Frameworks, techniques, and tools for evaluating businesses using public data, financial signals, and AI-powered diagnostics.
A practical six-pass checklist for analyzing a company before you invest, from business model and financial health to management quality and valuation.
Operating leverage explains why small revenue moves turn into big earnings swings. How to estimate it from filings and use it in forecasts and stress tests.
A company's low tax rate can hide expiring incentives, audit risk, and jurisdiction bets. How to read the tax footnote and spot the earnings risk early.
Buybacks can manufacture EPS growth with no improvement in the underlying business. A practical guide to separating real growth from share count math.
Deferred revenue shows next year's growth forming before it reaches the income statement. How to read the trend, adjust for seasonality, and spot the distortions.
A practical method for picking true comparables: match business model, size, growth, margins, and leverage before you trust any valuation multiple.
A goodwill write-down is management admitting it overpaid for a deal. How to read the charge, spot warning signs early, and judge an M&A track record.
Capex is where management's real priorities show up. How to split maintenance from growth spending, test returns on capital, and spot the games in the filings.
How to find customer concentration in SEC filings, judge when a dominant customer becomes dangerous, and model what happens if that customer walks.
Diversified companies often trade below the sum of their parts. Here is how to use segment disclosures and sum-of-the-parts math to find the gap.
R&D budgets tell you little on their own. Here is how to use filings, patents, and new product disclosures to judge whether the spending produces anything.
Where to find geographic revenue in a 10-K and how to read it for currency risk, political exposure, concentration, and the growth hiding underneath.
A practical process for judging management from public filings alone: capital allocation record, insider ownership, board quality, and how they explain bad quarters.
A practical guide to reading DSO, DIO, DPO, and the cash conversion cycle so you can spot operational trouble quarters before it reaches earnings.
How to decode management language on earnings calls: confidence signals, hedging patterns, dodged questions, and the tone shifts that precede results.
A practical guide to reading supply chain risk out of 10-Ks: supplier concentration, geographic exposure, inventory footnotes, and cash cycle signals.
How to pull a company's top line apart using public filings: recurring versus one-time revenue, organic versus acquired growth, and the metrics that reveal durability.
A practical guide to reading a company's debt footnote: maturity schedules, fixed versus floating exposure, covenants, revolver capacity, and refinancing risk.
A practical guide to reading cash flow statements: operating cash flow, free cash flow, capex quality, and the red flags that earnings numbers hide.
How to analyze small-cap companies with zero analyst coverage: reading filings nobody reads, building your own estimates, and sizing for thin liquidity.
A practical guide to SEC Form 4 filings, covering which insider trades carry real information, how to read 10b5-1 plans, and how to spot cluster buying.
How to read a DEF 14A proxy statement: pay structure, performance metrics, clawbacks, severance terms, and the red flags that signal weak alignment.
How to test competitive moat claims using SEC filings alone: gross margin stability, ROIC persistence, customer concentration, and the tells in Item 1 and 1A.
A practical guide to spotting accounting red flags in financial statements, from revenue recognition games to cash flow divergence and the Beneish M-Score.
Nearly every big company reports an adjusted profit number alongside GAAP. Here is how to judge which adjustments clarify and which ones mislead.
Most investors skip the MD&A. Here is how to read it in fifteen minutes: what to skim, what to compare year over year, and the red flags that actually matter.
A step-by-step framework for analyzing any company with free public data: SEC filings, proxy statements, FRED, court records, and the ratios that matter.
P/E ratios differ across industries for structural reasons. Here is why cross-sector comparisons mislead investors and which valuation metrics work better.
Seven ratios that flag corporate distress early, from the Altman Z-Score to free cash flow yield, and where to pull each number from SEC filings.
A section-by-section map for reading a 10-K in about 30 minutes: where the real information lives, what to skip, and how to spot red flags on a first pass.
