Amundi MSCI China Tech UCITS ETF EUR (CC1.PA)

10-Year Study

CC1.PA · FR · ETF

About Amundi MSCI China Tech UCITS ETF EUR (CC1.PA)

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Source: EODHD Financial Datasets
Fundamentals updated: Jul 31, 2026

Fundamental Snapshot

Amundi MSCI China Tech UCITS ETF EUR (CC1.PA) charges an annual expense ratio of low annual fee, manages approximately $210.8M in net assets, and maintains a portfolio of 7 holdings. At the portfolio level, its underlying basket trades at 1.35x sales and 2.46x book value.

Executive Summary: Amundi MSCI China Tech UCITS ETF EUR has compounded at 1.7% annually over the last 10 years, with a maximum drawdown of 40.5% and an annualized volatility of 27.1%.

1Y CAGRCAGRCompound Annual Growth Rate — the annualized rate of return over a period, accounting for compounding.Click for full definition →
-4.4%
3Y CAGRCAGRCompound Annual Growth Rate — the annualized rate of return over a period, accounting for compounding.Click for full definition →
+5.3%
5Y CAGRCAGRCompound Annual Growth Rate — the annualized rate of return over a period, accounting for compounding.Click for full definition →
+1.2%
10Y CAGRCAGRCompound Annual Growth Rate — the annualized rate of return over a period, accounting for compounding.Click for full definition →
+1.7%

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

View full price history data
DateValue
2016-07-01$10,000
2016-08-01$10,622
2016-09-01$10,758
2016-10-01$10,770
2016-11-01$11,568
2016-12-01$10,966
2017-01-01$11,190
2017-02-01$12,040
2017-03-01$11,919
2017-04-01$11,601
2017-05-01$11,569
2017-06-01$11,356
2017-07-01$11,648
2017-08-01$12,027
2017-09-01$11,788
2017-10-01$12,601
2017-11-01$12,291
2017-12-01$12,458
2018-01-01$13,931
2018-02-01$12,830
2018-03-01$12,586
2018-04-01$12,941
2018-05-01$12,943
2018-06-01$11,922
2018-07-01$12,221
2018-08-01$11,933
2018-09-01$12,268
2018-10-01$11,557
2018-11-01$12,014
2018-12-01$11,368
2019-01-01$12,438
2019-02-01$12,874
2019-03-01$12,902
2019-04-01$13,091
2019-05-01$12,009
2019-06-01$12,461
2019-07-01$12,424
2019-08-01$11,809
2019-09-01$12,074
2019-10-01$12,106
2019-11-01$12,182
2019-12-01$12,840
2020-01-01$11,615
2020-02-01$12,030
2020-03-01$11,299
2020-04-01$11,603
2020-05-01$10,842
2020-06-01$10,900
2020-07-01$10,814
2020-08-01$10,521
2020-09-01$10,258
2020-10-01$10,625
2020-11-01$11,693
2020-12-01$11,580
2021-01-01$11,970
2021-02-01$12,323
2021-03-01$12,692
2021-04-01$12,108
2021-05-01$12,468
2021-06-01$12,484
2021-07-01$11,839
2021-08-01$12,113
2021-09-01$11,998
2021-10-01$12,118
2021-11-01$11,933
2021-12-01$12,150
2022-01-01$12,662
2022-02-01$12,609
2022-03-01$12,150
2022-04-01$12,369
2022-05-01$12,503
2022-06-01$13,105
2022-07-01$12,501
2022-08-01$12,307
2022-09-01$11,276
2022-10-01$10,044
2022-11-01$11,956
2022-12-01$11,742
2023-01-01$12,634
2023-02-01$11,940
2023-03-01$12,044
2023-04-01$12,404
2023-05-01$11,811
2023-06-01$12,334
2023-07-01$12,858
2023-08-01$11,479
2023-09-01$10,992
2023-10-01$10,328
2023-11-01$10,131
2023-12-01$10,009
2024-01-01$8,294
2024-02-01$9,236
2024-03-01$9,536
2024-04-01$9,779
2024-05-01$9,636
2024-06-01$8,977
2024-07-01$8,823
2024-08-01$8,418
2024-09-01$10,722
2024-10-01$10,784
2024-11-01$11,024
2024-12-01$10,814
2025-01-01$11,193
2025-02-01$12,180
2025-03-01$11,313
2025-04-01$10,224
2025-05-01$10,367
2025-06-01$10,669
2025-07-01$11,666
2025-08-01$13,241
2025-09-01$14,724
2025-10-01$14,491
2025-11-01$13,564
2025-12-01$13,437
2026-01-01$13,707
2026-02-01$13,548
2026-03-01$12,794
2026-04-01$13,562
2026-05-01$13,661
2026-06-01$13,183
2026-07-01$12,706
Max DrawdownMax DrawdownThe largest peak-to-trough decline in the asset's value over the measurement period.Click for full definition →
40.5%
Sharpe RatioSharpe RatioRisk-adjusted return: how much excess return you earn per unit of total risk (volatility).Click for full definition →
0.00
Sortino RatioSortino RatioLike Sharpe, but only penalizes downside volatility — a more accurate risk measure for asymmetric return distributions.Click for full definition →
0.01
Ann. VolatilityAnnualized VolatilityThe annualized standard deviation of an asset's returns — a measure of how much prices fluctuate.Click for full definition →
20.9%
Best YearBest & Worst YearThe single calendar year with the highest and lowest return in the measured period.Click for full definition →
2025 · +24.3%
Worst YearBest & Worst YearThe single calendar year with the highest and lowest return in the measured period.Click for full definition →
2023 · -14.8%
% Positive Years% Positive YearsThe percentage of calendar years in the measurement period where the asset delivered a positive return.Click for full definition →
50%

