Frequently Asked Questions
Everything you need to know about using the ETF Composition Comparison Tool
This is an ETF Composition Comparison Tool that helps you analyze and compare the holdings of different ETFs. It's particularly useful for investors who want to understand portfolio overlap, avoid over-concentration in specific companies, and make more informed investment decisions. The tool calculates overlap scores between ETFs, measures diversification through entropy analysis, and helps you identify which companies appear across multiple ETFs in your portfolio. This is especially valuable when building a diversified portfolio or when you want to ensure you're not unknowingly overexposed to certain stocks through multiple ETF positions.
The typical process for using this tool involves these steps:
- Find ETF composition data: Visit the official website of the ETF issuer and download the holdings data file, usually available as CSV or Excel format in the fund details section.
 - Upload the file: Use the upload page to select and upload your ETF composition file. The tool will automatically parse the data and extract company names, weights, and other relevant information.
 
You can repeat this process for multiple ETFs to build a comprehensive view of your entire ETF portfolio and identify any potential overlaps or concentration risks.
You can upload CSV or Excel files containing ETF holdings data. These files should include company names, ISINs (optional), ticker symbols (optional), and weight percentages for each holding.
ETF composition data is available on the official websites of ETF providers. Each provider typically offers CSV or Excel downloads of their ETF holdings in the fund details or documentation sections.
Your data is stored locally in your browser using IndexedDB. This means your files never leave your computer and are not uploaded to any servers. Your data remains completely private and secure on your device.
Yes, absolutely! Your data never leaves your computer. It's stored locally in your browser and is not transmitted to any external servers.
This can happen if you switch browsers, clear your browser data/cache, or use private/incognito mode. Since data is stored locally in your browser, it's only available in the same browser where you uploaded it.
The application supports CSV and Excel (.xlsx) files. Make sure your files have the required columns: company name, weight percentage, and optionally ISIN and ticker symbol.
Check that your file has the required columns (company name and weight percentage). Make sure the file isn't corrupted and is in a supported format. ETF provider files often contain additional information beyond the holdings table, so you may need to clean your data by removing headers, footers, and other non-tabular content before uploading. If you're still having issues, try saving your Excel file as CSV format.
Currently, you need to upload files one at a time. After uploading each file, you can add more files by using the upload page again. All your uploaded ETFs will be available in the portfolio section.
If you want to update the holdings data for an ETF you've already uploaded, simply upload a new file with the same ISIN. The application will automatically detect that the ISIN already exists and replace the old data with the updated composition. This makes it easy to keep your portfolio up to date with the latest ETF holdings.
The overlap score measures how much two ETFs overlap in their holdings. It's calculated by taking the minimum weight of each common holding between the two ETFs and summing all these minimum values. For example, if ETF A has 5% in Apple and ETF B has 3% in Apple, the overlap contribution is 3% (the minimum). The final score is the sum of all such minimum values, expressed as a percentage. A score of 0% means no overlap, while 100% would mean identical portfolios.
Entropy measures the diversification level of an ETF's holdings using Shannon entropy. It's calculated as -sum(p_i × ln(p_i)) where p_i is the weight of each holding. Higher entropy values indicate better diversification (more holdings with similar weights), while lower values indicate concentration (few dominant holdings). For example, an ETF with equal 5% weights across 20 holdings has maximum entropy, while an ETF with 90% in one stock has very low entropy.
The application uses intelligent matching algorithms to identify the same companies across different ETFs, even when names are slightly different. It handles variations in company names to provide accurate overlap analysis. However, the algorithm is not perfect and may occasionally miss matches or create false positives, so it's always good to review the results manually.