▶ Full Post Text
I have been practising Damodaran-style valuation for a while now. What pulled me into it was the way of thinking.
Before I got into valuation properly, I used to look at companies in a very scattered way. I would read some news, look at revenue growth, check the stock chart, maybe read a few opinions, and then somehow form a view. It always felt like I was collecting information, but not really building a judgment.
Damodaran-style valuation changed that for me.
The thing I enjoy most is how it forces a story to become numbers. If I say a company is a great growth business, then I have to ask: where is that growth actually coming from?
If I say margins will improve, I have to ask: why would they improve, and how much improvement is realistic?
If I say the company has a strong moat, I have to ask: Does that show up in pricing power, margins, reinvestment efficiency, or lower risk?
If I say the market is wrong, I have to ask: which assumption do I disagree with?
That structure is what I enjoy. The number is almost the least interesting part if you cannot explain how you got there. The useful part is the discipline of slowing down and making your assumptions visible.
That is what I wanted to bring into my AI workflow.
Over time, I started using AI more and more for research. It is very good at helping me read faster, summarise filings, compare business segments, explain risks, and find what I may have missed. But I kept running into one problem.
Most AI stock analysis feels too smooth.
You ask about a company, and the model gives you a confident-looking answer. It sounds polished. It mentions the business, the risks, the market opportunity, maybe even valuation. But I often could not see where the assumptions came from. I could not tell what was researched, what was guessed, and what was just generated because it sounded reasonable.
That bothered me. So I started thinking about a different workflow.
\- What if the AI did the research and explanation, but the valuation structure stayed explicit?
\- What if the model helped me understand the business, but did not quietly invent the DCF math?
\-What if the assumptions were pulled out, challenged, changed, and recalculated?
That is what I tried to build with StockValuation.io. I basically converted my Codex / Claude workflow into a Damodaran-style valuation assistant.
The repo installs a [stockvaluation.io](http://stockvaluation.io) skill, exposes local MCP tools, and runs a local valuation service. The agent can research the company, understand the business, explain the story, and ask valuation questions. The local tools handle the actual DCF calculation and recalculation.
So the flow becomes more like how I personally want to think:
First, understand the business.
\- What does this company actually do?
\- What drives revenue?
\- What are the important segments?
\- What does the market believe?
\- Where could the market be too optimistic or too pessimistic?
Then, build the story.
\- Is this a high-growth company?
\- A mature cash-flow business?
\- A turnaround?
\- A commodity-like business?
\- A platform business?
\- A company with operating leverage?
\- A business where reinvestment matters more than margins?
Then, translate that story into assumptions. Growth, Margins, Reinvestment, Risk, Terminal value.
Then comes the useful part: challenge the assumptions.
\- If revenue growth is too aggressive, what happens?
\- If margins never reach the optimistic case, what happens?
\- If the company needs more reinvestment than expected, what happens?
\- If the terminal assumptions are too generous, what happens?
That is the part I wanted the AI to help me with. I want AI to help me ask better questions.
In the current workflow, the agent does not just produce a one-shot report. It researches first, builds a baseline valuation, forms an assumption view, and then stops to ask guided valuation questions before writing the final report.
That stopping point matters to me.
Because valuation is not only computation. It is judgment. For example, I want the assistant to say something like:
\- Here is the baseline.
\- Here are the assumptions driving the result.
\- Here are the business questions that matter.
Which assumptions do you want to challenge? That feels much closer to how I learn.
I also made the valuation math deterministic because I wanted to reduce the “AI magic” feeling. If a scenario changes, the recalculation should come from the valuation service, not from the model doing hidden math in the chat.
I have made mistakes explaining this project before. I think I described it too much like an “AI stock valuation bot,” and that gave the wrong impression.
What I actually care about is not stock picking. I care about using AI to practice structured thinking.
The project is open source here: [https://github.com/stockvaluation-io/stockvaluation\_io](https://github.com/stockvaluation-io/stockvaluation_io)
I am still improving it, but the current version is much closer to what I originally wanted.