What Is a Conviction Score? How StockSmarty Ranks Dividend Stocks

There are thousands of dividend-paying stocks across global markets. How do you decide which ones to hold? You could read analyst reports, screen by yield, follow financial media, or just buy whatever Reddit recommends this week. The problem with all of these approaches is that they rely on a single source of opinion. One analyst might love a stock that another one hates. High yield might signal a great opportunity — or a company in trouble. StockSmarty takes a fundamentally different approach: instead of relying on any single opinion, it looks at what the world's biggest ETF managers have collectively decided to hold.

When you select multiple ETFs in StockSmarty, the system analyses every holding across all of them. Each stock gets a conviction score based on two factors: 1. Frequency — how many of your selected ETFs hold this stock? A stock held by 4 out of 5 ETFs has higher frequency than one held by 2 out of 5. 2. Weight — how heavily is the stock weighted in each ETF? Being a top-10 holding in an ETF counts for more than being position #487. The conviction score combines these factors. A stock that appears in every ETF you selected with significant weighting gets the highest possible score. A stock that appears in only one ETF with a tiny position gets a low score.

StockSmarty offers two scoring methods: Method A — Conservative (Recommended): This method normalises conviction across all your ETFs. It penalises stocks that appear in fewer ETFs, even if their weight is high in the ones that do hold them. This produces a portfolio of stocks where the broadest consensus exists. Method B — Balanced 60/40: This gives a 60% weight to frequency and 40% to the average holding strength. It's slightly more generous to stocks that are heavily weighted in fewer ETFs. Most users choose Method A because the whole point of cross-referencing ETFs is to find consensus. If only 1 out of 5 funds holds a stock, the consensus simply isn't there regardless of how large the position is.

Once every stock has a conviction score, StockSmarty converts scores into portfolio allocation percentages. Higher-scored stocks get larger allocations. The algorithm ensures: • Every portfolio stock gets at least 1% allocation — no stock is included as a negligible position • Allocations always sum to exactly 100% — ready to copy directly into your broker • Score-proportional weighting — a stock with double the score gets roughly double the allocation • The largest remainder method handles rounding — guarantees whole-number percentages that sum perfectly You can also manually adjust any stock's allocation or lock it at a specific percentage. When you do, the remaining stocks are automatically rebalanced around your fixed positions.

In practice, the highest-conviction dividend stocks tend to be large, well-established companies with decades of dividend payment history. Names like Johnson & Johnson, Procter & Gamble, Coca-Cola, JPMorgan Chase, and Chevron routinely score highly because they appear across many different types of ETFs — growth funds, value funds, dividend funds, and broad market funds. This makes intuitive sense. When professional fund managers running billions of dollars independently agree that a company belongs in their portfolio, that's a signal worth paying attention to. Not a guarantee — nothing is — but a data-driven starting point that's harder to argue with than any single analyst's opinion.

Select any combination of up to 10 ETFs from over 3,681 available across 6 global exchanges. StockSmarty will cross-reference every holding, score each stock, and produce a ready-made portfolio with exact allocation percentages. You can adjust settings like minimum dividend yield, scoring method, and the number of top holdings to analyse per ETF. The AI analysis feature will then review your portfolio and provide a detailed breakdown of strengths, risks, and suggestions. Your first 30 days include 3 free analyses, 3 rebalances, and 3 AI portfolio reviews. No credit card required.