GLDA vs. 4GLD - ETF Comparison
GLDA - Amundi Physical Gold ETC (C)
The Amundi Physical Gold ETC (C) tracks the spot price of gold in US Dollar, providing investors with a physical gold-backed collateralised debt obligation. With a low expense ratio of 0.12% p.a., this large ETC offers a cost-effective way to invest in precious metals, specifically gold.
4GLD - Xetra-Gold
The Xetra-Gold ETF tracks the spot price of gold in US Dollar, providing investors with a cost-effective way to gain exposure to the precious metal. With a low expense ratio, it is an attractive option for those seeking to diversify their portfolios with a physical gold holding.
| GLDA | 4GLD | |
|---|---|---|
| Fund Name | Amundi Physical Gold ETC (C) | Xetra-Gold |
| Fund Provider | Amundi | Deutsche Boerse |
| Index | Gold | Gold |
| Asset Class | Commodity | Commodity |
| Listing | EU-listed | EU-listed |
| Expense Ratio | 0.12% | nan% |
| Inception Date | 2019-05-21 | 2007-11-27 |
| Currency | USD | EUR |
| Distribution Policy | Accumulating | Accumulating |
| Leveraged | Non-leveraged | Non-leveraged |
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Key Metrics
Performance Metrics
Risk Metrics
Detailed Returns
Benchmark Comparison
Key Metrics
Performance Metrics
Risk Metrics
Detailed Returns
Benchmark Comparison
Performance Analysis
The performance analysis examines historical data to assess the returns of the investment strategy, including key metrics such as Cumulative returns, End of Year (EoY) returns, and risk-adjusted returns like the Sharpe ratio or the Sortino ratio.
Cumulative Returns
End of Year Returns Table
End of Year Returns
Risk Analysis
The risk analysis refers to an assessment of potential negative events that could lead to a loss of capital. Conducting a risk analysis can help in deciding whether an investment should be made. This is done using risk metrics such as drawdowns, volatility and beta which reflect stakeholders' confidence in the consistency of an investment strategy.
Drawdowns
Drawdowns Table
Monte Carlo Simulation
The Monte Carlo simulation is a statistical method used to forecast portfolio returns by generating a wide range of potential outcomes through random sampling from historical asset price data. It helps investors assess the potential risk and return of a portfolio under various market conditions. The simulation takes into account the initial investment and optionally simulates cash flow scenarios like fixed contributions, fixed withdrawals, or percentage withdrawals.
IMPORTANT: The forecast generated through Monte Carlo simulations is purely hypothetical and does not guarantee future returns. Investment decisions should be made with consideration of various factors, and past performance is not indicative of future results.