Methodology & Data Transparency
How we calculate every score, projection, and comparison on My ETF Journey. Full algorithmic transparency so you can verify our work.
We believe investors deserve to know exactly how the numbers on this site are produced. Every score, projection, and comparison on My ETF Journey is generated using documented, reproducible formulas applied to publicly available data. This page provides a complete breakdown of each calculation so that you can audit, replicate, and critique our approach.
No black boxes. No proprietary magic. If you find an error or have a suggestion for improving our methodology, we welcome the feedback.
1. Beginner Suitability Score (1–10)
The Beginner Suitability Score is a composite metric designed to answer one question: how appropriate is this ETF for someone who is just getting started? It is not a performance prediction. It evaluates structural characteristics that research and practitioner consensus associate with lower-risk, lower-cost, easier-to-hold funds.
The score is the sum of five independently scored components, each with a defined maximum. The theoretical range is 0–10, though most ETFs in our dataset score between 3 and 9.
Component 1: Expense Ratio (max 3 points)
Expense ratio receives the highest weight because fees are the single most reliable predictor of long-term investor outcomes. Lower fees leave more of your returns in your pocket.
| Expense Ratio | Points | Rating |
|---|---|---|
| ≤ 0.05% | 3.0 | Excellent |
| ≤ 0.10% | 2.5 | Very Good |
| ≤ 0.20% | 2.0 | Good |
| ≤ 0.50% | 1.0 | Fair |
| > 0.50% | 0.0 | Poor |
Component 2: Volatility / Beta (max 2 points)
Beta measures how much an ETF moves relative to the broad market. A beta of 1.0 means it moves in line with the market; below 1.0 indicates lower volatility, which is generally more suitable for beginners who may panic-sell during drawdowns.
| Beta | Points | Rating |
|---|---|---|
| ≤ 0.8 | 2.0 | Low Volatility |
| ≤ 1.0 | 1.5 | Market-Like |
| ≤ 1.2 | 1.0 | Moderate |
| > 1.2 | 0.5 | High Volatility |
Component 3: Diversification / Holdings Count (max 2 points)
A higher number of holdings generally means broader diversification, which reduces the impact of any single stock on the overall fund. For beginners, broad diversification provides a smoother ride and reduces the risk of catastrophic single-name losses.
| Number of Holdings | Points | Rating |
|---|---|---|
| ≥ 500 | 2.0 | Highly Diversified |
| ≥ 100 | 1.5 | Well Diversified |
| ≥ 50 | 1.0 | Moderate |
| < 50 | 0.5 | Concentrated |
Component 4: Dividend History (max 1 point)
A binary factor: ETFs that pay a dividend receive 1 point; those that do not receive 0 points. Dividends provide tangible cash flow that can reinforce positive investor behavior for beginners, even when prices are flat or declining.
| Dividend Yield | Points |
|---|---|
| > 0% | 1.0 |
| 0% | 0.0 |
Component 5: Track Record Length (max 2 points)
Longer-lived funds have demonstrated the ability to survive market cycles, maintain assets, and operate consistently. A longer track record also provides more data for evaluating historical performance and risk characteristics.
| Fund Age | Points | Rating |
|---|---|---|
| ≥ 10 years | 2.0 | Established |
| ≥ 5 years | 1.5 | Seasoned |
| ≥ 3 years | 1.0 | Developing |
| < 3 years | 0.5 | New |
Score Summary
| Component | Max Points | Weight |
|---|---|---|
| Expense Ratio | 3 | 30% |
| Volatility (Beta) | 2 | 20% |
| Diversification (Holdings) | 2 | 20% |
| Dividend History | 1 | 10% |
| Track Record | 2 | 20% |
| Total | 10 | 100% |
Example: An ETF with expense ratio 0.03% (3 pts) + beta 0.95 (1.5 pts) + 3,500 holdings (2 pts) + 1.8% dividend yield (1 pt) + 20-year track record (2 pts) = 9.5 / 10.
2. DCA Compound Growth Projections
Our Dollar Cost Averaging projections model the effect of making fixed monthly contributions that compound at a constant annual rate. We use the future value of an annuity due formula, which assumes contributions occur at the beginning of each period:
// Monthly rate
r = annual_rate / 12
// Total number of contributions
n = years × 12
// Future Value (annuity due)
FV = PMT × ((1 + r)n − 1) / r × (1 + r)
Where:
- PMT = monthly contribution amount
- r = monthly rate of return (annual rate / 12)
- n = total number of monthly contributions (years × 12)
- FV = projected future value of all contributions
Important assumptions: This model assumes a constant rate of return, which does not occur in real markets. It does not account for taxes, inflation, fund closure risk, or changes to your contribution amount. The projection is a mathematical illustration, not a guarantee or prediction of future results.
3. Fee Impact Calculator
The Fee Impact Calculator shows how an expense ratio erodes your balance over time by comparing two parallel growth paths: one with fees and one without.
