Every numeric claim made by the SWG Architect portfolio-sizing tool traces back to one of the published sources below. Titles, dates, findings and URLs are reproduced verbatim from the SWG Architect Portfolio-Sizing Methodology & Citation Pack (verified 3 June 2026).
Past performance is not a reliable indicator of future results. Capital is at risk; you may get back less than you invest. This is not investment advice.
List A — Sources supporting the sizing thesis
Tier 1–3 sources. Relevance is graded honestly. No source states the literal "30, ideally 60–80" band; that range is an interpolation off curves and thresholds these sources do report. "Direct" means the source directly addresses how portfolio size affects returns; it does not mean it endorses the exact numbers.
A1
How Portfolio Size Affects Early-Stage Venture Returns
Nigel Koh & Abraham Othman, PhD — AngelList Data Science
Date:
April 2020
Tier:
Tier 2 (institutional data-science research; not a fund marketing page)
Across 10,665 real LP portfolios, median IRR p.a. was 11.9% for investors with >50 investments vs 2.9% at ≤50; 11.1% (>25) vs 2.0%; 11.5% (>100) vs 4.1%. ~90% of investors with ≥90 investments were 'in the money' vs under 50% with ≤3. Each extra company added ~9.0 bps median / ~6.9 bps mean p.a. Strongest empirical anchor for 'more positions → higher and more reliable returns.'
Monte Carlo on tens of thousands of portfolios (US, vintages since 2000, sizes 1–100): at 8 positions the chance of a fund-level loss (<1×) was 28% vs 6% at 100; chance of a 5×+ fund was 10% at 8 vs 2% at 100; median converges to mean by ~100. Firm's framing: expected (mean) return is roughly equal across sizes — only the risk shape changes.
Open sourceHosted on the firm's own channel; included under the institutional-self-publishing carve-out.
A3
US PE/VC Benchmark Commentary: Calendar Year 2024 (with the firm's investment-level dispersion analysis)
Cambridge Associates LLC
Date:
Benchmark commentary published Nov 2025 (CY2024 data)
Tier:
Tier 2 (the most-referenced institutional VC benchmark)
The US Venture Capital Index returned 6.2% in 2024 (rebounding after negative 2022–23) and has outperformed public markets over long horizons; the index is built from ~2,537 US VC funds (1981–2024). Cambridge's dispersion work shows median fund IRRs cluster around ~10% (net) while top-quartile upside and loss risk vary widely by strategy — VC carrying the widest top-to-bottom spread of any private strategy.
British Business Bank (analysis of PitchBook, Preqin, survey of 50 UK GPs, and Bank data)
Date:
2 December 2025
Tier:
Tier 3 (government-affiliated; the BVCA CEO is quoted in the release)
UK VC funds (2002–2020 vintages) returned a pooled TVPI of 1.84 (US 1.95; rest of Europe 1.85); 2020–2023 UK vintages (1.22) edged the US (1.14). Crucially for sizing: VC showed the highest dispersion of any private asset class — an upper-quartile TVPI of 2.15 — and only 8% of UK funds reached TVPI ≥3 versus 13% (US) / 14% (Europe). The Bank's own conclusion: 'dispersion remains high in VC, so manager selection and vintage timing are important.'
158 UK angels, £134m, 1,080 investments, 406 exits: 56% of exits returned <capital while the 9% returning >10× produced ~80% of all positive cash flows; overall 2.2× / ~22% gross IRR over ~4 years; ≥20 hours of diligence reduced failures. Honesty flag: establishes the concentration (9% → ~80%) but does NOT prescribe a position count.
Robert Wiltbank & Warren Boeker — Ewing Marion Kauffman Foundation & Angel Capital Education Foundation
Date:
November 2007
Tier:
Tier 3 (foundation research, academic authorship)
~1,137 angels, >1,100 exits: average 2.6× in 3.5 years (~27% IRR), heavily skewed (many losses, occasional very large wins). Re-analysis (DeGennaro & Dwyer, European Financial Management, 2014) estimates expected returns ~70% over the risk-free rate, with large variance.
Open sourceAbstract / citation live; full PDF via SSRN download. DOI 10.2139/ssrn.1028592.
A7
Startup Growth and Venture Returns
Abraham Othman, PhD — AngelList Data Science
Date:
December 2019
Tier:
Tier 2
Fitting AngelList data, winning seed investments after ~5 years draw from an α < 2 power law (unbounded mean), so an investor raises expected return by indexing into 'every credible deal' at seed — a result that does NOT hold at later stages. Lays out the three regimes that govern the whole debate.
The Power Law: Venture Capital and the Making of the New Future
Sebastian Mallaby — Penguin Press / Penguin Random House, 496 pp.
