VerifiedBeta is an independent ETF research project. It is a systematic screen across US-listed and Canadian-listed equity ETFs, using industry-standard factor regressions, risk-adjusted performance relative to the market, and dividend-based relative valuation to support better ETF due diligence.
If you want the broader context before diving into the formulas, start with the About VerifiedBeta page. If you want to see the framework applied rather than described, start with the US ETF Leaders, the Canadian ETF Leaders, or the full US and Canadian universe tables. This page explains the logic behind those rankings and how to interpret the public metrics.
Coverage
Coverage begins with the current US ETF universe and the current TMX Canadian ETF universe. A fund enters the regression universe only after it has established at least 24 monthly total-return observations. Funds that do not yet meet that threshold are tracked as excluded for insufficient history and are not mixed into the leaders tables.
To qualify for a Leaders list, a fund must also earn at least one visible non-market factor score beyond market beta alone.
Separately, VerifiedBeta uses an intentionally loose first-pass equity detector to decide which funds merit a broader factor-space search. That routing gate only asks whether the fund has enough market exposure and statistical significance to justify deeper analysis. It is deliberately inclusive and is not itself a publication-quality stamp.
What We Optimize For
- Factor exposure that has historically delivered better risk-adjusted results than the market after a realistic allowance for implementation drag.
- Clean, statistically credible capture of the intended factors rather than incidental noise.
- Dividend-based relative valuation that compares the current recurring yield with the fund's own history.
Score Construction
The public Fund Score is a capped ratio rather than a raw percentile. That matches the code used to build the leaders tables:
Fund Score = min(Relative Sharpe / P95(Relative Sharpe of passing funds), 1.0) * 100
In plain terms, the score is each fund's backtested relative Sharpe scaled to the 95th-percentile passing fund and capped at 100. That works better than a simple percentile because a percentile forces equal spacing between ranks even when the underlying Sharpe differences are small. Scaling to the 95th-percentile passing fund preserves more of the real gap between credible funds while still capping extreme outliers at 100.
Dividend-Based Valuation
Relative valuation is based on recurring distribution yield, which is our most objective and least gameable valuation proxy at the fund level. We use split-adjusted fund distributions so older payouts are comparable with today's share count, and we exclude one-off special or capital-gains distributions from the valuation series because they do not represent durable earning power. Those cash payments still remain in total return. In practice we compare the fund's current recurring yield with its own historical median recurring yield and express the result on a 100 = median scale. Values above 100 indicate the fund is rich relative to its own history; values below 100 indicate it is inexpensive.
Definitions
| Column | Definition |
|---|---|
| Fund Score | The fund's Rel. Sharpe / Backtested scaled to the 95th-percentile passing fund and capped at 100, so the score preserves real performance gaps without letting outliers dominate. |
| Mkt Beta Score | How strong the fund's statistically significant market-beta loading is relative to the 95th-percentile passing fund on that same factor. |
| Small / Value / Profitability / Investment / Momentum Factor Score | US-only factor-strength scores. Each is scaled to the 95th-percentile passing fund among those with statistically significant exposure to that factor. |
| Quality Factor Score | Canadian-model analog to the quality sleeve, using AQR's QMJ factor. |
| Alpha % | Annualized regression intercept over the analysis period. In practice this is usually negative and acts as a proxy for implementation drag not captured by the factor model, including fees, trading frictions, and the limitations of long-only fund construction. |
| Non-Market Factor Contribution % (Backtested Simulation) | Geometrically annualized contribution of the fund's combined non-market factor sleeves over the full-history factor backtest. This is computed from the combined monthly non-market contribution series itself, not by summing separately annualized factor bars. |
| Fund Sharpe / Market Sharpe (Backtested Simulation) | Calculated as the fixed-loading factor backtest's geometric average excess return divided by the fund's actual realized lifetime volatility. This is generally conservative, because realized fund volatility is usually higher than the smoother volatility produced by the fixed-loading factor backtest. The figure is shown only when regression fit clears the market's minimum Leaders-quality threshold. |
| Fund Sharpe / Market Sharpe (Fund Lifetime, Actual) | Calculated as the fund's realized geometric average excess return over its actual analysis period divided by its realized volatility over that same period. The figure is shown only when regression fit clears the market's minimum Leaders-quality threshold. |
| Analysis Period | Regression window length shown in years and months. |
| Start Date | First month included in the regression window. |
| Score Quality (Adj. R^2) | Adjusted R-squared from the regression. Higher values mean the factor model explains more of the fund's return variation. |
| Relative Dividend Valuation (100 = Median) | Current split-adjusted recurring distribution yield versus the fund's own historical median recurring yield. Special distributions are excluded from this valuation measure but remain in total return. Values above 100 indicate the fund is rich relative to its own history; values below 100 indicate it is inexpensive. |
| MER % | Management expense ratio where the upstream source provides it. This is currently available in the Canadian Leaders and Universe tables, and in the US tables where fee data is available from the upstream ETF source. |
Process
- Load the current ETF universe for the relevant market.
- Exclude funds with fewer than 24 monthly returns from regression eligibility.
- Run the market's default factor regression.
- For funds that clear the loose equity-candidate gate, search a bounded factor space across supported families and simple regional blends.
- Accept an alternate factor family only when the best searched model clears the minimum fit threshold.
- Use the fitted coefficients to estimate a synthetic all-time factor portfolio.
