolio Construction — frequently asked questions
What affects Portfolio Construction the most?
Portfolio Construction is driven above all by Winner multiple. Perturbing only that field by ten percent either way produced 1.50 × against the 1.41 × baseline — a ≈6.4 % response, while % of positions that become winners manages ≈6.2 %.
Do % of positions that become winners and winner multiple matter equally?
On these inputs, yes: the two measured within a rounding error of each other — ≈6.4 % and ≈6.2 % of the expected portfolio MOIC per ten-percent change. Treat them as one pair to verify together rather than ranking them.
What is a typical Portfolio Construction?
With the example inputs on this page the result is 1.41 ×, labelled “The power law is not carrying this book”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Portfolio Construction by hand?
Use the stated model — Portfolio MOIC = Σ (class weight × class multiple) — equal position sizing assumed — and substitute in the order the tool prints (it starts from MOIC = 5%×18 + 22%×1 + 73%×0.4 = 1.41×). On the example inputs the hand route lands on 1.41 ×, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to Portfolio Construction if winner multiple changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 1.32 × to 1.50 × across the ten-percent probe, i.e. ≈6.4 % sensitivity on the expected portfolio MOIC.
Is the Portfolio Construction result of 1.41 × good or bad?
Good and bad are comparisons, so the honest test is three questions: where does your own Portfolio Construction sit against the 1.41 × reference above, how far would a plausible input error move it (≈6.4 % per ten percent on Winner multiple), and what does the alternative option compute to?
Can Portfolio Construction be negative or empty?
Yes, and that is informative rather than broken: a negative Portfolio Construction usually means the inputs encode an outflow or a shortfall, and the verdict band above labels exactly that situation instead of hiding it.
What does 1.41 × tell me about the business?
It answers the narrow question mechanically and the broad question only in context: Portfolio Construction is one dashboard light, and it pairs with unit economics and cash timing before any bet the business size.
Why not simply diversify wider?
Fixed capital spread over 60 names buys raffle tickets, not outcomes — ownership depth is the point.
Do most funds follow this math?
Roughly; deviations come from timing and from how hard they press the winners.
Do I need an account to use this Portfolio Construction calculator?
No, and no data leaves the tab. The engine runs in the page itself, which is why the worked example, the bands and the sensitivity ranking all respond instantly to your keystrokes.
Glossary for Portfolio Construction
- Winners' contribution
- 0.90 × on the example inputs — a companion figure printed beside the headline of Portfolio Construction so that no result is read in isolation.
- Middle (1×) contribution
- 0.22 × on the example inputs — a companion figure printed beside the headline of Portfolio Construction so that no result is read in isolation.
- Tail contribution
- 0.29 × for the Portfolio Construction worked case; it sits next to the main result because it carries context the headline leaves out.
- Expected portfolio MOIC
- The primary output of this tool: 1.41 × for the default inputs, computed exactly from Portfolio MOIC = Σ (class weight × class multiple) — equal position sizing assumed.
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “The power law is not carrying this book”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Portfolio Construction moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
The model is published arithmetic implemented exactly as written: Portfolio MOIC = Σ (class weight × class multiple) — equal position sizing assumed. Source of record: Gompers, Kovner & Lerner (2020); NBIA statistics. Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The promise to the reader is narrow and testable: same inputs, same output, no silent rounding, and no figure presented without the formula that produced it.
After the number: what to do with it
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
This page was written for people who need one trustworthy number before their next decision, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.