Date:
February 2022
Tier:
Tier 4 (book, recognised publisher)
ISBN 9780525559993 (US hc); 9780525560005 (ebook); 9780141988948 (UK paperback). The organising thesis — most venture bets fail while a tiny number succeed at a scale that more than compensates, and this asymmetry drives the whole VC model — is set out in the Introduction and recurs through the Sequoia, Kleiner Perkins and Accel chapters. Page numbers differ between US hc and UK pb; cite by chapter. Mallaby argues the disposition the power law demands (back outliers, let winners run); he does not prescribe a numeric position count.
John H. Cochrane — Journal of Financial Economics 75(1), 3–52 (NBER WP 8066)
Date:
January 2005
Tier:
Tier 1 (peer-reviewed)
Selection-bias-corrected MLE gives an arithmetic mean return ~59%/yr but SD ~100% and a heavily right-skewed (lognormal) distribution; Cochrane describes VC investments as option-like — small chance of a huge payoff, high chance of total loss — and notes only sufficiently large VCs can 'effectively diversify.'
The principled method — maximum-likelihood estimation of the scaling parameter plus a Kolmogorov–Smirnov goodness-of-fit test — for detecting and validating power laws; warns that common least-squares fitting is biased. This is the method the AngelList papers rely on.
Robert S. Harris (Virginia), Tim Jenkinson (Oxford), Steven N. Kaplan (Chicago) — Journal of Finance 69(5), 1851–1882 (NBER WP 17874)
Date:
October 2014
Tier:
Tier 1 (peer-reviewed)
Using a research-quality Burgiss dataset of ~1,400 US buyout and VC funds, buyout has consistently beaten public markets (outperformance vs the S&P 500 averaging 20–27% over a fund's life); VC beat public equities in the 1990s but lagged in the 2000s — cross-validated against Cambridge Associates, Preqin and Venture Economics.
The thesis is robust as a downside-management argument for a broadly diversified strategy. It is weaker (a) as an expected-return claim and (b) for an active value-add manager.
C1
The expected-return caveat lives inside the pro-diversification camp
Correlation Ventures (see A2)
Date:
29 April 2022
Tier:
Counter-evidence / qualifier
Correlation Ventures — the firm demonstrating diversification's benefit — also states that expected (mean) returns are the same across portfolio sizes; larger portfolios cut loss probability and cut home-run probability, and average deal quality tends to fall as size grows. Implication: a 'higher multiple at 60–80' claim is the exposed one; 'lower loss probability + higher median' is defensible.
Diversification in Private Equity Funds: On Knowledge Sharing, Risk Aversion, and Limited Attention
Mark Humphery-Jenner — Journal of Financial and Quantitative Analysis (JFQA)
Date:
2013
Tier:
Tier 1 (peer-reviewed). Full text paywalled; Cambridge abstract live.
Beyond a point, finite partner attention/monitoring dilutes the value-add that drives returns — over-diversification can erode fund-level returns ('limited attention').
Open sourceCambridge abstract live; full text paywalled.
C3
Diversification, risk, and returns in venture capital
Buchner, Mohamed & Schwienbacher — Journal of Business Venturing 32(5), 519–535
Date:
2017
Tier:
Tier 1 (peer-reviewed). DOI 10.1016/j.jbusvent.2017.05.005. Full text paywalled.
Diversification across industries can RAISE VC returns — but via enabling riskier, higher-upside bets, not via simple variance reduction; a specialisation literature (Gompers, Kovner & Lerner) cuts the other way. Net: the academic picture is mixed; avoid a clean 'more = higher multiple' claim.
Open sourceScienceDirect / DOI live; full text paywalled.
C4
The α<2 assumption is contested, and it changes the claim
AngelList (A7) vs Correlation Ventures (A2)
Date:
—
Tier:
Counter-evidence / qualifier
AngelList (A7) estimates α<2 (more positions raises the mean); Correlation's flat-mean result (A2) implies 2<α≤3 (more positions raises the median, not the mean). If UK EIS sits at α>2 — which the flat-mean evidence makes plausible — the honest benefit of 30→60–80 is narrower dispersion and a higher median / lower loss probability, not a higher expected multiple. Frame it that way.
The indexing result (A1/A7) holds only if the investor can sample the true market distribution; diversifying within a weak or adversely-selected funnel will not replicate market returns. For the tool: position count is necessary but not sufficient — deal-source quality/breadth must be scored alongside it, or the diversification claim is overstated.
Even the originator now de-emphasises the multiples
SyndicateRoom
Date:
Current canonical research framing
Tier:
Counter-evidence / qualifier
SyndicateRoom has retired the pages stating 3.7×/4.7× and moved to a variance/probability framing ('50/year to minimise variation'). This corroborates C1/C4: the durable claim is risk-shape, not headline return.
Open sourceCurrent canonical research home (open).
Information document only — not investment advice or personal recommendation. SWG is not FCA-authorised to give investment advice. The Wealth Architects Programme operates under FPO 2005 exemption reliance.|Methodology v2026.1-SWG·Disclosure v6.1-2026-05