- Compare the synthetic portfolio's Sharpe ratio with the market's Sharpe ratio.
- Score the fund and each significant factor exposure relative to the passing universe.
- Overlay split-adjusted recurring-distribution valuation and publish the resulting leaders tables.
Models
The US leaders page uses the Fama-French 5-factor model plus momentum (UMD). The Canadian leaders page uses the current AQR Canada factor set: market, size, value, quality, and momentum. Because the Canadian model does not expose separate profitability and investment legs, the Canadian public table uses a single quality score instead.
Currency basis. Canadian-fund regressions are run on a native CAD basis: fund total returns are kept in CAD, the risk-free rate is the FRED 3-month Canadian interbank series (IR3TIB01CAM156N), and the Canadian market factor is AQR's USD-denominated MKT_CAN algebraically inverted to CAD using the same end-of-month CAD/USD series applied consistently across VerifiedBeta's return calculations, ensuring same-day alignment. The four AQR long-short factors (size, value, quality, momentum) are first-order FX-neutral and used as published. We previously regressed Canadian funds in USD basis, which inflated R² by 1–13 percentage points (depending on each fund's FX-correlated exposure) because the FX series sat inside both sides of the regression. The CAD-basis numbers are the un-inflated, structural fit. The same minimum R² threshold (0.85) is now applied to the CAD-basis figure.
Model search and public best-fit regressions. For broader factor-family classification, VerifiedBeta runs a bounded search across supported pure factor families first, then Europe/Pacific regional blends only when a hybrid improves adjusted R² by a material margin. When a fund is successfully classified, its Universe-table metrics and fund profile use the best-fit model instead of the listing-market default. TMX-listed funds are compared in USD-normalized space for the fit-search step so cross-region model selection is not distorted by CAD/USD translation. Public Canadian-equity winners remain on the native CAD decomposition basis; TMX-listed non-Canadian-equity winners use the best-fit USD-normalized regression basis publicly.
Machine-Readable Data
VerifiedBeta also publishes a static API for agents and researchers. The API exposes usable-regression funds, leaders tables, ticker aliases, model metadata, and currently published factor-similar peers as JSON and CSV files. Use the API for scripts and structured workflows; use the Universe page URLs when you want to share a human-readable filtered browser view.
Verified Factor Similarity
VerifiedBeta's first public ETF comparison pages use a deliberately limited similarity lane. A pair can qualify only when both funds are US-listed equity ETFs, both use the same USD Fama-French 6-factor public model, and both have at least one non-market factor that is statistically significant and material. Broad-market funds are intentionally excluded unless they have verified non-market factor exposure.
The key gate is exact material-factor overlap. If one fund has statistically significant, material exposure to size, value, and profitability, the comparison set requires the other fund to have that same material factor set. This is why a fund with an extra significant sleeve, or a missing significant sleeve, is not treated as a closest substitute even if the tickers look similar in a conventional ETF peer group.
After that gate, the similarity score blends four terms: normalized distance between the funds' factor-loading levels, the shape similarity of their non-market factor vector, market-beta closeness, and a small secondary term for backtested relative-Sharpe closeness. Exposure level and vector shape dominate the score. Backtested relative Sharpe is a secondary input because the similarity score is designed to emphasize factor-exposure match first, not historical payoff alone.
The comparison pages are therefore best read as a factor-exposure match, not as a recommendation. They answer: "Do these funds harvest the same verified factor sleeves at roughly similar magnitudes?" They do not yet answer every implementation question about index construction, holdings methodology, turnover, tax handling, or issuer process.
FAQ
What does Fund Score actually measure?
Fund Score is the fund's Rel. Sharpe / Backtested expressed as a percentage of the 95th-percentile passing fund and capped at 100. It is a normalized way to compare credible funds without letting a few outliers dominate the top of the table.
What is relative Sharpe?
The site uses two relative Sharpe variants. Rel. Sharpe / Backtested is the fixed-loading factor backtest's geometric average excess return divided by the fund's actual realized lifetime volatility, then compared with the market's Sharpe ratio over the same full-history framework. Rel. Sharpe / Actual uses the fund's realized geometric average excess return and realized volatility over its actual analysis period. In both cases, a value above 1.0 means the fund delivered better risk-adjusted results than the market benchmark used in that framework.
Both Sharpe fields are shown only when Adj. R^2 clears the same minimum quality threshold used by the Leaders screen for that market. Below that threshold, the values are hidden because the factor model is not reliable enough to support a meaningful Sharpe estimate.
Why does alpha matter if factor exposure explains most of the return?
Alpha in this context mostly captures implementation drag that the factor model does not explain cleanly. Fees, turnover, trading frictions, index construction choices, and long-only constraints all tend to show up here, which is why persistent negative alpha is an important warning sign.
How should I interpret dividend valuation?
Dividend valuation compares the current recurring distribution yield with the fund's own history on a 100 = median scale. Values below 100 imply the fund is inexpensive relative to its own yield history, while values above 100 imply it is rich. It is not a market-timing tool; it is a cross-check on what price you are paying for a given factor sleeve.
Why are live and backtested relative Sharpe different?
The backtested series is a synthetic factor portfolio built from the fund's fitted exposures across a longer factor-history window. The live figure is the fund's actual realized history. The gap between them is expected because factor cycles, fees, construction drag, and timing of the fund launch all affect realized outcomes.