Knowledge workers waste an average of 8 hours per week on duplicate or unnecessary tasks. Lean principles applied to office work reveal waste hiding in approval chains, reports, and workflows.
Ask any operations manager to describe their process and you get a clean, logical sequence. Pull the actual event logs and you see something entirely different.
Someone mentions a company you've never heard of. You have 30 minutes. Here's a systematic approach to building a picture from scratch using only public information.
By the time a company announces a pivot, it's been underway for months. New job titles, quiet domain registrations, and targeted leadership changes are the early warning signs.
A company has 200 employees. Without context, the number is meaningless. Growth rate, acceleration, and the composition of that growth carry far more signal about actual trajectory.
Leaders leave an enormous amount of public signal. LinkedIn profiles, conference talks, org changes, and decision patterns build a surprisingly detailed picture of who's running the show.
Translating complex analysis into clear narratives for investors, board members, or clients who do not live in spreadsheets. Structure, visualization, and prioritization techniques.
The shift from compliance to advisory. How accounting firms are using company analysis tools to offer strategic insights alongside traditional financial services.
Cash flow triage, customer concentration risk, key employee flight risk, vendor relationship health. The diagnostic framework turnaround professionals use when time is short.
The first 48 hours of a fractional CFO engagement: what they check immediately, which questions they ask, and how they prioritize issues in unfamiliar companies.
Board-level company profiles require concise scoring, specific risk callouts, honest competitive positioning, and recommendations concrete enough to act on.
Technology choices compound. They determine how fast a company can move, how easily it can scale, and how vulnerable it is to disruption. If you're not looking at the tech stack, you're missing a critical dimension.
Individual reviews are unreliable. Patterns across hundreds of reviews are remarkably consistent indicators of company health, if you know how to read them.
A collection of facts about a company is like a pile of ingredients on a kitchen counter. You haven't cooked anything yet. Analysis is where the real work begins.
A marketplace without network effects is just an intermediary. Analyzing marketplace businesses requires frameworks built for two-sided dynamics, liquidity measurement, and non-linear economics.
Real cleantech leaves measurable traces, and greenwashing tends to fall apart under systematic scrutiny. The reliable approach is looking for consistency across multiple indicators.
You do not need to understand the mechanism of action of a drug to assess whether the company selling it is well-run. The business signals in healthcare are surprisingly accessible if you know where to look.
A practical blueprint for building a systematic company analysis function inside your organization. Covers scope, templates, tools, team building, processes, and integration.
Business intelligence and company analysis are converging. Combining internal operational data with external market intelligence creates insights neither discipline provides alone.
Understanding the experience of buying from your competitor reveals gaps and opportunities that feature comparisons never surface. Here is how to map it.
Every company leaves a strategic trail through job postings, patents, partnerships, and website changes. Here is how to read it.
Before accepting a job offer, analyze the company like an investor would. Financial stability, growth trajectory, leadership quality, and competitive position all affect your career.
Companies that pass strict compliance screens tend to exhibit characteristics that conventional quality investors also look for. Low debt, diversified revenue, strong governance. This overlap is not a coincidence.
Companies will tell you they value their employees. The question is whether the data supports the claim. A surprising amount of information about how a company actually treats its workforce is publicly available if you know where to look.
Strip away the activism, the branding, and the political arguments, and there is a practical question worth taking seriously: do companies that manage environmental, social, and governance factors well tend to be better long-term investments?
A single letter grade or numerical score is a convenient way to summarize a company's ethical profile. It is also a dangerous oversimplification that can lead values-based investors to hold companies that violate their principles.
A skilled analyst can build a credible investment thesis without any privileged access. The key is being systematic with the public information that is already available.
A company can grow revenue every quarter while simultaneously losing market share, eroding margins, and building a fundamentally weaker business. Here is how to spot the pattern.
Competitive moats, by their nature, tend to be visible from the outside. A moat that only shows up in a spreadsheet is probably not much of a moat.