Annual Returns

View full annual returns data
YearReturn
201713.6%
2018-8.7%
201912.9%
2020-9.8%
20214.9%
2022-3.4%
2023-14.8%
20248.0%
202524.3%
2026-5.4%

Rolling 12-Month Returns

Rolling 12-Month Annualised Volatility

Historical Drawdowns

Monthly Returns

Monthly Returns Heatmap

YearJanFebMarAprMayJunJulAugSepOctNovDecAnn.
20262.0-1.2-5.66.00.7-3.5-3.6-5.4%
20253.58.8-7.1-9.61.42.99.313.511.2-1.6-6.4-0.924.3%
2024-17.111.43.22.5-1.5-6.8-1.7-4.627.40.62.2-1.98.0%
20237.6-5.50.93.0-4.84.44.2-10.7-4.2-6.0-1.9-1.2-14.8%
20224.2-0.4-3.61.81.14.8-4.6-1.6-8.4-10.919.0-1.8-3.4%
20213.42.93.0-4.63.00.1-5.22.3-1.01.0-1.51.84.9%
2020-9.53.6-6.12.7-6.60.5-0.8-2.7-2.53.610.1-1.0-9.8%
20199.43.50.21.5-8.33.8-0.3-5.02.20.30.65.412.9%
201811.8-7.9-1.92.80.0-7.92.5-2.42.8-5.84.0-5.4-8.7%
20172.07.6-1.0-2.7-0.3-1.82.63.3-2.06.9-2.51.413.6%
20166.21.30.17.4-5.29.7%

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.

Factor Risk Decomposition

Share of annualised volatility attributable to each macro factor.

Total Est. Vol
27.1%
View full factor risk breakdown
FactorRisk Exposure
VTI.US-5.7%
VEA.US-4.9%
VWO.US70.4%
QQQ.US1.1%
VTV.US9.8%
IJR.US-2.7%
QUAL.US-2.0%
SHV.US2.1%
TLT.US0.4%
LQD.US0.1%
HYG.US0.4%
GLD.US-0.8%
USO.US0.3%
VNQ.US-0.6%
BTC-USD.CC2.0%
CPER.US-2.5%
VIX.INDX1.5%
UUP.US1.7%
TIP.US8.2%
Idiosyncratic21.3%

Amundi MSCI China Tech UCITS ETF EUR ETF Profile & Portfolio Fundamentals

Detailed fund structure, fee metrics, portfolio-level valuation, and asset distribution statistics.

Fund Structure & Fees

Fund Size vs Peers
24th pct
larger than 24% of 1,446 ETFs we track
Holdings Count7
Distribution Yield
0.0%
10th pct of 1340 ETFs · median 1.9%

Portfolio Valuation Multiples

Portfolio P/E Ratio
Portfolio Forward P/E17.99x
Portfolio Price-to-Sales1.35x
Portfolio Price-to-Book2.46x

Market Sentiment & Squeeze Risk

Short Squeeze RiskLow

Dividend & Income Analysisi10-Year historical income simulation on a $10,000 initial investment, cumulative dividend income generated, average yield on cost, and annual payout table.

Income Simulation

Based on $10,000 initial investment.

Total Income Generated
$0
Avg Yield on Cost
0.00%

Momentum & MacroiPrice momentum indicators: distance from 50/200-Day SMA, 52-Week High proximity, Golden Cross trend signal, RSI momentum gauge, Fibonacci retracement levels, and Beta (market sensitivity).

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 →
-2.8%
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 →
-6.1%
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 →
15.5% 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 →
0.33
Market sensitivity coefficient
Trend SignalGolden Cross & Death CrossTechnical chart patterns that occur when a short-term moving average crosses over a long-term moving average.Click for full definition →
✦ Death Cross
Bearish — 50 SMA below 200 SMA
RSI (14-Day)Relative Strength Index (RSI)A momentum oscillator that measures the speed and change of price movements to identify overbought or oversold conditions.Click for full definition →
55
OversoldNeutralOverbought
Neutral
Fibonacci LevelsFibonacci RetracementTechnical levels based on mathematical ratios that indicate potential support and resistance areas.Click for full definition →
38.2% retracement-7.3%
50.0% retracement-4.5%
61.8% retracement-1.4%
% distance of current price from each 52-week Fibonacci support level.

