Probably nobody ever says “Initial Public Offering” but for a title “IPO” would be a bit short, so I decided to use the full description instead of the abbreviation.
This post is about preparing the next graph. Because of this no graph is available yet. It will come in a next post as this really is an adventure and I don’t know the results at all. Of course in the end a graph will appear, but I don’t know that will be in it exactly, except that it will be about finance.
Last week I was thinking about another interesting subject for a new post and I then a couple of IPOs discussed during the year came to my mind. I wondered how to get a good overview of recent IPOs and fortunately I found a nice site with a lot of information called IPOScoop.
Photo by AhmadArdity on Pixabay
There I selected the most recent 100 at this moment in time – all in 2019 (between the 14th of February and 3rd of October). That’s not a well-chosen threshold, but I expected to be enough interesting cases to be found in a set of one hundred. I started reading about a couple of them, suddenly realising it would take ages to go forward like that. A good set of thresholds for different values would allow me to weed out a couple of companies immediately and getting some numbers for the remaining group. Then an additional set of thresholds on the properties of the survivors (in my selection that is) would allow me to select an interesting set.
Which choices did I make? Well, the first one was simple. I took the companies with the most extreme changes in the value of their stocks. IPOScoop presents a “return” based on the current value of a share and the price at the end of the day of the IPO. I selected a group with a very negative return (< -40% or below) or a very positive one (> 60%). These values reflect more or less whether the people like the shares and it’s similar to a fashion show, with the companies on the catwalk.
My range is an asymmetrical one because the final was unbalanced at first. Because of this I had to adjust my original (absolute) values of 50% at both sides. Then other criteria were chosen, to pick the most interesting ones:
- The company should be founded in or before 2015, to have a reasonable period to investigate the financial data. The very young ones could show one-off results.
- The number of employees should be 100 or higher. With a lower number the impact of employees coming and leaving would be too substantial, although the threshold is arbitrary.
- The revenue should be 10 million USD at least. Originally I took out the ones without revenue. No profit or a loss is fine, but no revenue is a bit tricky to show in a graph. Later I raised the bar a bit and added another one:
- The revenue had to be below 1000 MM$ to get a balanced graph.
The original set had a top 15 and bottom 15. The screenshot is not clickable, but you can easily reproduce the selection. After the adjustments and additional selections only a bottom four and a top fiver remained (from the “return” perspective as explained above).
The remaining set is shown below. I put in all the hyperlinks for their IPOScoop-page:
Next time this set will be investigated more thoroughly. There will be some interesting financial data to work with and those will be visualised in a 3D-graph. Of course it will be generated with the help of the AnRep3D-generator.
If you are impatient, you can fetch the company-figures yourself and put them in the free demo-version of the AnRep3D-generator. It’s fully functional, yet limited to one line of data (meaning that only one building will appear, representing one company in a specific year or quarter). You don’t even have to give us your email-address to be able to download a free demo, since we believe that the true visionairs will ask for a licence anyway.
If you don’t know about the 3D-graphs yet, please have a look at our youtube-channel, showing all details of AnRep3D in short videos of a couple of minutes. And of course you can visit our website and follow us on Twitter: @AnRep3D