At first exposure these datasets are almost overwhelming but then....wow.
From Aswath Damodaran's Musings on Markets blog, Jan 5:
January 2018 Data Update 1: Numbers don't lie, or do they?
Every year, since 1992, I have spent the first week of my year, paying homage to the numbers gods. I collect raw accounting and market data from a variety of raw data providers, and I am grateful to all of them for making my life easier, and I summarize the data on many dimensions, by geography, by industry and by market capitalization. That summarized data, for the start of 2018, can be found on my website, as can the archived data from prior years.
The What?My dataset includes every publicly traded firm that has a market price available for it, in my raw dataset, and at the start of 2018, it included 43,848 firms, up from the 42,678 firms at the start of 2017. To the question of why I don't restrict myself to just the biggest, the most liquid or the most heavily followed firms, my answer is a statistical one. Any decision that I make on screening the data or sampling will create biases that will color my results, and while I will not claim to be bias-free (no one is), I would prefer to not initiate it with my sampling.
There are 135 countries that are represented in the data, though many have only a handful of firms that are incorporated there. That said, it is worth noting that while the companies are classified by country of incorporation, many have operations in multiple countries. I have classified my firms into five "big" groups: the United States, Europe (EU, UK), Emerging Markets, Japan and Australia/Canada/New Zealand. The pie chart below provides the breakdown:
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| Download spreadsheet |
Since the emerging market grouping includes firms from Asia, Latin America, Africa and Eurasia, I also have the data for sub-groups including India, China, Small Asia (other than India, China and Japan), Latin America, Africa & MidEast and Russia/Eurasia. That is pictured in the second pie chart above....MUCH, MUCH MORE (so much more)
Within each geographic group, I break the companies down into 94 industry groupings and the numbers in each grouping are summarized at this link. While some would prefer a finer breakdown, I prefer this coarser grouping because it allows for larger sample sizes, especially as I go to sub-groups. Finally, I compute a range of numbers for each grouping, reflecting my corporate finance biases, and classify them into risk, profitability, leverage and cash return measures in the table below:...
The two posts that preceded the hiatus:
Oct 24
The Bitcoin Boom: Asset, Currency, Commodity or Collectible?
Oct 27
Bitcoin Backlash: Back to the Drawing Board?
