Visualising a mix of large companies in a spatial graph (I)

Before the “abuse” of  the AnRep3D generator to visualise the average, individual energy-usage in different countries, normal AnRep3D spatial graphs were shown and discussed. With “normal” I mean visualising key financial data from the annual report (or more precisely: the Financial Statements in the Annual Report).  That’s what AnRep3D was developed for, although derivatives for other purposes could exist in the future, hence the demonstration of the “abuse”.

This time we go back to the original purpose, like we did in the series about relatively small companies. The difference is, the companies picked now are much larger. Tesla, EasyJet and USG People have revenues with a magnitude of billions, but the companies I selected this time, have annual revenues of tens of billions. At the same time, the AnRep3D-graphs are relative and a graph comparing small companies only won’t be very different from a graph comparing large companies. In a mix the difference would be huge, of course. Much like we saw with the energy-graphs, where the USA and India were in the same graph.

AnRep3D is all about comparing companies by looking at relative dimensions. It’s not important how large a company is, but the ratio of profit to revenue is, as is the ratio of equity and total assets (and e.g. the ratio of the revenue to the total assets for that matter). Even more important is the possibility to see companies change over time.

For this new series, I picked five very large companies in Europe and the USA: Tesco, PSA, E.on, Maersk and IBM. The most recent year – 2016 – is interesting but 2006, before the onset of the crisis, would make a good comparison. So I had to select ten sets of data: two years for five companies. Each set consists of four values: revenue, profit, equity and (total) assets. The abbreviation being R-PEA (i don’t know a real “pea” of this type, but its rather easy to remember).

For now I won’t present anything, except for the raw data I took from the annual reports (still a mix of USD, GBP and EUR). Some annual reports have a slightly unusual format and I don’t hope I made a mistake in picking the right values. Yet a warning: don’t take my numbers to work with when looking for financial information. Always go back to the source and look for yourself. If you want to visualise the data, please purchase an AnRep3D  licence 🙂 or download the free demo-set (only one “building”) [update: instead of the demo-generator the fully functional 3D-graph generator is now available without any charge].

raw input data large companies

About AnRep3D

AnRep3D is the new company, founded after the handover of Scientassist (together with VRBI) to one of my sons. From now I will focus on three-dimensional graphs for the financial markets, showing the main figures from annual reports in comparison. As per 2021 a second product is available: EnRep3D. It is meant to visualise energy. Although the engine is the same, the texts, manual, website and examples (including blogposts) are focused at energy.
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1 Response to Visualising a mix of large companies in a spatial graph (I)

  1. Pingback: Starting a new series, using the demo-generator | Annual reports presented as 3D graphs

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