Quality Screening: The 24 Factors Behind the Tessera Rating

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title: "Quality Screening: The 24 Factors Behind the Tessera Rating" description: "Value traps are cheap stocks you shouldn't own. Quality screening filters them out. Here are the 24 factors Tessera grades every US stock on — and why each matters." publishedAt: "2026-02-25" updatedAt: "2026-02-25" keywords: ["stock quality screening", "quality factors", "fundamental analysis", "tessera rating", "value trap"]

TL;DR

• Cheap does not equal good. A stock trading at 8x earnings is a gift if the business is durable and a trap if the earnings are about to halve.
• Tessera grades every US stock on 24 quality factors across 4 buckets: profitability, financial health, growth, and earnings quality.
• Every factor is z-scored within its GICS sector, so a SaaS company is compared to SaaS peers and a utility to utilities.
• The bucket scores roll up into a single letter grade (A/B/C/D/F). A single F bucket is a hard floor — the whole stock grades F regardless of the other buckets.
• Only A and B grades are eligible for buy signals. C is hold-only. D and F are ineligible.

Why quality matters more than valuation

Quantitative value strategies have one consistent failure mode: the value trap. You screen for low P/E, low P/B, or high dividend yield, and you systematically end up long businesses whose earnings are in secular decline. The multiple looks cheap because the market is pricing in deterioration the screen can't see. When the next earnings cut arrives, the "cheap" stock re-rates lower — not because sentiment shifted, but because the denominator shrank.

The academic literature on this is not subtle. Piotroski's 2000 paper on the F-Score showed that within the cheapest quintile of book-to-market stocks, the ones with weak fundamentals underperformed the ones with strong fundamentals by roughly 7.5% annually over a twenty-year window. Similar work by Novy-Marx on gross profitability, Sloan on accruals, and Fama-French on the "quality minus junk" factor all land in the same place: valuation alone is a noisy signal, and quality is the filter that separates real discounts from dying businesses.

The practical implication for Tessera is that our sector-relative P/E ranking — the engine that actually surfaces candidates — is necessary but not sufficient. A stock trading at a 35% discount to its sector median is interesting. A stock trading at a 35% discount to its sector median that also has a collapsing ROIC, rising leverage, and deteriorating cash conversion is a falling knife. Quality screening is how we tell those two apart before capital gets allocated.

Think of quality as the floor, not the ceiling. It will not make you rich on its own — a high-quality business at 50x earnings is still a poor risk/reward. But it keeps you out of the cheap stocks you should not own, which is most of them.

The four buckets

Profitability (6 factors)

The question this bucket answers: is this business actually earning its cost of capital, and can it defend that?

• Return on Equity (ROE) — net income / shareholder equity. Baseline profitability measure.
• Return on Invested Capital (ROIC) — NOPAT / invested capital. The headline metric in the bucket.
• Gross margin stability — 3-year standard deviation of gross margin. Lower is better.
• Operating margin level — trailing operating margin, sector-adjusted.
• Free cash flow margin — FCF / revenue.
• ROIC − WACC spread — economic profit, not accounting profit.

Why ROIC matters more than ROE. ROE can be inflated by leverage: a business with mediocre underlying economics can show a 20% ROE by loading up debt. ROIC is leverage-neutral — it asks how productively the total capital base (debt plus equity) is deployed. When ROIC and ROE diverge sharply, that is almost always a leverage story, not a quality story.

Why gross margin stability matters. A business with pricing power passes input-cost shocks through to customers and maintains gross margin through the cycle. A business without pricing power absorbs shocks in margin. Three-year standard deviation of gross margin is a cheap, robust proxy for competitive moat — the more stable, the more durable the franchise.

Why FCF margin matters. Accrual earnings can be managed. Cash flow is harder to fake, though not impossible. A business with persistently high FCF margin is converting its reported profit into actual cash, which is the only kind of profit that funds dividends, buybacks, or reinvestment.