// Growth without fees
balance_no_fee = FV(PMT, annual_rate, n)
// Growth with expense ratio deducted
effective_rate = annual_rate − expense_ratio
balance_with_fee = FV(PMT, effective_rate, n)
// Total fee drag
fee_impact = balance_no_fee − balance_with_fee
The expense ratio is subtracted from the annual return rate before compounding. This is a simplification; in practice, fund fees are deducted from net asset value daily, but the annual-deduction approach produces nearly identical results over long horizons and is easier to understand.
Example: Contributing $500/month for 30 years at 8% annual return: a 0.03% expense ratio costs roughly $4,800 in total fees, while a 0.75% expense ratio costs approximately $115,000. That is a 24x difference from a seemingly small fee change.
4. Holdings Overlap (Jaccard Similarity)
When comparing two ETFs, we calculate how much their portfolios overlap using the Jaccard similarity coefficient. This measures the proportion of shared holdings relative to the total number of unique holdings across both funds.
// A = set of ticker symbols in ETF 1
// B = set of ticker symbols in ETF 2
Overlap = |A ∩ B| / |A ∪ B| × 100%
Where:
- A ∩ B = tickers that appear in both ETFs (intersection)
- A ∪ B = all unique tickers across both ETFs (union)
The calculation uses the top holdings by weight for each fund. A higher overlap percentage means the two ETFs hold many of the same stocks, which reduces the diversification benefit of holding both. A lower overlap indicates more complementary exposure.
| Overlap Range | Interpretation |
|---|---|
| 70–100% | High overlap — limited diversification benefit from holding both |
| 30–70% | Moderate overlap — some shared exposure but meaningful differences |
| 0–30% | Low overlap — strong diversification benefit from combining these funds |
5. Data Sources & Update Frequency
Our dataset covers more than 800 U.S.-listed ETFs spanning broad market, sector, bond, international, commodity, and thematic categories.
Data Points Collected
- Fund name, ticker symbol, and issuer
- Expense ratio (net)
- Assets under management (AUM)
- Number of holdings
- Top holdings by weight with ticker symbols
- Dividend yield (trailing twelve months)
- Inception date and fund age
- Beta (3-year, relative to S&P 500)
- Historical returns (1-year, 3-year, 5-year, 10-year)
- Investment category and sector classification
Sources
All data is sourced from publicly available materials including SEC filings (N-PORT, N-CSR), official fund-provider fact sheets and prospectuses, and publicly available financial data aggregators. We do not use proprietary or paywalled data feeds.
Update Frequency
This is a static dataset that is reviewed and updated periodically. Expense ratios, holdings, and performance figures may lag real-time values. We clearly note the dataset date on relevant pages. For real-time quotes and live portfolio data, consult your brokerage platform or an official financial data provider.
Known limitations: Our static dataset may not capture very recently launched ETFs, recent fee changes, or intraday holdings updates. Bond ETF holdings data can be less complete than equity ETF data due to the nature of fixed-income portfolio reporting.
6. Disclaimer
All content on My ETF Journey is for educational and informational purposes only. Nothing on this site constitutes financial advice, investment advice, tax advice, or a recommendation to buy, sell, or hold any security.
Past performance does not guarantee future results. The scores, projections, and calculations presented here are based on historical data and mathematical models that involve simplifying assumptions. Actual investment outcomes depend on factors including market conditions, timing, taxes, and individual circumstances that our models do not capture.
The Beginner Suitability Score reflects structural characteristics of an ETF and is not a buy or sell signal. A high score does not mean a fund will perform well; a low score does not mean it will perform poorly.
Always conduct your own research and consult with a licensed financial advisor before making any investment decisions. You are solely responsible for your own investment choices and their outcomes.
See These Tools in Action
Frequently Asked Questions
How is the Beginner Suitability Score calculated?
The Beginner Suitability Score is a composite 1-10 rating based on five weighted factors: expense ratio (up to 3 points), volatility measured by beta (up to 2 points), diversification measured by number of holdings (up to 2 points), dividend history (up to 1 point), and track record length (up to 2 points). Each factor is scored independently and the points are summed to produce the final score.
Where does My ETF Journey get its ETF data?
All ETF data is sourced from publicly available regulatory filings, fund prospectuses, and official fund-provider fact sheets. Our dataset covers over 800 U.S.-listed ETFs and is reviewed and updated periodically to reflect changes in fund characteristics, expense ratios, and holdings.
How accurate are the DCA compound growth projections?
The DCA projections use the standard future-value-of-annuity-due formula to model monthly contributions compounded at a fixed annual rate. These projections illustrate the mathematical effect of consistent investing over time. They assume a constant rate of return and do not account for market volatility, taxes, inflation, or changes in contribution amounts. Actual results will vary.
What does the Holdings Overlap percentage mean?
Holdings Overlap uses the Jaccard similarity coefficient, which measures the size of the intersection divided by the size of the union of two sets of holdings tickers. A 70% overlap means that 70% of the combined unique holdings appear in both ETFs. This helps investors avoid unintentional concentration when holding multiple funds.