The Filter Lab is in active development. Some panels show illustrative data; full functionality ships with paid tiers. Your feedback shapes the product.
Three independent lenses on every public company - structural dynamics, traditional fundamentals, and forensic accounting. When they converge, the signal is high-conviction. When they disagree, you've found the question worth asking.
3
Independent Lenses
0.766
Two-Axis AUC
28
Years Tested
1.27M
Company-Months
AUC - how well the signal separates firms that later collapsed from those that didn’t: 0.5 is a coin flip, 1.0 is perfect. Across every US-listed company over 28 years, the two-axis signal scores 0.766. The full record is on the Validation page.
DRAFT - Pending final legal review. This page is provided for early-access testing. Final language will be reviewed by counsel before public launch.
Disclaimer
Effective: May 2026
Not Investment Advice
The Filter Lab Research Group LLC and The Filter Lab provide structural analysis tools for educational and informational purposes only. We are NOT a registered investment advisor, broker-dealer, or financial planner. Nothing on this site constitutes investment, legal, tax, or accounting advice.
About the methodology
The Filter Lab measures equity-return dispersion (turbulence) alongside accounting fundamentals for public companies. While the turbulence signal has been validated across every US-listed company over a 28-year period (see the Validation page), past patterns do not predict future outcomes for any individual company. Markets are complex adaptive systems subject to many factors this methodology does not measure.
No Recommendations
The Filter Lab does not recommend any particular security, sector, strategy, or transaction. References to specific tickers, sectors, or historical companies (Enron, WorldCom, Spirit Airlines, etc.) are illustrative of the framework only and do not constitute recommendations to buy, sell, or hold those or any related securities.
No Warranty
Analysis results are provided AS IS without warranty of any kind, express or implied. We do not warrant that results are accurate, complete, current, or fit for any particular purpose. Data is sourced from third parties (SEC EDGAR, Tiingo) and may contain errors, omissions, or delays.
User Assumes All Risk
You are solely responsible for your investment decisions. You should consult a licensed financial advisor, tax professional, and/or legal counsel before acting on any analysis from this site. Past performance is not indicative of future results. You may lose money - including all of your invested capital.
AI Disclosure
This site uses artificial intelligence (Claude by Anthropic) for educational analysis. AI-generated content may contain errors, fabrications, or outdated information. AI outputs are not a substitute for professional advice.
Limitation of Liability
To the maximum extent permitted by law, The Filter Lab Research Group LLC, its officers, employees, contractors, and affiliates shall not be liable for any direct, indirect, incidental, consequential, or punitive damages arising from your use of this site, including but not limited to investment losses, lost profits, or data inaccuracies.
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The Filter Lab Research Group LLC is a Texas LLC. Use of this site constitutes acceptance of the laws of the State of Texas as governing law. By using The Filter Lab, you acknowledge you have read, understood, and agree to this disclaimer.
DRAFT - Pending final legal review. This page is provided for early-access testing. Final language will be reviewed by counsel before public launch.
Privacy Policy
Effective: May 2026
What We Collect
The Filter Lab collects: (1) ticker queries you submit; (2) basic usage analytics such as page views and session duration; (3) email and payment information when you create a paid account. Free tier use does not require an account or personally identifiable information.
Data Sources
We retrieve company financial data from public sources: SEC EDGAR (filings) and Tiingo (market data). We do not permanently store this data; it is fetched fresh per analysis.
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Changes
We may update this policy. Material changes will be communicated via email to account holders. Continued use after changes constitutes acceptance.
DRAFT - Pending final legal review. This page is provided for early-access testing. Final language will be reviewed by counsel before public launch.
Terms of Service
Effective: May 2026
Acceptance
By accessing The Filter Lab (operated by The Filter Lab Research Group LLC), you agree to these Terms. If you do not agree, do not use the service.
Service Description
The Filter Lab provides structural financial analysis tools based on its equity-turbulence methodology. The service is provided for educational and informational purposes only. See our Disclaimer for limitations on the nature of this analysis.
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Free tier use does not require an account. Paid tiers require an account. You agree to: (a) provide accurate registration information; (b) keep credentials secure; (c) not attempt to reverse-engineer, scrape, or otherwise exploit the service; (d) not use the service for any unlawful purpose. We may suspend or terminate accounts for violations.
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Intellectual Property
The Filter Lab methodology, source code, and brand are property of The Filter Lab Research Group LLC and Ryan W. Malone. You may not copy, redistribute, or commercialize the service or its outputs without permission.
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The service is provided AS IS and AS AVAILABLE. We make no warranties of any kind, express or implied, including merchantability, fitness for a particular purpose, accuracy, or non-infringement. See our Disclaimer page for details on the educational nature of analysis outputs.