Financial health (6 factors)

The question this bucket answers: can the business survive a bad year without a capital raise, a covenant breach, or a restructuring?

• Debt-to-equity — total debt / shareholder equity.
• Interest coverage — EBIT / interest expense.
• Current ratio — current assets / current liabilities.
• Altman Z-score — bankruptcy prediction composite.
• Net debt / EBITDA — leverage normalized to earnings power.
• FCF to total debt — how many years of free cash flow to pay off all debt.

Why interest coverage matters. The single most useful question about a levered business is whether operating profit covers interest expense with room to spare. Coverage below 3x means a 30% EBIT decline — a plausible downturn — puts the business in covenant territory. Coverage above 8x means the balance sheet can absorb most cycles without drama.

Why the Altman Z-score matters. It is a crude, dated formula, but it works. Z-scores below 1.8 flag distress risk with meaningfully elevated bankruptcy rates in out-of-sample testing. It is not a prediction; it is a screen for names that deserve more scrutiny.

Why net debt / EBITDA matters. It is the only leverage measure that is scale-invariant and cycle-aware. Absolute debt tells you nothing. Debt-to-equity depends on accounting equity, which can be distorted by buybacks. Net-debt-to-EBITDA normalizes to current earnings power and is the metric credit analysts actually use.

Growth (6 factors)

The question this bucket answers: is this business growing, and is the growth coming from something durable?

• 3-year revenue CAGR — top-line growth.
• 3-year EPS CAGR — bottom-line growth per share.
• 3-year FCF CAGR — cash-flow growth.
• Sustainable growth rate — ROE × retention ratio.
• Reinvestment efficiency — incremental earnings per dollar of retained capital.
• Revenue consistency — 3-year standard deviation of annual growth rates.

Why sustainable growth rate matters. It ties reported growth back to the capital that was actually reinvested to produce it. A business growing 20% with a 25% ROE and a 100% retention ratio is compounding internally. A business growing 20% funded by serial secondary offerings is not — it is purchasing growth with shareholder dilution.

Why reinvestment efficiency matters. High growth at low ROIC is value-destructive. If the business reinvests a dollar and generates sub-cost-of-capital returns, growth is making shareholders poorer, not richer. Reinvestment efficiency is the ratio that flags this.

Why consistency matters. Lumpy growth — 40% one year, -10% the next, 25% the year after — is usually cyclical or project-driven, and the averaged CAGR is misleading. Revenue consistency penalizes volatility in the growth stream, which in practice filters out project-lumpy businesses masquerading as compounders.

Earnings quality (6 factors)

The question this bucket answers: are the reported earnings real, or are they being stretched by accounting choices?

• Accruals ratio (Sloan ratio) — (net income − operating cash flow) / total assets.
• Cash conversion — operating cash flow / net income.
• One-time charge frequency — count of "non-recurring" charges over 3 years.
• Goodwill as % of equity — acquired intangibles relative to book value.
• Share count trend — 3-year change in diluted share count.
• Audit risk proxies — auditor changes, going-concern flags, restatement history.

Why accruals matter. Sloan's 1996 paper is one of the most replicated results in accounting research: firms with high accruals — where reported earnings meaningfully exceed operating cash flow — underperform low-accrual firms by roughly 10% annually in long-short portfolios. The intuition is simple: when net income is running ahead of cash, something is being capitalized, accrued, or timed. Sometimes it's benign. Often it's not.

Why share count trend matters. Buybacks executed at prices below intrinsic value create per-share value mechanically. Dilution — from option grants, secondary offerings, or acquisitions paid in stock — destroys it. The 3-year trend separates businesses that return capital from businesses that quietly consume it.

Why goodwill bloat matters. Serial acquirers who pay up for growth carry enormous goodwill balances on the asset side and fragile equity on the liability side. When the cycle turns, the writedowns come. A goodwill-to-equity ratio above 1.0 means a full goodwill impairment would erase book value. That is not always a red flag, but it is always worth flagging.

