v-metrics

context

Our journey begins with the seminal work of Pr. Aswath Damodaran, a world-renowned authority on finance and business valuation. Over the past 20 years, Pr. Damodaran has researched, collected, and made thousands of valuation metrics of publicly traded companies available. These metrics are part of an open data set.

challenge

While these metrics are available, they are scattered in more than 1,200 Excel files, making it very tedious to research, analyze, and visualize them. Moreover, it is not easy to get all the metrics per industry at once, as each file contains a subset of the metrics. As such, we launched this project to make these metrics accessible to everyone.

1'200+ raw excel files

These Excel files are the original raw data from Damodaran's website. They are organized by category and are free to use and download—which is great. Every year (in January), Pr. Aswath Damodaran adds new Excel files to his website that correspond to the previous year, gradually creating a homemade "metric lake". You can find these files via the link below.

extract, LOAD & transform

This is where our work started. We carefully extracted, harmonized, structured, and enriched metadata that can be used to filter and analyze metrics. We also performed basic data normalization on the data to prepare it for analysis. This process is run against a new data set once a year.

analytical-ready data set

From the previous ELT process, we created a fact table and dimension tables using the star schema and then merged everything into one large table. This table, stored as a CSV file, is used for queries, analysis, and metrics sharing. In the future, a database (eg. Snowflake or DuckDB) could be used to store and process data.

Multidimensional model

Our first output is a CubeWeaver "hypersheet" that works like a spreadsheet, but with an SQL database on the backend. In this use case, CubeWeaver is used to filter metrics. Note that it can also be used to develop and scale sophisticated multidimensional models. Outliers are neither detected nor removed from this model.

visual DATA APP

Tables are good to access the numbers, but visual analysis is best to understand them immediately and make decisions. That is why we took it a step further and developed an online visual data app using Streamlit. In this app, you have the option to remove/keep outliers.

context

If you need to value a private company, one of the methods is to look for market multiples or, even better, transaction multiples of comparable companies that you can use as a reference to value your company. These multiples are very useful to make market-based valuations.

challenge

Although market multiples are available for publicly traded companies, they are not appropriate for valuing small private companies. Transaction multiples are better, but difficult to find with traditional search portal like Google. This project aims to make it easier to find the two most important multiples: the Sales and EBITDA multiples.

Semantic search and AI

We partner with LinkAlong, a leading technology company based in Switzerland that specializes in developing innovative semantic search portals. LinkAlong has developed taxonomies, ontologies, and related semantic analytics that we can use to search for specific topics such as valuation metrics. They made this technology available to almost any type of companies or communities. To make this partnership viable, we set a business model that we think will be very interesting for the v-community.

our initial Focus

There are hundreds of metrics that can be associated with business valuation (see the Damodaran Project). To maximize impact and test whether this search service resonates with the community, we decided to focus on just two multiplies: the Sales EBITDA multiples. In the longer term, we could expand this to other metrics. The community will decide.

a Google-like portal

Yes, we like Google because it's so simple and so powerful. That's why we built the same minimalist style of search portal, but with more power in the search for valuation metrics. Why more power? because this portal does a deep search, it does not just process keywords or titles, it actually reads and processes all types of documents that exist on the Internet. That's why we can find what Google can not in terms of Sales and EBITDA multiples.