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QUAL iShares MSCI USA Quality Factor ETF

ETF

222.49 Close 10/30
Since 2024-12-02 +21.9%

Price and fund-flow snapshot

As of 2026-10-30

Momentum: Good, Risk / liquidity: Good.

Momentum Good All 2 indicators are Good.
60-day return (%) -0.89 57th percentile Good
240-day return (%) 15.51 72th percentile Good
Momentum Trading Strategy: A 12-1 Momentum Backtest
Risk / liquidity Good All 3 indicators are Good.
60-day annualized volatility (%) 8.39 28th percentile Good
Distance from peak (%) -2.3 74th percentile Good
20-day dollar volume 224,353,346 50th percentile Good
Factor Investing Explained: 10 Classic Factors in US Stocks

iShares MSCI USA Quality Factor ETF (QUAL) today

1 quant signals active · As of 2026-10-30

Win rate and excess are historical post-trigger stats vs the market. Click a signal for its full definition and samples; history does not predict future results.

FinLab Studio

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AI finds several complementary traits of iShares MSCI USA Quality Factor ETF across different dimensions, combines them into a multifactor strategy, and backtests it against the market.

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iShares MSCI USA Quality Factor ETF (QUAL) quant signal backtests

Signals active now

Each active condition is tested against this ETF’s own history and benchmarked versus the S&P 500.

Top-quintile 6-month momentum

Active · now 84.41%

The ETF ranks in the top 20% of liquid US-listed ETFs by 126-day adjusted return.

Momentum Trading Strategy: A 12-1 Momentum Backtest
18 historical occurrences (since 2017-12-04)

Average forward return after Top-quintile 6-month momentum

20-day: Win 70.6%, Sample 17, Median +3.03%, Excess -0.05%, Beat mkt rate 41.2%

60-day: Win 70.6%, Sample 17, Median +2.26%, Excess -0.13%, Beat mkt rate 47.1%

120-day: Win 76.5%, Sample 17, Median +7.96%, Excess +0.32%, Beat mkt rate 58.8%

120 days after: historical avg +5.03%

20-day +1.42%
60-day +1.3%
120-day +5.03%
20-day
60-day
120-day
Win
70.6%
70.6%
76.5%
Sample
17
17
17
Median
+3.03%
+2.26%
+7.96%
Excess
-0.05%
-0.13%
+0.32%
Beat mkt rate
41.2%
47.1%
58.8%

Over 17 occurrences, 120-day forward avg gained 5.03%, beat the market by 0.32 pts; win rate 76.5%.

More history: Holding-period returns in its own history, Signals not active now

Holding-period returns in its own history

Since 2016-01-04, 86.7% of 2,471 one-year holding periods ended with a gain; the median one-year return was +15.7%. Over three-year holding periods, 100.0% of 1,967 ended with a gain (median +45.2%).

A holding period starts on every trading day, so periods overlap; they describe the historical distribution rather than independent outcomes.

Signals not active now

Inactive signals still show this ETF’s historical forward-return distribution for context.

52-week adjusted-price high

Pending

Adjusted close first reaches a 252-trading-day high; repeated hits within 20 days count once.

26 times historically; 60-day forward avg +2.76%, win rate 84%, beating the market by +0.04 pts on average.

20% below its peak

Pending

Adjusted close first falls 20% or more below its highest level so far; repeated hits within 20 days count once.

7 times historically; 60-day forward avg +9%, win rate 85.7%, beating the market by +0.45 pts on average.

Back above the 200-day average

Pending

Adjusted close crosses from below to above its 200-day moving average; repeated crosses within 20 days count once.

17 times historically; 60-day forward avg +3.36%, win rate 76.5%, beating the market by -0.17 pts on average.

Method and limits

  • Returns use us_fund_price:adj_close (finlab data.set_market('us_fund')), adjusted for splits and for distributions where the data includes them.
  • Excess returns use world_index:^GSPC, the S&P 500 price index without dividends, as the benchmark.
  • Percentiles and the 6-month momentum signal compare against the most liquid US-listed ETFs by 20-day dollar volume; mutual funds are excluded.
  • Category labels come from the fund data where it provides one (bond, income, crypto) and from the fund name for leveraged, inverse and commodity ETFs.
  • Overlapping event windows are descriptive distributions, not independent samples.

This page is for historical data analysis and education only. It is not investment advice. Backtests and historical return distributions do not predict future performance; evaluate risk independently before trading.