Revenue is just one proxy for budget capacity. Employee count, tech stack, office footprint, and funding history get you close enough to qualify and price deals with private companies.
The gap between a forgettable sales pitch and a deal-closing conversation is not charisma. It is structured company analysis that turns cold outreach into contextualized conversations.
Analyzing companies in emerging markets requires different data sources, local context, and adjusted methods. Standard Western-market playbooks do not transfer directly.
The analyst workflow shifted from manual data collection across siloed sources to AI-powered integrated profiles. The tools changed dramatically. The need for good judgment did not.
Revenue is a lagging indicator. Website traffic moves earlier, reflecting real-time interest and customer acquisition momentum before it shows up in financial statements.
Advertising spend is one of the clearest indicators of growth ambition. Between transparency tools and traffic platforms, you can build a detailed picture from the outside.
Every domain name has a paper trail. Registration dates, ownership changes, historical snapshots. This internet archaeology provides context companies would never volunteer.
Open a company's blog and scroll. The publishing pattern, topic focus, and content quality reveal more about marketing maturity than most analyst reports.
You do not need to understand link building or keyword difficulty to audit a company's SEO. The basics are surprisingly revealing and take about fifteen minutes.
Security headers are the unglamorous plumbing of the web. A company that invests in them is investing in things that only matter if you care about doing things right.
Running a company analysis on your own business reveals what customers, competitors, and partners actually see. The outside-in perspective helps small business owners fix blind spots.
Patent filings reveal where a company invests its R&D effort. Filing volumes, technology classifications, citation patterns, and inventor networks all provide strategic insight.
ML detects risk signals across SEC filing language, employee sentiment, leadership turnover, and competitive positioning. The composite view reveals systemic patterns no single signal shows.
Point-in-time company analysis misses what changes between reviews. Continuous monitoring of job postings, reviews, news, and filings catches meaningful shifts as they happen.
Investigative reporters and financial analysts share overlapping methods. Corporate registries, beneficial ownership, financial patterns, and digital footprints can strengthen business journalism.
NLP turns unstructured text into structured data. For business analysis, that means extracting real signals from thousands of reviews, news articles, and filings at a scale manual reading cannot match.
Tab-heavy browser sessions, copy-pasting into spreadsheets, losing track of sources. Manual company research does not scale in a world with this much data.
The structured frameworks consultants use to diagnose company health in compressed timelines, from hypothesis-driven research to MECE issue trees.
Every company leaves a digital trail that's far more honest than its marketing copy. The value is in layering signals from multiple sources until a coherent picture forms.
AI processes thousands of data points without confirmation bias. Humans catch narrative inconsistencies AI misses. Understanding where they diverge is the key to better company analysis.
Financial statements measure outcomes. The qualitative signals that don't show up in any spreadsheet often tell you what's about to happen.
Job postings are strategic documents. When analyzed collectively, hiring patterns form one of the clearest maps of where a company is actually headed.
If you pull ESG ratings for Tesla from three different providers, you will get three meaningfully different answers. This is not a bug in the system. It is the inevitable result of measuring something genuinely complex using different frameworks.
Remote work has weakened traditional analysis signals like office lease data while strengthening digital signals like job postings, employee distribution, and technology stack choices.
Automation handles data collection and pattern detection. Humans handle interpretation and strategic implications. Neither alone produces great analysis.
AI gets company analysis wrong in specific, predictable ways. Entity confusion, temporal errors, and source conflation produce polished output that looks correct but is not. Here is how to catch it.
A bad client can cost more than they pay. The agencies that grow profitably vet potential clients with the same rigor clients use to evaluate agencies.
Professional services firms are unusual because their primary assets walk out the door every evening. But the right indicators can reveal business health with unusual clarity.
Coaches working with small business owners often rely on the owner's narrative alone. Access to independent company diagnostics changes the coaching conversation entirely.
Analyzing private companies presents unique challenges due to limited public data. Discover strategies and tools for evaluating non-public companies with confidence.