Limitation of Liability
To the maximum extent permitted by law, The Filter Lab Research Group LLC's total liability for any claim arising from your use of the service is limited to the amount you paid in the 12 months preceding the claim. We are not liable for indirect, incidental, consequential, or punitive damages, including investment losses.
Governing Law
These Terms are governed by the laws of the State of Texas. Disputes shall be resolved in the state or federal courts of Texas. You waive any right to participate in class actions to the extent permitted by law.
Changes
We may update these Terms. Material changes will be communicated via email to account holders or via in-product notice. Continued use after changes constitutes acceptance.
Cross-sectional cohort view — one transparent metric per column. Not a ranking or a recommendation.
Free tier
Free
Current Plan
Free
$0
Teaser · No account needed
Ticker or Company Name
Data Source
Pulls directly from SEC EDGAR. Works for any US public company with 10-K/10-Q filings.
Uses Tiingo daily price history to compute the turbulence reading on stock price dynamics.
Analysis Window
Rolling quarters12Q
Paste into ChatGPT, Claude, or any AI assistant to help you read this analysis.
How To Use
1. Type a ticker or company name
2. Choose data source (EDGAR / Price History)
3. Click Run Full Analysis
4. Read the plain-English structural read
5. Explore tabs · Click ? for explanations
How turbulence is measured
Turbulence = mean rolling 90d variance
✓ Variance ≤ 0.10 · Low ⚠ 0.10 to 0.25 · Elevated ✗ Variance > 0.25 · High / Extreme
Or type any US ticker or company name in the sidebar →
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● REGULATED
⚠Not investment advice. The Filter Lab provides structural analysis tools for educational use only. The Filter Lab Research Group LLC is not a registered investment advisor. Past patterns do not predict future outcomes. Full disclaimer →
View
View
Financials - the Analyst view reads the statements; the Quant view reads the distress models.
7-Layer Financial Cascade
Stress mapped across seven layers, liquidity to management credibility. L4 (capital structure) is the primary turbulence target. Click a layer for detail.
The Ride Over Time
How wild the ride has been over time. Higher means bigger swings, not a higher or lower price.
Shape of the swing over time, computed from daily closes. The current band comes from the full model; this line shows trajectory, not the exact scored signal.
Revenue vs Operating Income
Quarterly trend across the most recent 12 quarters of EDGAR filings (where available). The gap between top-line revenue and operating income is the operating margin - how much of each dollar in stays in after running the business. For banks, top line is Net Interest Income; for insurance, Premiums Earned; for REITs, Rental Income.
Cash Flow Components
Four cash-related metrics quarterly: Operating Cash Flow, Free Cash Flow, CapEx, Net Income. Healthy companies have CFO and FCF tracking together; divergence is a warning.
Asset Allocation
Where the company's value lives on the balance sheet. Goodwill-heavy = M&A risk. Cash-heavy = optionality. PP&E-heavy = capital intensive.
Cash Flow Bridge
From Net Income to Free Cash Flow. Each step shows how earnings convert (or don't) into cash. Big negative bars on operating items signal accounting risk.
Series Trend Spread
Distribution of trend strength across this company's financial series. Each dot is one series (revenue, op income, etc.). Magnitude near 1 = strong trend; near 0 = noise.
Want the full breakdown? Switch to Quant view for the variance gauge, health snapshot, and the three-regime explainer.
90-day rolling variance
Health Snapshot
This is a descriptive risk tool, not investment advice. It describes how a stock’s price has behaved and how solid its fundamentals look. It does not recommend buying, selling, or holding anything, and it does not predict returns.
Turbulence - Full Analysis
Want the full breakdown? Switch to Quant view for key drivers, per-series trajectory, and the 7-layer cascade.
Per-Series Trajectory
Turbulence is computed from price-return dynamics. Per-series fundamental trajectory below shows how each financial line item is trending across the analyzed quarters - rising, flat, or falling.
What The Four Ride Bands Mean
✓ Low
Rolling-window variance is in the framework's baseline range (variance ≤ 0.10). Equity dynamics absorb shocks without amplifying them across the rolling window. No active early-warning signal.
⚠ Elevated
Variance is between 0.10 and 0.25 - above the baseline range. Whether the trajectory warms or cools over subsequent quarters is the analytical question; outcomes in the 28-year test history varied.
▲ High
Variance is between 0.25 and 0.50 - well above baseline. In the framework's 28-year test history, signatures in this band appeared in cases of both structural stress (Lehman, AIG, SVB, Enron) and rapid positive scaling (NVIDIA, early Tesla). The turbulence reading does not predict which trajectory applies to any specific company.
