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FNDA Schwab Fundamental U.S. Small Company Index ETF

ETF

35.84 Close 10/22
Since 2024-11-21 +17.2%

Price and fund-flow snapshot

As of 2026-10-22

Risk / liquidity: Weak.

Momentum Neutral 240-day return is Good, but 60-day return is Lagging.
60-day return (%) -2.98 20th percentile Lagging
240-day return (%) 19.42 78th percentile Good
Risk / liquidity Weak 60-day annualized volatility is Good, but 20-day dollar volume is Lagging.
60-day annualized volatility (%) 11.93 40th percentile Good
Distance from peak (%) -6.61 47th percentile Average
20-day dollar volume 89,331,274 1th percentile Lagging

Schwab Fundamental U.S. Small Company Index ETF (FNDA) today

1 quant signals active · As of 2026-10-22

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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Schwab Fundamental U.S. Small Company Index ETF (FNDA) peers and tracking

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Schwab Fundamental U.S. Small Company Index ETF (FNDA) quant signal backtests

Signals active now

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

Back above the 200-day average

Active · now 1.47%

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

24 historical occurrences (since 2017-08-14)

Average forward return after Back above the 200-day average

20-day: Win 65.2%, Sample 23, Median +1.37%, Excess -0.29%, Beat mkt rate 47.8%

60-day: Win 60.9%, Sample 23, Median +4.36%, Excess +0.28%, Beat mkt rate 65.2%

120-day: Win 65.2%, Sample 23, Median +5.53%, Excess +0.44%, Beat mkt rate 39.1%

120 days after: historical avg +5.87%

20-day +1.49%
60-day +2.85%
120-day +5.87%
20-day
60-day
120-day
Win
65.2%
60.9%
65.2%
Sample
23
23
23
Median
+1.37%
+4.36%
+5.53%
Excess
-0.29%
+0.28%
+0.44%
Beat mkt rate
47.8%
65.2%
39.1%

Over 23 occurrences, 120-day forward avg gained 5.87%, beat the market by 0.44 pts; win rate 65.2%.

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

Holding-period returns in its own history

Since 2016-01-04, 71.9% of 2,465 one-year holding periods ended with a gain; the median one-year return was +9.6%. Over three-year holding periods, 93.1% of 1,961 ended with a gain (median +32.0%).

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 +0.11%, win rate 56%, beating the market by -1.89 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 +14.1%, win rate 100%, beating the market by +1.44 pts on average.

Top-quintile 6-month momentum

Pending

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

18 times historically; 60-day forward avg -0.08%, win rate 50%, beating the market by -1.43 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.
  • us_fund_price:adj_close records this ETF’s distributions regularly only from 2020-06-24; returns before that date mostly reflect price changes and splits, so they understate its total return.

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.