January 2019 Data Update 3: Playing the Numbers Game! Every year, going back three decades, I have spent the first week of the entire year, looking at amounts. Specifically, the year ends as the calendar, I download organic data on individual companies and try to decipher patterns and developments in the data. Over the full years, the raw data has become more easily accessible and richer, but ironically, I have become more apprehensive about trusting the true quantities.
In this post, I will describe, in broad terms, what the data for 2019 appears like, in conditions of geography and industry, and spend the next few content eking out as much information as I could out of them. My sample includes all publicly traded firms with a market capitalization higher than zero and every one of the information that I get from my data providers is within the public website. Put in a different way, for a person firm, you should be able to extract all of the given information that I have for the firms in my sample, and compute the figures and ratios that I do, if you are so willing.
For my 2019 data update, I have 43,846 firms in my test. Share a reliance on natural resources. Includes riskier EU countries, but shows European company options and prices. A mixed bag of countries from many regions with different characteristics really, with variations in the added risk. Different enough from the rest of the world that it deserves its own grouping still.
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Accounts for the largest chunk of world market capitalization. I shall confess up front that there is a component of arbitrariness to this classification, but no classification will be immune compared to that subjectivity ever. US companies are the leaders in the market capitalization race still, accounting for 38% of the overall market value. While emerging market firms account for half the companies in my overall sample roughly, their market capitalization is 30% of the entire global market capitalization.
The growing market grouping includes firms from four continents, listed in countries that range in risk from low risk to extraordinarily risky. Both biggest emerging marketplaces, in conditions of market and listings capitalization, are India and China and I am going to break out companies outlined in those countries separately for computing my figures.
To classify companies into commercial groups, I focus on the industry entries provided by my fresh data providers but add my own twist to make industry groupings. One reason that I really do so is to respect my fresh data providers’ proprietary classifications and the other is to compare across time, since I have classified companies with my groupings for decades. Law of good-sized quantities: The power of averaging gets more powerful, as test sizes increase, and using broader groupings leads to larger examples.
To illustrate, I’ve 1148 apparel firms in my global sample, thus allowing for enough firms in every sub-grouping. Better measures: In both valuation and corporate finance, there can be an argument to be made that the numbers we obtain for broader groups is a better estimate of where companies will converge than concentrating on smaller groups.
That said, you will see times where the broad industry classifications that I use will frustrate you, especially on pricing metrics, like PE EV and ratios to EBITDA multiples. I report the industry average PE EV and ratios to EBITDA multiples for specialty retailers collectively, but if you are valuing a luxury retailer, you would have liked to see these averages reported for luxury merchants just. I apologize in advance for that, but the consolation prize is that if you want to compute an average across a small sample of companies just like yours, the data to take action is available online and often for free.
In amount, I break companies into 94 industries and you will see the amounts of firms and market capitalizations of every industry in this document. While I used to provide company-level data until 2015, my natural data providers have put restrictions on that and I could no longer do this. If you’re interested in finding out which industry grouping a particular company that you will be interested in belongs to, you will get out by downloading this file.
Finally, I split financial service firms from the rest of the sample in computing my market-wide statistics, since they are so different that including them will skew the true quantities. You can view for yourself how a lot of an improvement this makes. I download data from both accounting statements and financial marketplaces and in doing so, I do run into a gentle timing issue. The accounting data that I have for most firms on January 1, 2019, is as of the third one-fourth of 2018 (closing September 30, 2018) and I use the trailing 12-month data as of the newest financial processing.