A high dividend yield can mean one of two things: a genuine value opportunity, or a company whose stock price has fallen because the market already expects a cut. Telling those apart from a yield number alone is impossible. It requires actually looking at the underlying business — and most free tools don't do that.
DividendGuard uses two independent tools to look under the hood. The Cut-Risk Scorecard estimates how likely a company is to cut, based on factors that were tested against history. The Trap Classifier describes why a company might be at risk, in plain terms. They are separate on purpose, and they answer different questions.
The scorecard is the part of DividendGuard we're most confident in, because we tested it. It sorts companies into four tiers — A and B (below-market cut risk), C (elevated), and D (high) — using four financial factors, each chosen because it kept working when tested on data the model had never seen.
In plain terms: in back-testing, companies the scorecard flagged in its highest-risk tier cut their dividends at roughly 1.7 times the rate of the market as a whole. That test was done out-of-time — the model was built only on data through 2019, then measured against 2020 and later, which it had never seen. That separation matters: it's the difference between a model that describes the past and one that has a real shot at the future. The median warning landed roughly 11 months before the cut.
We deliberately kept this list short. Several tempting factors were dropped because, when tested honestly, they described a cut that was already happening rather than predicting one coming — so they'd give a false sense of foresight.
As far as we're aware, no free dividend-safety tool publishes a back-test of its own accuracy at all. A score is easy to display; showing whether it actually worked, on data it hadn't seen, is the harder and more honest thing. We'd rather show a modest, real number than an impressive, unverifiable one.
Not equally. The numbers above are aggregate figures across our full coverage universe — but the scorecard's four factors don't work equally well everywhere. We measured performance sector by sector and are publishing the honest breakdown rather than the aggregate alone.
| Sector | Tickers covered | AUC | Tier D lift | Positive events measured | What this means |
|---|---|---|---|---|---|
| Consumer Defensive | 78 | 0.73 | 2.61× | 12 | Model works well |
| Consumer Cyclical | 178 | 0.69 | 1.68× | 27 | Model works well |
| Industrials | 255 | 0.68 | 2.07× | 22 | Model works well |
| Communication Services | 49 | 0.67 | 2.19× | 17 | Model works well — below our 30-event confidence bar, worth another look as more data accumulates |
| Basic Materials | 79 | 0.61 | 1.63× | 34 | Model works reasonably well |
| Real Estate | 206 | 0.62 | 1.13× | 45 | Weaker — treat the safest-tier grade with caution. Measured on a moderate, stable sample (45 cut events) |
| Financial Services | 532 | 0.56 | 1.34× | 71 | Weak — interest coverage and FCF coverage don't mean the same thing for banks and insurers that they mean for an industrial company. This is our best-supported "weak" reading of the three (71 cut events, comfortably above the confidence bar) |
| Energy | 113 | 0.55 | 1.16× | 27 | Weak — on a thinner sample than Financial Services or Real Estate. Measured weak, but with less certainty about exactly how weak |
| Technology | 120 | — | — | 7 | Rarely cuts in our data, but the sample isn't enough to prove it's actually safe |
| Healthcare | 66 | — | — | 8 | Low observed cut rate, but too small a sample either way to call this safe or risky |
| Utilities | 67 | — | — | 7 | Very low observed cut rate, but nowhere near enough events to know if that's real safety or luck |
Why this happened, in plain terms: our scorecard was built and tuned on a population weighted toward industrial and consumer-staples companies. Interest coverage, for a bank, is closer to describing the bank's core business than its financial stress. For a regulated utility or a pipeline, capital structure and payout dynamics work differently than for a typical industrial company. The same numeric ratio means different things in different sectors, and right now our model reads all of them on one scale.
What we're doing about it: we investigated a statistically-adjusted version of the scorecard for these sectors twice, with two different designs. Neither held up under proper cross-validation. We'd rather tell you that than ship something we can't stand behind.
Where the scorecard estimates how likely a cut is, the Trap Classifier explains why a company might be in trouble. It reads the financials across more than a dozen independent checks and sorts the result into one of a few plain-language verdicts:
The individual checks include leverage, payout sustainability, earnings quality, revenue and margin trends, capital-spending intensity, and how the dividend's growth is decelerating. Each is evaluated on its own, so a single strong number can't paper over a weak one. The classifier is descriptive: it's meant to give you the story behind a company's risk so you know what to investigate — not to produce a single probability.
Neither the scorecard nor the classifier uses an AI model at the moment you look up a stock. Both are fixed, auditable logic: the same inputs always produce the same output — no randomness, no model drift, no black box. A score means the same thing today as it did six months ago, and it can be traced back to the exact numbers that produced it.
The scorecard's factor weights were set once, offline, by fitting them to historical data — and then frozen into a fixed table. At the moment you use the app, nothing is being learned or predicted on the fly; it's a lookup. We do use AI elsewhere in the product — for example, to surface relevant news around a stock you're researching — but that layer is kept entirely separate, clearly labeled as a distinct signal, and never allowed to move the safety rating.
All fundamental data — revenue, earnings, cash flow, debt — is sourced from SEC EDGAR filings, the same primary-source data public companies are legally required to disclose, not third-party estimates. Prices are sourced separately and cross-validated for accuracy. Because the fundamentals come straight from filings, every factor in the scorecard traces back to a number a company reported to the SEC.
DividendGuard is free to use. There's no subscription — the product is supported by minimal, non-intrusive advertising instead.
Data may be incomplete or contain errors, and past patterns don't guarantee future results. Always do your own research before making investment decisions.