Fundamental Screening: Building Stock Screens That Work

A quality stock screen filters for business durability - consistent profitability, low leverage, stable margins, and high returns on capital - rather than for a specific valuation or growth profile.

Raw valuation and margin multiples vary enormously by industry, so a sector-normalized screen compares each company only against its own sector peers rather than against the whole market.

Accounting-risk screening flags red flags like aggressive revenue recognition, rising receivables relative to sales, and frequent one-time adjustments that can signal a company's reported earnings are less reliable than they appear.

Screens built from widely available fields using conventional thresholds select overlapping sets, because the underlying data and the popular cutoffs are shared. A screen that produces a distinctive list generally does so by combining fields unusually, using a metric that requires manual computation, or applying a filter based on something the aggregators do not carry. Distinctiveness comes from the construction rather than from tightening a common threshold.

Applying one absolute threshold across all sectors selects whichever sectors structurally produce that characteristic, so a leverage screen selects utilities and a margin screen selects software regardless of company quality. Ranking within sector rather than across the whole universe removes that bias. The tradeoff is that a within-sector rank selects the best of a poor sector as readily as the best of a strong one.

Trailing figures that have not updated after a recent report, restated periods that the provider has not incorporated, negative denominators producing meaningless ratios, and inconsistent treatment of items like leases and stock-based compensation across companies. Each produces screen results that look correct and rest on a bad figure. Verifying the top results against filings catches most of them.

Exclusions are often more effective, because a screen designed to find good companies tends to also find companies whose figures are good for the wrong reasons. Excluding recent restatements, auditor changes, very low liquidity, or negative operating cash flow removes categories where the included metrics are unreliable. This is usually a larger improvement than refining the inclusion thresholds.

Fundamental data updates on reporting cycles, so rerunning more frequently than quarterly mostly picks up price movement rather than new information. Running immediately after a reporting season captures the newly reported figures. Running weekly on a screen dominated by fundamental fields produces churn that reflects price changes rather than changed businesses.

A screen mechanically applied as a portfolio rule is a strategy, and it can be tested as one, with the usual caution that the thresholds were probably chosen with knowledge of past data. A screen used to generate candidates for individual analysis is a starting point, and its job is to be roughly right rather than precise. Which of the two you are building determines how much the exact thresholds matter.