EN DE

Embed Fair Value data

Every way to use our fair-value data elsewhere: as a ready-made block on your own website, as a table in Excel or Google Sheets, as a dataset for your own analysis, or straight inside your AI assistant. Over 35,000 stocks, updated daily, free to use with attribution (CC BY 4.0). No key, no sign-up.

1 · Show it on your own website

The simplest route, no coding: copy the code, paste it into your page, done. It is plain HTML, so it works in WordPress, Wix, Squarespace, Notion or a newsletter, with no script and no changes to your stylesheet.

Fair-value badge for one stock

Shows fair value and the gap to the price. Dropbox as an example. This is how it looks:

Dropbox Inc · Fair Value
$50.52 +77%
Fair Value
Calculator →

Copy this code (click selects everything):

For a SPECIFIC stock: open that stock page and use its “Embed” box, then the badge carries its numbers.

List of undervalued quality stocks

The current top 5 as a compact list:

Most undervalued quality stocks
  1. Lululemon Athletica Inc.+198%
  2. CGI Inc+146%
  3. China Resources Land Ltd+137%
  4. Gree Electric Appliances Inc of Zhuhai+127%
  5. Haier Smart Home Co Ltd+121%
See all · Fair Value Calculator →

Note: the snippet is a snapshot. For permanently fresh numbers, copy the code again now and then, or read the datasets below automatically.

2 · In Excel or Google Sheets

One line into an empty cell and the list appears as a table, refreshable with one click. No download needed.

Google Sheets

=IMPORTDATA("https://www.fairvalue-calculator.com/undervalued-stocks.csv")

Excel

Data → From Web → paste the address above. Excel creates a refreshable connection.

3 · Download the datasets

Five ready-made, daily-fresh lists. Choose the format via the extension: .json or .csv. No sign-up, no key.

Undervalued quality stocks

40 stocks

Big names trading below our fair value, with a quality score. Optional sector filter: ?sector=technology

Show fields
FieldMeaning
tickerExchange ticker symbol
companyCompany name
upside_pctGap from price to our fair value in % (positive = undervalued)
quality_scoreQuality score 0 to 100 (profitability, growth, balance sheet, moat)
price_localCurrent price in the listing currency
evidenceConfidence tier: low, medium or high
sectorGICS sector
countryListing country
urlLink to the stock page

Overvalued stocks

Same calculation, other direction: stocks well above our fair value.

Show fields
FieldMeaning
tickerExchange ticker symbol
companyCompany name
upside_pctGap from price to our fair value in % (positive = undervalued)
quality_scoreQuality score 0 to 100 (profitability, growth, balance sheet, moat)
price_localCurrent price in the listing currency
evidenceConfidence tier: low, medium or high
sectorGICS sector
countryListing country
urlLink to the stock page

Value traps

Optically cheap but weak quality. The dataset nobody else publishes.

Show fields
FieldMeaning
tickerExchange ticker symbol
companyCompany name
upside_pctGap from price to our fair value in % (positive = undervalued)
quality_scoreQuality score 0 to 100 (profitability, growth, balance sheet, moat)
price_localCurrent price in the listing currency
evidenceConfidence tier: low, medium or high
sectorGICS sector
countryListing country
urlLink to the stock page

Largest companies by country

520 companies from 42 countries

The 10 largest listed companies per country by market cap in USD, 20 for the ten largest markets, plus fair value and quality score. One row per company, home listings only. Optional single country: ?country=DE

Show fields
FieldMeaning
countryCountry code (ISO-2) of the home listing
country_nameCountry name
rankRank within the country by market cap
companyCompany name
tickerTicker
symbolTicker with exchange code, e.g. SAP.XETRA
exchangeExchange
market_cap_usdMarket cap in USD: price x shares outstanding, reproducible from the fields in this row
market_cap_reported_usdMarket cap as reported by our data vendor (a snapshot, often older)
market_cap_ratioRatio of the two figures, 1.00 = agreement. Rows beyond a factor of 2 are dropped
shares_outstandingShare count we calculate with
currencyListing currency (price and fair value are in this currency)
priceLatest closing price in the listing currency
previous_closePrevious closing price
change_1d_pctOne-day change in percent
price_dateTrading day the price belongs to
websiteCompany website (use it with any logo service you like)
fair_valueOur fair value per share, null when we withhold it
quality_scoreQuality score 0 to 100
valuation_statuspublished or withheld
sectorGICS sector
as_ofSnapshot date of the dataset
urlLink to the stock page

The 150 largest companies worldwide

150 companies

The same fields without the country grid: the 150 largest listed companies by market cap in USD, across all markets.

Show fields
FieldMeaning
countryCountry code (ISO-2) of the home listing
country_nameCountry name
rankRank within the country by market cap
companyCompany name
tickerTicker
symbolTicker with exchange code, e.g. SAP.XETRA
exchangeExchange
market_cap_usdMarket cap in USD: price x shares outstanding, reproducible from the fields in this row
market_cap_reported_usdMarket cap as reported by our data vendor (a snapshot, often older)
market_cap_ratioRatio of the two figures, 1.00 = agreement. Rows beyond a factor of 2 are dropped
shares_outstandingShare count we calculate with
currencyListing currency (price and fair value are in this currency)
priceLatest closing price in the listing currency
previous_closePrevious closing price
change_1d_pctOne-day change in percent
price_dateTrading day the price belongs to
websiteCompany website (use it with any logo service you like)
fair_valueOur fair value per share, null when we withhold it
quality_scoreQuality score 0 to 100
valuation_statuspublished or withheld
sectorGICS sector
as_ofSnapshot date of the dataset
urlLink to the stock page

Provenance: our fair value from multiple models on company-reported fundamentals (regulatory filings) plus third-party market data. Update frequency: daily. Every response carries snapshot_date and updated_at. Where we withhold a valuation, you get null instead of a number.

4 · In your AI assistant (ChatGPT, Claude and others)

Connect Fair Value Calculator to ChatGPT, Claude, Perplexity and others via our MCP server. The assistant can then resolve a stock, fetch a preview and a fair-value verdict, and compare two stocks.

Set up integrations → Research datasets (CC BY) → Datasets on GitHub →

5 · In your own code

curl

curl -s https://www.fairvalue-calculator.com/undervalued-stocks.json

Python (pandas)

import pandas as pd
df = pd.read_json("https://www.fairvalue-calculator.com/undervalued-stocks.json")
print(df["stocks"].apply(pd.Series)[["ticker","upside_pct","quality_score"]].head())

JavaScript

const r = await fetch("https://www.fairvalue-calculator.com/undervalued-stocks.json");
const { stocks } = await r.json();
console.table(stocks.slice(0, 10));

Google Sheets

=IMPORTDATA("https://www.fairvalue-calculator.com/undervalued-stocks.csv")

GitHub

All datasets are also a Git repository to clone and track (including market valuation by sector and country): github.com/petz00711/fvc-open-data

License & citation

Free to use under CC BY 4.0. Attribution is appreciated, a plain-text credit or a link is fine:

Source: Fair Value Calculator (fairvalue-calculator.com), accessed 2026-09-09.

Rate limits protect the service; for higher frequency or a custom cut, write to [email protected].

Research & data

Dated data studies from the Fair Value engine, free to cite with attribution. Every study ships methodology, limitations and a CSV download.

Global Equity Fair Value Report September 2026
Global market vs aggregate fair value: +34.6 %
Global Equity Fair Value Report August 2026
Global market vs aggregate fair value: +34.6 %
Global Equity Fair Value Report July 2026
Global market vs aggregate fair value: +33.8 %