Multi-Factor ETFs

Last Updated: 5/9/2026

Our combined factor score ranks all ETFs using a combination of value, momentum, quality, and low volatility. The ETFs with the highest scores and the greatest consistency across the factors will receive the best rankings.

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ETFs with Most Multi-Factor Exposure

Ticker	ETF Name	Total Assets	Expense Ratio	Total Holdings	Active Share vs. S&P 500	Active Fee	Active Share vs. Russell 2000	Size Exposure	Value Exposure	Quality Exposure	Multi-Factor Exposure	Low Volatility Exposure
OMFL	Invesco Russell 1000Dynamic Multifactor ETF	$5,995,671,000	0.29%	377	100.00%	0.29%	100.00%	31	58	54	99	57
RDVY	First Trust NASDAQ Rising Div Achiev ETF	$22,213,320,000	0.50%	51	99.85%	0.50%	99.93%	22	61	90	99	63
RFDA	RiverFront Dynamic US Dividend Advantage ETF	$71,090,340	0.52%	75	100.00%	0.52%	98.86%	35	56	68	99	56
ROUS	Hartford Multifactor US Equity ETF	$596,976,800	0.19%	367	99.51%	0.19%	96.21%	34	59	70	99	77
AFLG	First Trust Active Factor Large Cap ETF	$616,896,400	0.55%	238	100.00%	0.55%	99.81%	25	46	74	98	58
BFOR	BARRON'S 400SM ETF	$205,590,900	0.65%	400	100.00%	0.65%	84.87%	57	64	70	98	41
IUS	Invesco RAFI Strategic US ETF	$515,349,000	0.19%	556	99.58%	0.19%	98.22%	31	62	58	98	71
QLC	FlexShares US Quality Large Cap ETF	$807,941,100	0.25%	168	100.00%	0.25%	100.00%	16	47	80	98	66
WBIL	WBI Large Cap Tactical Select Shares ETF	$28,959,520	1.65%	143	100.00%	1.65%	99.51%	30	39	59	98	65
AVUS	Avantis U.S. Equity ETF	$10,698,630,000	0.15%	2,375	99.55%	0.15%	91.72%	53	53	65	97	59

Performance Disclaimer: Returns presented on Validea.com are model returns and do not represent actual trading. As a result, they do not incorporate any commissions or other trading costs or fees. Model portfolios with inception dates on or after 12/30/2005 include a combination of back tested and live model returns. The back-tested performance results shown are hypothetical and are not the result of real-time management of actual accounts. The back-testing of performance differs from actual account performance because the investment strategy may be adjusted at any time, for any reason and can continue to be changed until desired or better performance results are achieved. Back-tested returns are presented to provide general information regarding how the underlying strategy behind the portfolio performed in our historical testing. A back-tested strategy has the benefit of hindsight and the results do not reflect the impact that material economic or market factors may have had on advisor's decision-making if actual client assets were being managed using this approach. The model portfolios offered on Validea are concentrated and as a result they will exhibit high levels of volatility and their performance can be substantially impacted by the performance of individual positions.

Optimal portfolios presented on Validea.com represent the rebalancing period that has led to the best historical performance for each of our equity models. Each optimal portfolio was determined after the fact with performance information that was not available at portfolio inception. As a result, an investor could not have invested in the optimal portfolio since its inception. Optimal portfolios are presented to allow investors to quickly determine the portfolio size and rebalancing period that has performed best for each of our models in our historical testing.

Both the model portfolio and benchmark returns presented for all equity portfolios on Validea.com are not inclusive of dividends. Returns for our ETF portfolios and trend following system, and the benchmarks they are compared to, are inclusive of dividends. The S&P 500 is presented as a benchmark because it is the most widely followed benchmark of the overall US market and is most often used by investors for return comparison purposes. As with any investment strategy, there is potential for profit as well as the possibility of loss and investors may incur a loss despite a past history of gains. Past performance does not guarantee future results. Results will vary with economic and market conditions.

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