GraniteShares 1.5x Long Meta Daily ETF

10-Year Study

FBL.US · · US · ETF

Executive Summary: GraniteShares 1.5x Long Meta Daily ETF has compounded at 94.8% annually over the last 10 years, with a maximum drawdown of 52.4% and an annualized volatility of 112.7%.

1Y CAGR
-10.0%
3Y CAGR
+45.4%
5Y CAGR
+94.8%
10Y CAGR
+94.8%

History & Riski10-year historical performance analysis including CAGR, Max Drawdown, Sharpe & Sortino ratios, annual returns, and rolling volatility — all computed from daily market data.

10-Year Growth of $10,000

Max DrawdownMax DrawdownThe largest peak-to-trough decline in the asset's value over the measurement period.Click for full definition →
52.4%
Sharpe RatioSharpe RatioRisk-adjusted return: how much excess return you earn per unit of total risk (volatility).Click for full definition →
1.97
Sortino RatioSortino RatioLike Sharpe, but only penalizes downside volatility — a more accurate risk measure for asymmetric return distributions.Click for full definition →
4.03
Ann. VolatilityAnnualized VolatilityThe annualized standard deviation of an asset's returns — a measure of how much prices fluctuate.Click for full definition →
67.5%
Best YearBest & Worst YearThe single calendar year with the highest and lowest return in the measured period.Click for full definition →
2023 · +341.6%
Worst YearBest & Worst YearThe single calendar year with the highest and lowest return in the measured period.Click for full definition →
2026 · -2.2%
% Positive Years% Positive YearsThe percentage of calendar years in the measurement period where the asset delivered a positive return.Click for full definition →
75%

Annual Returns

Rolling 12-Month Returns

Rolling 12-Month Annualised Volatility

Historical Drawdowns

Monthly Returns

Monthly Returns Heatmap

YearJanFebMarAprMayJunJulAugSepOctNovDecAnn.
202615.0-19.1-24.138.4————————-2.2%
202536.2-7.0-27.1-14.236.728.17.6-9.9-2.0-24.4-1.72.70.5%
202415.450.9-2.8-24.216.215.3-13.818.919.5-3.21.52.7112.7%
202336.624.631.919.215.610.416.1-11.41.1-0.412.111.4341.6%

Risk X-RayiA 19-factor macroeconomic risk decomposition showing exactly which market forces (equity beta, rates, inflation, credit, commodity, crypto) drive this asset's volatility. Powered by multivariate regression against daily factor returns.

Risk Profile Insight: This asset has an estimated annualized volatility of 112.7%. The dominant macroeconomic risk driver is SHV.US, accounting for 47.7% of variance. Idiosyncratic stock-specific factors contribute 11.0%.

10-Year Historical Price Series (Growth of $10,000)
DateSimulated Value
2022-12-0110000
2023-01-0113658.67115809037
2023-02-0117019.979091648274
2023-03-0122448.89069578348
2023-04-0126761.819026600067
2023-05-0130943.198977813914
2023-06-0134168.602625159714
2023-07-0139668.079916366594
2023-08-0135137.35625508189
2023-09-0135529.3878499245
2023-10-0135374.60796840516
2023-11-0139637.87896387501
2023-12-0144158.72923684517
2024-01-0150948.7164595191
2024-02-0176906.72551980485
2024-03-0174783.94703217562
2024-04-0156713.90405389709
2024-05-0165876.98919735162
2024-06-0175950.45882216285
2024-07-0165507.02752932977
2024-08-0177857.18434196772
2024-09-0193024.45115576722
2024-10-0190093.5067952143
2024-11-0191487.68730398419
2024-12-0193935.1260308979
2025-01-01127912.06876524567
2025-02-01118948.48414449993
2025-03-0186707.22499709607
2025-04-0174357.06818445813
2025-05-01101646.82309211291
2025-06-01130188.75595307238
2025-07-01140091.47403879662
2025-08-01126261.76094784528
2025-09-01123700.48786154024
2025-10-0193508.24718318039
2025-11-0191943.02474154954
2025-12-0194407.01591357881
2026-01-01108578.23208270414
2026-02-0187815.07724474388
2026-03-0166674.41050063887
2026-04-0192287.14136368917
Annual Return Matrix
YearAnnual Return
20233.4158729236845167
20241.1272153355472985
20250.00502357214622462
2026-0.0224546293448169
Total Factor Risk
1.1268966653052248
VTI.US Exposure
0.10637218501713702
VEA.US Exposure
-0.01983541171098893
VWO.US Exposure
-0.00864491221678077
QQQ.US Exposure
0.05478306450785938
VTV.US Exposure
-0.032374890661772376
IJR.US Exposure
-0.0046528654692801616
QUAL.US Exposure
0.19232162123379812
SHV.US Exposure
0.4772973863600146
TLT.US Exposure
0.0011649585783134791
LQD.US Exposure
-0.014586345871943388
HYG.US Exposure
-0.006690104868575496
GLD.US Exposure
0.0001942837043086569
USO.US Exposure
-0.0007567857484551272
VNQ.US Exposure
-0.014305350576770924
BTC-USD.CC Exposure
-0.006493390417383683
CPER.US Exposure
0.0017641101703073834
VIX.INDX Exposure
-0.016890866717259093
UUP.US Exposure
0.0012089670394969105
TIP.US Exposure
0.18017785635281117
Idiosyncratic Exposure
0.10994649129516323
Value Score
50
Growth Score
50
Profit Score
37.5
Health Score
23.6
Yield Score
26.8
Moat Score
40