In-Depth Analysis

CC1.PA — 10-Year Return & Risk Profile

Amundi MSCI China Tech UCITS ETF EUR (CC1.PA) has delivered near-flat annualized growth of 1.7% over the last 10 years. A $10,000 investment at the start of the period would have grown to approximately $11,845, representing a total return of 18%. Over this period, CC1.PA generated positive annual returns in 5 out of 10 calendar years (50%).

The best single calendar year for CC1.PA was 2025, with a return of +24.3%. The worst year was 2023, when the asset declined 14.8%. This spread between best and worst year is a useful indicator of the range of outcomes an investor might have experienced in a given 12-month window.

The asset's Sharpe ratio of 0.00 is considered poor on a risk-adjusted basis. The Sharpe ratio measures return earned above the risk-free rate per unit of total volatility — a higher reading indicates more efficient return generation relative to the risk taken. Investors focused on risk-adjusted outcomes should weigh this figure alongside absolute CAGR when making allocation decisions.

CC1.PA — Drawdown, Volatility & Downside Risk

CC1.PA's annualized volatility of 20.9% is classified as elevated relative to the long-run US equity benchmark of approximately 15%. This above-average volatility means investors in CC1.PA have historically experienced larger day-to-day price swings than the broader market, which requires a higher tolerance for short-term portfolio fluctuations.

The asset's maximum peak-to-trough decline over the study period was 40.5% — a severe bear-market collapse. Drawdown magnitude is a critical consideration for investors who may need to liquidate positions during market stress, as a larger decline requires proportionally greater subsequent gains to recover to the prior peak. A 40% drawdown, for example, requires a 68% gain just to break even.

When evaluating CC1.PA for inclusion in a diversified US portfolio, it is important to note that historical volatility and drawdown metrics are backward-looking. They capture the risk environment of the past 10 years, which included the COVID-19 market crash (2020), the 2022 Federal Reserve rate hike cycle, and various geopolitical disruptions. Future risk may differ materially, particularly in response to structural changes in US monetary policy, sector regulation, or macroeconomic regime shifts.

CC1.PA — Macroeconomic Factor Risk Exposure

The macroeconomic factor model attributes 70.4% of CC1.PA's return variance to Emerging Market Equities. This means that when Emerging Market Equities rises or falls sharply, CC1.PA tends to move in the same direction with meaningful magnitude. Investors who already hold significant exposure to this factor — through other funds or direct equity positions — should be aware of this concentration when sizing their CC1.PA allocation.

The second-largest macro driver is US Value Equities, contributing 9.8% of variance. 21.3% of CC1.PA's risk is attributable to idiosyncratic, stock-specific factors that are uncorrelated with the broader macro drivers. A higher idiosyncratic share generally indicates that the fund's performance is more dependent on the security selection or holdings composition of the individual underlying assets, rather than broad market forces.

For US investors building a diversified multi-asset portfolio, understanding CC1.PA's factor exposures helps assess its marginal contribution to overall portfolio risk. Adding CC1.PA alongside assets with low correlation to Emerging Market Equities — such as US Treasury bonds, commodities, or assets with significant developed-market ex-US exposure — can reduce the overall portfolio's sensitivity to any single macroeconomic theme.

Compare this AssetiRun a head-to-head backtest and risk analysis against similar assets.

Frequently Asked Questions & Methodology

Is Amundi MSCI China Tech UCITS ETF EUR a high-risk investment?

Amundi MSCI China Tech UCITS ETF EUR (CC1.PA) has an annualized volatility of 27.1% and experienced a maximum drawdown of 40.5% over the last 10 years. Its primary macro risk driver is VWO.US.

What is the 10-year return of CC1.PA?

Over the past 10 years, CC1.PA has generated a Compound Annual Growth Rate (CAGR) of 1.7%. A $10,000 investment would have grown to approximately $11,845. It has had a positive return in 50% of calendar years.

What is CC1.PA's Sharpe ratio?

CC1.PA has a Sharpe ratio of 0.00 and a Sortino ratio of 0.01 over the 10-year period. The Sharpe ratio measures risk-adjusted return — how much excess return is earned per unit of volatility. A reading below 1.0 suggests investors were not fully compensated on a risk-adjusted basis.

What is CC1.PA's dividend yield?

CC1.PA does not pay a meaningful dividend. Its returns are driven primarily by price appreciation. Investors seeking regular income may wish to consider dividend-focused alternatives.

Is CC1.PA above its 200-day moving average?

CC1.PA is currently below its 200-day moving average by 6.1%. The current trend signal is: Bearish — 50 SMA below 200 SMA. The 200-day SMA is a widely used long-term trend filter — assets trading above it tend to exhibit positive price momentum.

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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