✗ Extreme
Variance is at the top of the framework's scale (≥ 0.50, annualized vol ~70%+) - its highest measurement band. High dispersion alone is a measurement, not a verdict. The turbulence reading does not predict outcomes for any specific company.
⚠Reference data, not a prediction. The Backtest Reference shows what the turbulence model would have classified for this ticker at a historical observation date, alongside the actual market outcome that followed. This is descriptive analysis of past data — not a forecast for any future period and not investment advice.
📊
How to read this tab: Each ticker in the backtest universe has a single Q8 observation point from its 20-quarter EDGAR history. We computed five frameworks (Turbulence, Altman Z, Piotroski F, Beneish M, Composite) at that date, then tracked the realized return over the next 1Q, 2Q, 4Q, 8Q, and 12Q. The “closest-aligned framework” is whichever signal best matched the realized outcome — not the “winner”, just the one whose classification landed closest to what actually happened.
⏳
Loading backtest data...
If this message persists, click Run Full Analysis on the left to fetch backtest data for this ticker.
Key Ratios · current value · recent-quarter trend
How to read the income statement: Follow the money from the top (Revenue) down to the bottom (Net Income). Each layer subtracts another type of cost. A healthy company should have strong margins at each layer. Watch for gross margin vs operating margin - a big gap means high overhead costs.
Income Statement - Trailing Twelve Months
Source: SEC EDGAR 10-K / 10-Q · All figures in millions USD
How to read the balance sheet: Assets = Liabilities + Equity. Always. Think of it as a snapshot of what the company owns vs what it owes on a single day. High debt relative to equity means the company is leveraged - manageable when profitable, dangerous when earnings fall.
Balance Sheet - Most Recent Quarter
Source: SEC EDGAR
Cash flow is the most honest financial signal. Net income can be manipulated by accounting choices - depreciation schedules, revenue recognition timing, write-offs. Cash flow is much harder to fake. A company that consistently generates more cash than it reports in earnings is almost always high quality.
Cash Flow Statement - Trailing Twelve Months
Source: SEC EDGAR
These are the standard financial-health metrics most tools rely on - Altman Z (a distress-zone score), Piotroski F (a 9-point fundamental-quality check), and the liquidity and coverage ratios. They describe the company's accounting condition at the filing date. Turbulence measures something different and uncorrelated: the dispersion in its equity dynamics. Reading the two side by side - accounting state next to structural state - is the point of this tab. Neither is a prediction.
Traditional Financial Health Metrics
Altman Z - Decomposition
The single Z is built from five weighted ratios. Seeing the parts shows where the strength - or stress - comes from.
Piotroski F-Score - 9 Signals
Forensic accounting screen. The Beneish M-Score is a peer-reviewed model (Beneish, 1999, Financial Analysts Journal) for flagging firms with characteristics of earnings manipulation. It examines eight year-over-year ratio changes - receivables growth vs. revenue, margin trajectory, accruals, leverage, and more - and combines them into a single composite score. A high M-Score is not a verdict - it is a screening flag for closer examination. The original 1999 study reported approximately 76% sensitivity and 17.5% false-positive rate against SEC enforcement actions on US firms.
📊
Run Full Analysis to compute the Beneish M-Score
The forensic screen pulls annual (FY) data from EDGAR filings and computes eight year-over-year ratios. Click Run Full Analysis on the left sidebar to generate the breakdown.
Peer comparison: See how this company's turbulence and traditional metrics stack against industry peers. Turbulence reveals structural patterns conventional metrics miss - comparing peer turbulence values shows where this company's variance sits relative to its sector.
AI Analyst — temporarily unavailable
The in-app AI chat is disabled pending a redesign in which you bring your own LLM with the framework’s system prompt. No questions are sent to any AI service from this page.
Pricing
Simple, Transparent Pricing
Two tiers. No setup fees. Cancel anytime. Annual plans save ~20%.
Every U.S.-listed company, every month, for 28 years. We checked whether our turbulence signal actually sorted the companies that later failed from the ones that survived - point-in-time, with no peeking at the future.
Educational research about historical patterns. Not a trading recommendation.
28 yrs
1998-2026 full market history
1.27M
company-months 9,411 failures
0.766
AUC, top-3,000 walk-forward, no lookahead
survivor- honest
bankruptcies at −100% nothing dropped
What we tested
The idea, and the test
The idea: a company’s stock gets choppier in a characteristic way before it gets into real trouble - the same “critical slowing down” that shows up before tipping points in ecosystems, climate, and other complex systems. We measure that choppiness as the rolling variance of daily returns.
