CompanyGraphStory Stock Screener
Named configurations where multiple signals align. Each story represents a recognizable structural condition.
Select stories or signals to build your search
Stocks matching this structure will appear here
Named configurations where multiple signals align. Each story represents a recognizable structural condition.
Select stories or signals to build your search
Stocks matching this structure will appear here
The screener is built on structural evaluation. Before any search is performed, each company is evaluated across defined structural characteristics. These evaluations are called signals. A signal measures a structural relationship within the data — not just a value, but a pattern across time, balance sheet structure, capital allocation behavior, or market dynamics.
Examples include:
Each signal has a defined range, strength score, and confidence measure. All searches ultimately resolve to signals.
Some structural conditions require multiple relationships to align. When several signals align simultaneously, they form a recognizable configuration. These configurations are called stories.
A story is the result of structural signals aligning. Stories describe structure — they do not predict outcomes.
Stories are grouped into two structural roles.
Describe recognizable structural conditions present in the data.
Highlight structural tension or mismatch between surface interpretation and underlying structure.
Traditional filtering combines isolated metrics manually. This screener evaluates structural relationships in advance, allowing multi-dimensional conditions to be expressed as coherent configurations. Instead of assembling dozens of independent filters, you can explore calibrated structural conditions directly.
You remain in control of signal selection. Stories reflect structural alignment when it occurs.
Below are example structural configurations that can be opened directly in the screener. These illustrate how signals align into recognizable patterns.
What it represents: Revenue and earnings expanding steadily with low variance in growth rates. This configuration highlights businesses where expansion appears structurally supported rather than episodic, emphasizing regularity over raw speed.
What it represents: Elevated capital expenditure relative to operating cash generation. This configuration identifies companies directing substantial resources toward asset expansion. It describes allocation posture, not investment quality.
What it represents: Increasing earnings growth relative to prior periods. This configuration captures momentum phases where multiple acceleration signals align. It describes a current structural phase, not permanence.
Each configuration can be opened and modified inside the screener. Stories describe structure. You remain in control of signal selection.
CompanyGraph does not provide financial advice. Signals measure current structural conditions. Stories describe configurations of those conditions. The system does not promise performance and does not eliminate uncertainty. It clarifies structure.
Most screeners ask you to build filters from raw metrics — P/E under 15, ROE above 20%, debt-to-equity below 1. You combine them manually, hope they interact meaningfully, and scroll through whatever survives. CompanyGraph works differently. It pre-evaluates every company against structural conditions before you arrive. You describe what you're looking for in terms of patterns, not thresholds.
The Stories tab is the primary entry point. Over 200 named configurations span a wide range of investing philosophies — Graham Value sits next to Trend Alignment, Insider Buying next to Antifragile Growth, Balance Sheet Fortress next to Momentum Phase. The tool is deliberately philosophy-agnostic. Each story visually decomposes into its component signals, so you can see exactly what structural conditions must align. Quality Compounder, for instance, requires earnings quality, growth consistency, and cash flow margin signals to all be present simultaneously. The separation into Situational and Diagnostic categories is a quiet but important design choice — it tells you not just what a story describes, but what kind of structural observation it represents.
This is the engine room. Nearly 500 individual signals cover everything from deeply fundamental measurements (accrual intensity, asset turnover, book value growth, free cash flow yield) to technical structure (Aroon, Bollinger squeeze, ADX trend strength, Ichimoku configurations) to behavioral and diagnostic indicators (Altman Z-score, Piotroski F-score, short interest dynamics). Each signal measures one structural relationship — not just a value, but a pattern across time, balance sheet structure, or market behavior. For users who want to build their own configurations from scratch rather than starting from a named story, this tab is where that happens.
Seven categories classify companies not by sector but by their structural function in an economy: Production, Flow, Risk, Interface, Attention, Rule, and Sense-Making. This is an unusual taxonomy influenced by systems thinking — a semiconductor manufacturer and an airline both produce, but an exchange and a payment processor both enable flow. It cuts across traditional sector boundaries and surfaces structural similarities that industry classification obscures.
A comprehensive set of 147 industry categories, from Advertising Agencies to Waste Management, presented in the same visual language as the rest of the screener. Straightforward sector filtering — but useful as a secondary refinement layer when you want to constrain a structural search to a specific economic area.
The most structurally distinctive tab. Dependencies maps supply chain relationships between industries using six connection types: who provides inputs, who builds infrastructure, who supplies tooling, who handles distribution, who creates demand, and who regulates. When you select an industry as a dependency, the screener finds all industries that structurally depend on it — and shows you stocks across that entire downstream ecosystem. This answers a question no traditional screener asks: if you believe in semiconductors, who structurally needs semiconductors to function? It lets you think in terms of economic ecosystems rather than isolated sectors.
Clean and focused: Size and Valuation (Market Cap, Enterprise Value, P/E, P/B, P/S, EV/EBITDA, PEG), Profitability (margins, ROE, ROA), Growth (Revenue and Earnings year-over-year), and Balance Sheet (Debt/Equity, Current Ratio). This tab works as a refinement layer on top of the structural search — not a replacement for it. You find companies through stories or signals first, then narrow by fundamental characteristics if needed.
Portfolio construction logic built directly into a screener. You input a company and filter by relationship type — Strong Amplifier, Strong Mirror, Strong Dampener, and moderate variations of each — optionally filtering by how closely stocks track the market over different time horizons. Finding stocks that dampen relative to a holding you already own, or that mirror it for concentration awareness, is genuinely useful for thinking about portfolio structure rather than individual stock selection.
The tabs are not independent filters — they compose. A search can combine a story with an industry constraint, a dependency relationship, a fundamental threshold, and a correlation filter simultaneously. The structural pre-evaluation means results reflect genuine multi-dimensional alignment, not just the intersection of independent screens. Stories and signals are also reused across individual stock pages throughout the site, so the same structural language you learn in the screener appears when you examine any company in detail. The vocabulary is consistent — what you see here is what you see everywhere.
Capital Reinvestment
Company with elevated capital expenditure relative to cash generation
Earnings Acceleration
Company with accelerating growth in earnings, profits, and cash flow