InsightDCF: Open-model DCF analysis
Institutional-grade discounted
cash flow, for everyone.
Enter any public ticker. The model auto-detects industry terminal assumptions from Damodaran's NYU datasets, scores moat durability, and builds a 10-year DCF — all visible, all editable, all explainable.
— By Seymur Mammadov
Damodaran-anchored terminal assumptions
Industry terminal margins, WACC, D/E, and betas pulled live from NYU Stern datasets — the same source used by professional valuation practitioners.
Moat-scoring convergence detector
ROIC-WACC spread, gross margin trends, and reinvestment efficiency combined into a scoring system that determines when a given company's financial metrics start converging towards the industry average.
Fully editable projections
Every assumption is surfaced and editable. Change year-by-year growth rates, margins, or capital structure — the model re-runs instantly.
Monte Carlo + sensitivity analysis
5,000-draw simulation with correlated growth/margin shocks. Three sensitivity heatmaps across WACC, terminal growth, ERP, margin, and tax.
AI-generated plain-English explanation
Claude Haiku reads the model's own computed outputs and explains why the numbers came out the way they did — no hallucinated facts.
Equation flowcharts
Every major calculation chain visualised as an interactive flowchart: beta → WACC, EBIT → FCFF, Gordon growth, equity bridge, and more.
About Me
From my first DECA competition as a high school sophomore, I knew I was interested in finance. What started as a senior year project made to combine my interests in computer science and valuation quickly became something much more personal: building a DCF engine I could actually trust.
Every edge case I encountered showed me how much more there was to refine. Assumptions had to be tested, outputs had to be challenged, and every improvement revealed another layer of complexity behind what seemed to me at first as a mathematically simple model. After hundreds of hours of development, iteration, and testing, this platform became the result of that process.
Seeing the gap between market prices and underlying business value grow in recent years pushed me to share my work beyond a personal project. My goal is to create a tool that gives students an interactive way to learn financial modeling, provides investors with an accessible valuation framework that is as automated as they want it to be, and gives professionals the flexibility to build their own assumptions into a rigorous system.
For educational and informational purposes only. Output is a model estimate, not investment advice. Past financial performance does not guarantee future results. Always do your own research before making investment decisions.
Recent updates
View all →2026-06-20
Impairment-aware EBIT normalization (write-off detection)
Reported operating earnings often fold in one-time, non-cash charges like goodwill impairments and restructuring write-downs that say nothing about how the business actually earns. Kraft Heinz is the clearest case. After writing down billions in post-merger goodwill, its most recent reported operating margin came in around negative 18%, even though the underlying business still earns close to 20% margins and produces positive cash. Anchoring a ten-year forecast on that negative number turned a stable consumer-staples company into something that looked permanently unprofitable. The engine now spots these charges by their fingerprint. A write-off leaves gross margin alone, since the cost of goods doesn't change, but it collapses operating margin, since the charge lands in operating expense. Any year where that gap blows past the company's own historical norm, or where operating profit goes negative while gross margin stays healthy, gets flagged and dropped, and the forecast anchors on the remaining clean years. Because the starting margin feeds everything downstream, the effect is large. It lifts projected after-tax operating profit, pulls return on capital back out of false-negative territory, and stops the terminal value from dividing by a broken number, a failure that previously aborted the valuation outright. The filter stays conservative on purpose. A company with thin gross margins and broad losses trips neither test and keeps its real losses, so the model strips accounting noise without hiding actual trouble.
2026-06-19
Marginal vs. average return on capital for terminal value
A company's average return on invested capital divides profit by every dollar of capital ever put to work, including goodwill from old acquisitions. For companies built through M&A, that average stays permanently weighed down by premiums paid years ago. Kraft Heinz earns about 3% on its goodwill-heavy capital base, and Caterpillar shows the same pattern, so the model originally assumed both destroy value forever and priced them well below the market. The fix separates two different questions. Future growth is paid for with new capital, not by re-buying old goodwill, so the terminal value now sizes its reinvestment off the return on the next dollar invested (operating capital efficiency times margin times one minus tax), rather than the historical average. Caterpillar's marginal return came out near 23% against a 17% average, and Kraft Heinz near 16% against 3%. That change roughly quadrupled Kraft Heinz's intrinsic value and lifted Caterpillar's as well, without turning into a blanket giveaway. A company with poor unit economics, meaning thin margins and low capital efficiency, still produces a marginal return below its cost of capital and stays cheap. Only companies whose average is dragged down by sunk goodwill get the adjustment, so the large share of public companies that really do fail to earn their cost of capital are not flattered. The average return is kept and still drives the competitive-advantage logic described further down.
2026-06-18
Goodwill-stripped capital-efficiency convergence
The model assumes a company's capital efficiency, meaning the revenue it produces per dollar of invested capital, drifts toward its industry average over time. But reported efficiency can be thrown off by goodwill sitting on the balance sheet, which inflates the capital base without reflecting how the operations actually run. An acquisition-heavy company can look far less efficient than it is. The terminal anchor now strips goodwill out, taken as the current balance since goodwill is a standing figure rather than something that accumulates, to get operating capital efficiency, then blends that toward the industry average. This grounds the long-run assumption in operating reality rather than an accounting artifact, and avoids the opposite error of assuming a company snaps all the way to the industry figure. The blend pulls in both directions, so a goodwill-burdened laggard isn't assumed to fully catch up and an efficient operator isn't assumed to fully lose its edge. Since capital efficiency drives both the reinvestment schedule and the marginal return on capital, this one anchor moves the explicit forecast and the terminal value together. It is gated on acquisition behavior. If goodwill is still climbing, the company is still buying growth and the reported drag is treated as real, while a stable or shrinking goodwill balance signals a company that has finished acquiring.
2026-06-16
Industry-classification hardening
Every terminal assumption — margin, beta, leverage, tax rate, cost of debt, capital efficiency — is pulled by matching the data provider's industry label to Damodaran's industry tables. Left to plain fuzzy string matching, that step fails in two quiet ways. Microsoft's provider label carried an em-dash in "Software—Infrastructure" while the lookup key used a regular hyphen, so the correct override silently missed and the company fell through to a worse guess. Separately, fuzzy matching can pick a label that shares letters but not meaning, pulling in the wrong industry's economics with no warning at all. Three layers now sit in front of the match. A hand-built override map covers known provider-to-Damodaran naming gaps across all eleven sectors. A normalization step collapses dash and spacing variants so character differences can't break a lookup. And a confidence score flags any weak fuzzy match for review instead of trusting it blindly. This matters because one bad industry match corrupts the entire terminal block at once — every assumption listed above — so it is one of the most consequential reliability fixes in the engine. Fixing the Microsoft dash mismatch alone restored the right set of terminal inputs.