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BUG Global X - Cybersecurity ETF

ETF

51.02 Close 10/29
Since 2024-11-29 +52.4%

Price and fund-flow snapshot

As of 2026-10-29

Momentum: Strong; Risk / liquidity: Weak.

Momentum Strong All 2 indicators are Top.
60-day return (%) 23.74 97th percentile Top
240-day return (%) 56.9 94th percentile Top
Momentum Trading Strategy: A 12-1 Momentum Backtest
Risk / liquidity Weak Distance from peak is Good, but 20-day dollar volume is Lagging.
60-day annualized volatility (%) 47.7 87th percentile Lagging
Distance from peak (%) -2.54 68th percentile Good
20-day dollar volume 102,725,516 12th percentile Lagging
Factor Investing Explained: 10 Classic Factors in US Stocks

Global X - Cybersecurity ETF (BUG) today

2 quant signals active · As of 2026-10-29

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 Global X - Cybersecurity ETF across different dimensions, combines them into a multifactor strategy, and backtests it against the market.

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Global X - Cybersecurity ETF (BUG) quant signal backtests

Signals active now

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

52-week adjusted-price high

Active

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

15 historical occurrences (since 2020-12-08)

Average forward return after 52-week adjusted-price high

20-day: Win 53.3%, Sample 15, Median +1.02%, Excess -0.13%, Beat mkt rate 46.7%

60-day: Win 61.5%, Sample 13, Median +2.16%, Excess -0.89%, Beat mkt rate 53.8%

120-day: Win 54.5%, Sample 11, Median +2.98%, Excess -4.93%, Beat mkt rate 18.2%

120 days after: historical avg +0.75%

20-day +1.42%
60-day +2.68%
120-day +0.75%
20-day
60-day
120-day
Win
53.3%
61.5%
54.5%
Sample
15
13
11
Median
+1.02%
+2.16%
+2.98%
Excess
-0.13%
-0.89%
-4.93%
Beat mkt rate
46.7%
53.8%
18.2%

Over 11 occurrences, 120-day forward avg gained 0.75%, lagged the market by 4.93 pts; win rate 54.5%.

Top-quintile 6-month momentum

Active · now 98.85%

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
19 historical occurrences (since 2020-05-04)

Average forward return after Top-quintile 6-month momentum

20-day: Win 57.9%, Sample 19, Median +1.32%, Excess +0.8%, Beat mkt rate 47.4%

60-day: Win 63.2%, Sample 19, Median +1.99%, Excess +0.28%, Beat mkt rate 63.2%

120-day: Win 64.7%, Sample 17, Median +4.51%, Excess -1.34%, Beat mkt rate 35.3%

120 days after: historical avg +5.12%

20-day +1.08%
60-day +3.46%
120-day +5.12%
20-day
60-day
120-day
Win
57.9%
63.2%
64.7%
Sample
19
19
17
Median
+1.32%
+1.99%
+4.51%
Excess
+0.8%
+0.28%
-1.34%
Beat mkt rate
47.4%
63.2%
35.3%

Over 17 occurrences, 120-day forward avg gained 5.12%, lagged the market by 1.34 pts; win rate 64.7%.

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

Holding-period returns in its own history

Since 2019-10-31, 66.1% of 1,506 one-year holding periods ended with a gain; the median one-year return was +15.4%. Over three-year holding periods, 93.5% of 1,002 ended with a gain (median +22.1%).

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.

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 +7.34%, win rate 42.9%, beating the market by +4.98 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.

12 times historically; 60-day forward avg +5.9%, win rate 66.7%, beating the market by +1.02 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.