The test is deliberately unflattering. We take the entire universe of U.S.-listed companies - not a hand-picked set - and, at every point in time, ask a simple question: did the companies that went bankrupt in the next 12 months rank as riskier on this signal than the companies that survived? Every measurement uses only data available at that moment. Acquisitions count as survivals, not wins. Companies that died are kept in at −100%, so nothing is quietly dropped to flatter the numbers.
The honest number
Why we don’t show you a 0.96
You will see other early-warning work quote accuracy scores up around 0.96. We could too - on a small, curated list of famous collapses, this same signal scores that high. We don’t lead with it, because a curated list is the easy version of the test. The honest number is what the signal does across every company, including the thousands of unglamorous ones, with no cherry-picking.
What was tested
Score (AUC)
What it means
Every company, full 28-yr history
0.789
the broad, honest science number
The 3,000 names we actually cover
0.766
the number we stand behind for the product
A small curated list of collapses
~0.96
the easy test - not how we describe the product
An AUC of 0.766 means: pick a company that later failed and one that survived at random, and the failing one ranked riskier about 77% of the time. 0.5 is a coin flip; 1.0 is perfect. This is a ranking statistic about the past, not a prediction about any single company’s future.
The core finding
It sizes the door - it doesn’t point you through it
Here is the single most important thing the data says. Sort every company into four turbulence bands, then look at the actual range of returns over the next 24 months. As turbulence rises, the typical outcome drifts down - but the spread of outcomes explodes. The Extreme band holds the worst wipeouts and the biggest moonshots; they look identical at the start.
Bars span the 10th-90th percentile of 24-month forward return; dot is the median. 1.27M company-months, 1998-2026.
This is why the product is descriptive, not a buy/sell call: a high band tells you the door is wide - the range of what could happen is large - not which way you’ll go through it. A second “company health” axis sharpens the downside within each band (it sorts the trapdoors, not the jackpots), which is why we read the two together rather than turbulence alone.
Across the cycle
It lights up at every crisis
The share of the market sitting in the two highest turbulence bands, month by month, for 28 years. The fast (90-day) lens spikes early and sharply; the slow (trailing-year) lens lags and smooths into a regime read. Shaded bands are the 2001, 2008-09, and 2020 recessions.
COVID is the clearest example: the fast lens hit ~97% of the market in weeks, while the slow lens barely moved - a true shock that never became a sustained regime. Both lenses ship, because they answer different questions.
Not a penny-stock trick
Strongest on the biggest companies
A fair worry about any signal like this: maybe it only “works” on tiny, illiquid junk stocks. The opposite is true. Split the market into ten size buckets and the signal is weakest on the smallest names and strongest on the largest - the reverse of a penny-stock artifact.
Turbulence-alone AUC within each market-cap decile. 0.50 = chance.
Where it fails
Most validation pages skip this part. It’s the part that matters most.
The signal is not uniform across sectors. In two corners it is weak enough that we treat it differently in the product:
Sector (top-3,000)
Turbulence
Company
Two-axis
Lift
Consumer Defensive
0.948
0.867
0.959
+0.011
Basic Materials
0.860
0.856
0.898
+0.037
Consumer Cyclical
0.870
0.818
0.892
+0.022
Energy
0.847
0.756
0.857
+0.010
Technology
0.759
0.787
0.812
+0.052
Real Estate
0.825
0.656
0.809
-0.016
books ≈ coin-flip — ride-only
Healthcare
0.763
0.757
0.803
+0.040
Communication Services
0.724
0.776
0.799
+0.075
Utilities
0.742
0.705
0.766
+0.024
Financial Services
0.736
0.719
0.753
+0.017
turbulence-led
Industrials
0.445
0.630
0.501
+0.056
turbulence below chance — lean on books
Industrials: turbulence alone scores below a coin flip (0.445) on the product universe - there we lean on company fundamentals, not the price signal. Real Estate: the fundamental axis is roughly a coin flip, so we run it turbulence-only. We tested bank- and REIT-specific fundamental recipes to fix these; none beat the simple signal, so we don’t pretend otherwise.
It is not a timing trigger. The signal characterizes risk; it does not tell you when something will happen. Both axes tend to light up together, late, rather than one leading the other. Useful for describing how exposed a company is - not for trading a date.
What this doesn’t prove
This is a retrospective study of historical patterns, not a live, prospective forecast. The signal was measured against the past, not deployed in real time.
Results may not generalize beyond 1998-2026 U.S.-listed companies. Different markets or regimes could behave differently.
