PRpradeep1177·Jun 24, 2026Submission
Show HN: Empowering codex/Claude Code with Aswath Damodaran valuation thinking
2 points · Open discussionstockvaluation-io
stockvaluation_io
Java2026–2026
stockvaluation-io / stockvaluation_io
stockvaluation_io
Open-source, local-first MCP DCF valuation workflow that turns a stock ticker into transparent Damodaran-style assumptions, scenarios, and valuation outputs you can inspect, challenge, and refine.
2Distinct people
8Mentions
0Explicit recommenders
37GitHub stars
What to know before exploring
What it is
Open-source, local-first MCP DCF valuation workflow that turns a stock ticker into transparent Damodaran-style assumptions, scenarios, and valuation outputs you can inspect, challenge, and refine.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week8 dated mentions
Observed mentions: 2026-03-04 07:10:11 UTC to 2026-06-24 16:31:10 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 2 | 2026-03-02 00:00:00 UTC | 2026-03-09 00:00:00 UTC | 2 | 1 | 1 |
| Mar 9 | 2026-03-09 00:00:00 UTC | 2026-03-16 00:00:00 UTC | 5 | 2 | 3 |
| Mar 16 | 2026-03-16 00:00:00 UTC | 2026-03-23 00:00:00 UTC | 0 | 0 | 0 |
| Mar 23 | 2026-03-23 00:00:00 UTC | 2026-03-30 00:00:00 UTC | 0 | 0 | 0 |
| Mar 30 | 2026-03-30 00:00:00 UTC | 2026-04-06 00:00:00 UTC | 0 | 0 | 0 |
| Apr 6 | 2026-04-06 00:00:00 UTC | 2026-04-13 00:00:00 UTC | 0 | 0 | 0 |
| Apr 13 | 2026-04-13 00:00:00 UTC | 2026-04-20 00:00:00 UTC | 0 | 0 | 0 |
| Apr 20 | 2026-04-20 00:00:00 UTC | 2026-04-27 00:00:00 UTC | 0 | 0 | 0 |
| Apr 27 | 2026-04-27 00:00:00 UTC | 2026-05-04 00:00:00 UTC | 0 | 0 | 0 |
| May 4 | 2026-05-04 00:00:00 UTC | 2026-05-11 00:00:00 UTC | 0 | 0 | 0 |
| May 11 | 2026-05-11 00:00:00 UTC | 2026-05-18 00:00:00 UTC | 0 | 0 | 0 |
| May 18 | 2026-05-18 00:00:00 UTC | 2026-05-25 00:00:00 UTC | 0 | 0 | 0 |
| May 25 | 2026-05-25 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 1 | 2026-06-01 00:00:00 UTC | 2026-06-08 00:00:00 UTC | 0 | 0 | 0 |
| Jun 8 | 2026-06-08 00:00:00 UTC | 2026-06-15 00:00:00 UTC | 0 | 0 | 0 |
| Jun 15 | 2026-06-15 00:00:00 UTC | 2026-06-22 00:00:00 UTC | 0 | 0 | 0 |
| Jun 22 | 2026-06-22 00:00:00 UTC | 2026-06-29 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
8 sourcesRuns fully local:
https://github.com/stockvaluation-io/stockvaluation_io
In Open-source DCF engine based on Damodaran's datasets with LLM narrativesRuns fully local:
https://github.com/stockvaluation-io/stockvaluation_io
In Show HN: Self-hosted DCF workspace using Damodaran datasets, LLM narrativesShow HN: Self-hosted DCF workspace using Damodaran datasets, LLM narratives
I wanted a valuation tool I could actually audit. Every "AI stock
analysis" product I tried either hid the math or hallucinated the
inputs. So I built my own.
You type a ticker. You get:
- Intrinsic value via DCF using Damodaran's industry datasets
(betas, ERP, country risk premiums)
- Every assumption exposed — cost of capital, reinvestment rate,
terminal value, all of it
- LLM-generated bull/bear narratives with cited news sources
- The base case and the override case are shown side by side
The math is deterministic. The LLM handles research and narrative
Only, it cannot silently change the numbers.
Runs fully local, one Docker command:
https://github.com/stockvaluation-io/stockvaluation_io
Rough edges still. Curious what assumptions people would challenge, especially the terminal growth rate, and how to handle high-growth name
Read the rest
s where DCF tends to break down.
Open-source DCF engine based on Damodaran's datasets with LLM narratives
2 points · Open discussionFor years, I've wanted to automate Damodaran's valuation methodology industry betas, ERP, country risk premiums, the whole thing.
His data is free on the NYU page, but turning it into an actual valuation takes hours per stock.
StockValuation.io does it in minutes:
- Pulls live financials via yFinance
- Uses Damodaran's published datasets for cost of capital inputs
- Runs deterministic DCF math (every assumption is visible)
- Layers an LLM on top for bull/bear narratives and assumption overrides
The math never changes based on AI mood. The LLM only touches research and storytelling.
Self-host: https://github.com/stockvaluation-io/stockvaluation_io (one Docker command, MIT license)
Happy to discuss the methodology, assumption choices, or why I think Deterministic math + probabilistic narratives is the right architecture for this.
In Open-source DCF engine based on Damodaran's datasets with LLM narrativesI built a local-first stock valuation tool based on Damodaran's methodology
3 points · Open discussionBuilding a DCF model isn’t actually the hard part. The painful part is everything around it: finding industry averages, checking current risk-free rates, and digging through earnings transcripts to justify assumptions.
After repeating that process too many times, I started building a small tool to automate parts of the workflow while keeping the valuation model itself deterministic.
The core idea is to separate two very different problems:
1. Deterministic valuation math
DCF calculations should be reproducible and deterministic. The model handles the financial logic and keeps the valuation mechanics consistent.
2. Qualitative research and narrative building
Things like reading filings, summarizing earnings calls, or challenging assumptions are much more open-ended. That’s where LLMs are useful.
The tool is local-first and runs entirely on
Read the rest
your machine. There are no accounts or hosted services — you just bring your own API keys if you want to use LLM features.
It also comes pre-seeded with Professor Damodaran’s public datasets (industry margins, risk metrics, failure rates, etc.) so you don’t have to manually assemble those inputs each time you build a model.
One design constraint was making sure the AI never touches the core valuation math. LLMs are allowed to suggest assumptions or critique them, but the underlying calculations remain deterministic.
A useful side effect is that the AI often behaves more like a skeptical analyst than an oracle. For example, it might flag things like:
“This margin expansion assumption is outside the historical range for this industry.”
Which is often exactly the kind of pushback you want when building a valuation narrative.
Curious if others building investing tools have experimented with a similar separation between hard financial models and AI-assisted research.
Repo:
https://github.com/stockvaluation-io/stockvaluation_io