Factor Risk Decomposition

Share of annualised volatility attributable to each macro factor.

Total Est. Vol
112.7%

FundamentalsiCompany financial health metrics: P/E valuation, dividend yield, Piotroski F-Score (9-point profitability signal), Altman Z-Score (bankruptcy risk proxy), and a radar chart across 6 fundamental dimensions. Note: ETFs may show N/A for some metrics.

Fundamental Dimensions

Core Valuation

P/E Ratio (TTM)P/E RatioPrice-to-Earnings ratio — the market price of a stock divided by its earnings per share, a key valuation measure.Click for full definition →N/A
Dividend YieldDividend YieldAnnual dividend paid per share divided by the current share price — expressed as a percentage income return.Click for full definition →2.23%
Market Cap$1.6T
Piotroski F-ScorePiotroski F-ScoreA 9-point scoring system evaluating a company's financial strength across profitability, leverage, and operating efficiency.Click for full definition →
9-point profitability signal
0.0/ 9
Weak
Altman Z-ScoreAltman Z-ScoreA bankruptcy prediction model that combines 5 financial ratios into a single score indicating financial distress risk.Click for full definition →
Bankruptcy risk proxy
1.18
Distress Zone
Income Simulation

Based on $10,000 initial investment.

Total Income Generated
$0
Avg Yield on Cost
0.00%

Momentum & MacroiPrice momentum indicators relative to key technical levels: distance from 50-Day SMA (intermediate trend), 200-Day SMA (long-term trend), 52-Week High (bullish proximity), and Beta (market sensitivity coefficient).

vs 50-Day SMAMoving Averages (SMA)A rolling average of an asset's price over a defined window — used to identify trends and momentum signals.Click for full definition →
+12.0%
Above/below 50-day moving average
vs 200-Day SMAMoving Averages (SMA)A rolling average of an asset's price over a defined window — used to identify trends and momentum signals.Click for full definition →
-11.3%
Above/below 200-day moving average
vs 52-Week High52-Week HighThe highest price an asset reached in the past 52 weeks — a key reference for momentum and valuation context.Click for full definition →
36.5% from high
Distance from 52-week high
BetaBetaA measure of an asset's sensitivity to broad market movements relative to a benchmark (e.g. S&P 500).Click for full definition →
3.32
Market sensitivity coefficient

Frequently Asked Questions & Methodology

Is GraniteShares 1.5x Long Meta Daily ETF a high-risk investment?

GraniteShares 1.5x Long Meta Daily ETF (FBL.US) has an annualized volatility of 112.7% and experienced a maximum drawdown of 52.4% over the last 10 years. Its primary macro risk driver is SHV.US.

What is the 10-year return of FBL.US?

Over the past 10 years, FBL.US has generated a Compound Annual Growth Rate (CAGR) of 94.8%. It has had a positive return in 75% of the years measured.

Data Methodology & Trust

The risk and return information on this page is pre-calculated mathematically using daily market data spanning a 10-year period. Fundamentals (such as P/E Ratio, Market Cap, and Dividend Yield) represent trailing averages and may not immediately reflect real-time live market fluctuations. Advanced scoring models like the Piotroski F-Score and Altman Z-Score are proxies applied to publicly available trailing-twelve-month financial statements and may not account for recent off-balance-sheet events, qualitative company shifts, or sector-specific capital structures. Macroeconomic factor exposures are estimated via multivariate regression against standard market indices. This data is provided for quantitative insight and backtesting research, and should not be misconstrued as tailored financial advice.

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