Walk-forward testing respects time direction but cannot test against a future regime with no historical analog.
Nothing here is a trading recommendation. Historical ranking accuracy does not imply forward predictive validity for any individual security.
The data and the code
Method
A point-in-time discrimination study over 1.27M company-months and 9,411 bankruptcies (1998-2026), survivorship-honest, walk-forward. Fundamentals use as-reported trailing figures joined as-of their filing date - no restatement lookahead.
Disclosure
The Filter Lab is a research program; conclusions follow the data. Analysis by Ryan W. Malone (independent researcher); AI was used as a drafting and engineering aid, with all results computed and reviewed by the author.
How It Works
Find it, read it, track it.
Filter Lab measures two independent things about a company — how turbulent the stock has been (The Ride) and how the books look (The Company) — and describes where it sits. Here is how the three screens work together.
FILTER LAB PHILOSOPHY
We measure. We describe. We compare. We monitor. You decide.
Every reading is descriptive — a measurement of what has happened, not a forecast or a recommendation. Filter Lab sizes the range of what could happen, never the direction, and never tells you what to buy, sell, or hold.
THE INVESTIGATION LOOP
Screener → find candidates worth a look Analyzer → the hub, where you dig into one name Watchlist → track what you keep, get flagged when it changes
You start at the Screener and spend most of your time in the Analyzer. Every result on every screen can be handed to your own AI assistant with one click.
1 · Start at the Screener
Where you begin — explore by criteria
The Screener is the exploration loop. You set the criteria — ride band, sector, fundamentals — and it returns the companies that match. Sort by turbulence, size, or any column. Use the Divergence filter to surface names where the ride is wild but the books still read solid — often the most interesting cases to investigate.
Convergence (both readings pointing the same way) is a clearer signal than a single reading alone. Click any row to open it in the Analyzer.
You chose the filters; it returned the matches — never “what fits you.”
2 · The Analyzer is the hub
Where you spend the most time — one company, in depth
Type any ticker (or arrive by clicking a Screener result) and run a full structural read. You get the two readings side by side, the risk home the company lands in, its position within its sector cohort, and the turbulence history charted over time. This is a deep read on one company at one point in time, assembled so you do not have to gather it by hand.
Read the plain-English summary first; click any ? to go deeper on a measurement. When the two readings diverge — a wild ride on solid books, or the reverse — both can be true at once; the signal sizes the range of outcomes, not the direction.
3 · Track in the Watchlist
Retention — the return trigger
Save the names you care about. The Watchlist shows the current reading for each holding and, more importantly, flags when a reading changes — a holding moving from a Low to an Elevated ride, for example. That change is your cue to open it in the Analyzer and understand why.
The Analysis tab X-rays your own basket: how it distributes across the risk homes, sizes, and sectors. It describes what you put in — it never assembles a basket or calls one suitable.
On every screen: work with your own AI
Each screen has its own Copy for AI button. It hands the data — plus a reading guide that teaches your AI assistant how to interpret it — to ChatGPT, Claude, or any assistant you use. The three do different jobs:
Screener · Copy list for AI — triage a list of names: which are worth a closer look, which to set aside.
Analyzer · Copy analysis for AI — understand one company: what each reading shows and what to investigate.
Watchlist · Copy changes for AI — review what moved in your holdings and what is worth a second look.
Filter Lab structures the problem. Your AI helps you think it through. You make the decision.
About The Filter Lab
The structural signal Wall Street doesn't measure.
What it does
The Filter Lab is a structural monitor for public companies. It measures when a company's equity dynamics are widening - unstable, dispersing return behavior - and shows that alongside its accounting fundamentals. It describes a company's current structural state. It does not predict prices, rank stocks, or tell you what to do.
Where it comes from
Complex systems under stress tend to behave the same way before they tip - their fluctuations grow and slow, a pattern studied across physics, ecology, and physiology as “critical slowing down.” The Filter Lab applies that lens to corporate finance: it watches whether a company's equity dynamics are settling or dispersing, often before the strain reaches earnings reports or credit ratings.
Descriptive by design
Most tools measure the state of a company - what the numbers say right now. The Filter Lab measures the dynamics - whether the system is settling toward stability or dispersing away from it. That shows up earlier, but it is a description of risk, not a forecast: it sizes the range of what could happen, not the direction.
We also show exactly where the signal works and where it fails - including the sectors it reads poorly. The full 28-year, every-company test record lives on the Validation page.
Built in the open
Independent research by Ryan W. Malone. The method and its derived data are archived publicly, so the work can be checked rather than just trusted. AI was used as a drafting and engineering aid; all results were computed and reviewed by the author.