How factors combine

Each of the 24 factors is z-scored within its GICS sector. A z-score of +1 means one standard deviation better than the sector median; -1 means one standard deviation worse. We use the sector cohort because absolute thresholds are meaningless across industries — a 40% debt-to-equity ratio is aggressive for a software company, normal for a utility, and conservative for a regional bank.

Each bucket score is the simple average of the 6 z-scores in that bucket. Equal weights, by design — factor weighting on historical backtests is one of the fastest routes to overfitting, and the marginal forecasting power from optimized weights rarely survives out-of-sample.

The overall letter grade is derived from the four bucket scores:

• A — all four buckets in the top decile of the sector (roughly top 10%)
• B — all four buckets in the top quartile (top 25%)
• C — bucket scores cluster around the sector median
• D — one or more buckets in the bottom quartile
• F — any bucket in the bottom decile

The last rule is the hard floor: any single bucket in the bottom decile forces the overall grade to F, regardless of how strong the other buckets look. A growth monster with collapsing financial health is not a quality business. Neither is a fortress balance sheet attached to stagnant, unprofitable operations. We refuse to average our way around a single catastrophic bucket.

Why sector-relative z-scores

Same reasoning as sector-relative P/E. A 40% debt-to-equity is aggressive for a SaaS company, normal for a utility, and conservative for a bank. A 15% operating margin is great for a grocer and mediocre for a software business. Applying absolute thresholds to any of these metrics produces sector-concentrated portfolios — all utilities, or no banks, or no industrials — regardless of whether any individual name is actually high-quality.

Normalizing within sector does two things. First, it strips out structural differences that have nothing to do with management skill or competitive position. Second, it forces the screen to rank every stock against the peers it actually competes with, which is the only comparison that carries economic meaning.

The Tessera Rating in practice

The letter grade is a signal-eligibility gate, not a scalar weight:

• A — eligible, full signal weight
• B — eligible, full signal weight
• C — hold-only: existing positions are not force-exited, but no new entries
• D — ineligible for buy signals
• F — ineligible for buy signals

Pairing this with the rest of the stack: a candidate has to be A or B graded, trade at a meaningful sector-relative P/E discount, survive regime filtering (we don't take new positions in a CRISIS regime), and pass competitive-rotation comparison against current holdings before capital gets committed. Even then, position size is capped at 7% of portfolio equity.

Known limitations (honest caveats)

Quality screening is a filter, not an oracle. A few things it does not do well:

• It's lagging. Financials release quarterly and often on a 45-90 day delay. A business whose quality is deteriorating in real time will still grade well on stale data for a quarter or two. Price-based signals and regime detection partially compensate, but the lag is real.
• Fraud proxies are weak. The audit-risk and accruals factors would have flagged some Enron-era frauds and would have missed others. No factor model reads 10-Ks for intent.
• Sector classification shapes everything. GICS has known quirks — conglomerates, recently reclassified names, platform businesses that don't fit a bucket cleanly. A misclassified stock gets compared to the wrong peers and the grade becomes noise.
• Revenue recognition is industry-specific. Aggressive bookings in subscription software, deferred revenue dynamics in ad-tech, percentage-of-completion accounting in long-cycle industrials — these can all look clean on the standard earnings-quality metrics while being economically aggressive. The model does not catch this.
• Small-cap coverage is thinner. Sector z-scores require enough peers to be meaningful. Below roughly $500M in market cap, data quality degrades, analyst coverage thins, and grades rely on filings that may be months stale. Treat small-cap grades with more skepticism than large-cap grades.

None of this makes the screen useless — it makes it a screen. The goal is to eliminate the bottom tail systematically. The top tail still requires everything else: valuation, regime, position sizing, and the discipline to exit when the thesis breaks.

Related reading

• Relative P/E Sector Analysis
• Backtesting Pitfalls
• Position Sizing and Stop Losses