Most screeners find noise, not value. ROE and debt-to-equity filters combined with sector context uncover specific, concentrated bets — not the broad winners that every retail investor already knows. The Finviz screener returns over 200 results when you filter for ROE above 15% and debt-to-equity below 0.5. That list is useless without further context. Here is how to combine filters that actually separate the ten or twelve names worth owning from the hundreds that merely pass a threshold.
Why ROE alone misses 70% of real value opportunities
Return on equity is a popular metric because it is simple: net income divided by shareholders' equity. The problem is that ROE can be inflated by leverage. A company with 40% ROE and a debt-to-equity ratio of 3.0 is not creating value — it is leveraging risk. The Finviz screener does not distinguish between these cases by default.
When you add a debt-to-equity ceiling of 0.5, the Finviz result set drops from 200+ names to roughly 60. That single filter removes the majority of leveraged financials, REITs, and capital-intensive industrials that inflate the surface-level list. But 60 is still too many. The next layer is sector context: not every sector rewards low leverage equally.
In consumer staples, a debt-to-equity of 0.4 is relatively aggressive — these companies typically run at 0.15–0.25 because their cash flows are predictable enough to avoid borrowing. In technology, 0.4 is conservative because high-growth firms often take on debt to accelerate R&D. The same number means different things in different sectors.
The two-filter framework that reduces noise by 85%
An analyst reviewing detailed financial metrics — the kind of granular work that automated screens cannot fully replicate. — Photo by RDNE Stock project on Pexels
Start with ROE above 15% and debt-to-equity below 0.5. Then layer the following:
-
Free cash flow yield above 5%. This eliminates companies that report high earnings but do not convert them to cash. Accounting earnings can be manipulated through depreciation schedules, revenue recognition timing, and one-time gains. Free cash flow is harder to fake.
-
Three-year revenue CAGR above 3%. This removes declining businesses that look cheap because the market has correctly priced in contraction. A company with 18% ROE and -2% annual revenue growth is returning capital through buybacks while the underlying business shrinks.
-
Market cap above $1 billion. Micro-caps can pass deep value filters but lack the liquidity and analyst coverage to provide price discovery.
After applying all four filters (ROE, debt, FCF yield, revenue growth), the typical result set drops from 200+ to roughly 15–25 names.
Sector-specific calibration
| Sector | Typical ROE Range | Debt/Equity Norm | Deep Value Signal |
|---|---|---|---|
| Technology | 15–35% | 0.1–0.5 | ROE above 20% with D/E below 0.3 |
| Consumer Staples | 12–25% | 0.15–0.4 | ROE above 15% with D/E below 0.25 |
| Healthcare | 10–30% | 0.2–0.8 | ROE above 18% with D/E below 0.4 |
| Industrials | 12–22% | 0.3–1.0 | ROE above 16% with D/E below 0.5 |
| Financials | 10–18% | N/A (leverage-based) | Price-to-tangible-book below 1.2 |
Financials break the framework entirely. Banks and insurers operate with leverage as a core business model. For financials, replace the filter set with price-to-tangible-book below 1.2 and return on tangible equity above 12%.
The failure mode: when deep value screens find traps
The manual review step that separates deep value discovery from mechanical screening. — Photo by RDNE Stock project on Pexels
Deep value screens fail in three predictable ways:
Cyclical peaks: Companies at the top of their earnings cycle show inflated ROE. A mining company with 25% ROE during a commodity supercycle reverts to 8–10% when prices normalize.
Accounting distortions: Share buybacks reduce equity, which mechanically increases ROE without improving profitability. A company borrowing to buy back shares can show 30% ROE while destroying bondholder value.
Structural decline with lagging financials: Some businesses report strong trailing metrics while their competitive position erodes. The numbers were real; the business model was dying.
The fix: overlay qualitative judgment. No screen replaces reading the 10-K and understanding industry dynamics. The screen generates candidates. The analysis makes the decision.
How FinTara handles this automatically
FinTara's stock screener applies the ROE + debt filter combination by default and adds free cash flow yield and revenue growth as secondary filters. The sector-adjusted thresholds are built into the scoring model, so a technology company with 22% ROE and 0.25 D/E scores differently from a consumer staples company with identical numbers. This eliminates the sector-blindness problem that makes raw Finviz screens unreliable for concentrated, deep-value bets.