Verified Beta was founded by Andrew Jones, CFA, as an adjunct to his public and private company valuation practice, M&A advisory work, and role as a fractional CFO to scale-stage SaaS businesses. The need originates with asset allocation and portfolio optimization for independently-managed family assets. The initiative gives Andrew an excuse to return to his software engineering roots whilst keeping his analytical, research, and writing skills sharp. He loves this stuff almost as much as writing about himself in the 3rd person.

Equity ETFs marketed as Smart Beta have attracted over $1T of AUM. When filtering for funds in Verfiedbeta’s analysis of over 3000 US-listed ETFs that feature statistically-significant exposure to *at least one* of the well-researched ‘factors’ such as Value and are at least 2 years old, the total is over $3T of the $6T aggregate AUM.

The top few passive index funds have amassed roughly the other 50% of aggregate AUM in the US-listed ETF universe and are essentially free, charging just 1-3bps in total net fees. In stark contrast, Smart beta funds suffer **fees and turnover-related trading costs on the order of 100 – 150 bps per year**, or 30-50 times greater.

The expectation is that the contribution from Smart Beta factor exposure more than makes up for fees and trading costs. But how often is that *actually* true? How often are investors assuming that a so-called Smart Beta ETF really is a *smart investment*, when it might otherwise be a source of negative expected returns relative to a passive index fund? The waters are further muddied by the prospect that factor returns are unstable and likely decaying over time.

Finally, following the recently-ended macro economic epoch characterized by decade-plus-long near-zero interest rates and concomitant widespread distortions in fundamental value, how can we manage the risk that we’re not overpaying for equity ETFs writ large?

We systematically identify those funds that:

- capture factor contributions projected to cover their management fees and implied trading costs;
- have projected forward (ex-ante) Sharpe ratios superior to the market, based on real, measured, in-sample fund volatility and factor capture;
- are presently composed of equities fairly-valued or inexpensive relative to their historical median valuation, in the aggregate.

Throughout our fund analyses, we standardize core summary metrics defined as follows:

Our Measure | Definition |
---|---|

Implied Relative Factor Sharpe | the ratio between the ex-post Sharpe Ratio of a simulated fund with factor loadings equal to those derived in the in-sample regression with actual fund data, including alpha (the typically-negative regression intercept), divided by the market Sharpe Ratio, calculated over the maximum period of available factor data. |

Factor Capture Score | a 100% score is assigned to all funds that rank above the 95% percentile on Implied Relative Factor Sharpe. For funds below the 95% percentile, the Score is (Implied Relative Factor Sharpe [fund]) / (Implied Relative Factor Sharpe [95% percentile fund]). We avoid using straightforward percentiles as Implied Relative Factor Sharpe tends toward a fat tail of closely-clustered underperforming funds; this measure clearly highlights the relatively few winners. |

Value, Small, Profitability, Investment, and Momentum Scores | identical to Factor Capture Score for each of the respective factors, separately. eg. a score of 100% for Value (HML) means the fund ranks above the 95% percentile of all funds with statistically-significant exposure to the Value factor. |

Net Implied Factor Alpha | the contribution from factor exposure of the simulated fund, less fees and implementation costs as implied by the regression intercept. |

Relative Valuation | the ratio between the current fund dividend yield and the median historical fund dividend yield, expressed in percent. |

We perform systematic, fund universe-wide regressions and then use back-tested synthetic fund performance to infer our best estimate of expected forward performance. We’ve standardized on the Fama and French 5-factor model plus momentum and refer to the model as FF6. Although the Q5 and AQR models appear to subsume FF6 in the GRS test for model superiority, we elect FF6 both because of its pervasiveness and its frequency of overlap with actual ETF product implementation. We summarize comparisons with the AQR and Q5 models where fund constitution criteria are aligned.

Stepwise (see Definitions for reference terms, above):

**perform**OLS linear regressions on monthly fund total return data to determine the best-estimate of historical factor capture (regression coefficients);**create**a synthetic fund with equal factor exposure starting in July of 1963 (the first month of publicly-available FF6 data);**calculate**the Implied Relative Factor Sharpe;**rank**all funds in the universe to generate Factor Capture Score;**calculate**the individual Factor Capture Scores;**calculate**the Relative Valuation**summarize**in Factor Tombstones, fund spotlight analyses, and our ETF Finder Tool

It’s a one-two punch:

- We
**summarize**estimated risk-adjusted fund performance*relative to*to the historical return of a plain-vanilla market-cap-weighted passive index fund with a single metric: Factor Capture Score - We
**compare**current fund valuation to historical valuation to help allocators avoid funds which are presently overvalued, expensive and potentially risky.

We detail the process, method, and analytics.

Simple, intuitive summary metrics.

All US-listed ETFs as they become listed and establish return history.