FT Cboe Vest ETF Performance
| GAUG ETF | 40.77 -0.03 -0.07% |
Risk-Adjusted Performance
0High
8 · Moderate
Over the last 90 days, FT Cboe Vest ranks in the bottom 92% of global equities and portfolios on a risk-adjusted return basis. A lower ranking does not preclude recovery, but it does signal that recent efficiency has lagged peers. FT Cboe has produced near-zero returns recently, indicating neutral to weak return quality for holders. Learn More
Relative Risk vs. Return Landscape
If you had invested $ 3,957 in FT Cboe Vest on February 6, 2026 and sold it today, you would have earned $ 120.00 , a return of 3.03% over 90 days. FT Cboe Vest is currently generating a 0.0485% daily expected return and carries 0.4694% risk (volatility on return distribution) over a 90-day horizon. In relative terms, FT Cboe exhibits above-average volatility, exceeding roughly 96% of comparable etfs, and GAUG has trailed 99% of traded instruments in return over the 90-day horizon. Expected Return |
| Risk |
Target Price Odds to finish over Current Price
Prices of ETFs like GAUG ETF tend to oscillate around a central value, a phenomenon known as mean reversion. Research shows that certain ETFs remain mispriced until demand-supply dynamics shift, suggesting embedded risk premiums. Additional risk factors may account for the delayed correction observed in some mispriced ETFs. Incorporating mean reversion alongside momentum and volatility analysis strengthens GAUG ETF forecasting.
| Current Price | Horizon | Target Price | Odds moving above the current price in 90 days |
| 40.77 | 90 days | 40.77 | under 4% |
Under a normal probability framework, the likelihood of FT Cboe moving above the current price in 90 days from now is under 4%. The historical return profile over this window has produced more above-current than below-current outcomes. (The distribution shows where the market has recently assigned the greatest probability for GAUG ETF within 90 days). Use the curve width to gauge whether the current setup for GAUG ETF looks concentrated or dispersed.
FT Cboe Price Density |
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Predictive Modules for FT Cboe
Predicting future values of FT Cboe Vest in the ETF market involves navigating significant uncertainty. Investors who apply multiple methods and compare results are better positioned to manage risk around FT Cboe Vest. Cross-checking model outputs helps calibrate expectations about FT Cboe Vest in changing market conditions. Investors who recognize forecasting limitations while still using structured methods gain a meaningful analytical edge.While mean reversion in FT Cboe is a statistically observable tendency, it operates on uncertain timelines. Mean reversion signals in FT Cboe's arise when prices disconnect from earnings, book value, or historical multiples. Mean reversion in FT Cboe is more reliable over longer time horizons than shorter ones. In highly covered equities like FT Cboe, the mean reversion window tends to be shorter.
Primary Risk Indicators
Market turbulence over the past two decades has affected virtually every corner of the ETF market, including FT Cboe. Price swings in FT Cboe during this period have created both risk and opportunity for investors. Monitoring FT Cboe's fundamental risk indicators anticipates market swings. The risk indicator data for FT Cboe supports a systematic approach to portfolio protection.Performance Metrics & Calculation Methodology
Drawdown and recovery analysis for FT Cboe reveals how the fund behaves during stress episodes and subsequent rebounds. Comparing drawdown severity across periods reveals whether risk characteristics are stable or shifting.
FT Cboe Vest analytics rely on fund disclosures and market reference feeds, with quality checks and normalization applied. Return and risk statistics are calculated from historical price series.
Editorial review and methodology oversight provided by: Michael Smolkin, Member of Macroaxis Board of Directors