Automated screening, monitoring of important events, alternative selection criteria and less time spent on manual analysis. We believe this matters to you when your working day is about getting into position to buy all or part of a company.
The investment mandate sets the terms for what you are able to invest in. In that reality, carrying the right selection all the way from screening to the sourcing phase becomes decisive. Screening is what makes sure you find the candidates for the best investment option. To succeed with the best investment, the first step is having the right and the best selection.
We have great respect for the fact that valuing a company is an "art". It is a discipline where long experience combined with technical methods and a range of skills and backgrounds in finance makes a difference. This is the expertise that makes each investment firm unique.
We are not going to spend time on disciplines like those, nor on M&A (mergers and acquisitions) or Due Diligence (full company analyses). Instead we will concentrate on automating the data access and the analyses that are decisive when screening and sourcing the strong candidates.
Automate the screening
As everywhere else, the challenge for an investment director is a shortage of time. Days are spent being pulled between the closing stages of one investment case while, in parallel, having to stay in touch with promising new leads for future investments. A data-driven approach is the solution. It creates more room to spend your time on the activities that really matter.
The other side of finding companies with varying degrees of probability of going bankrupt over the next 12 months is putting machine learning to work presenting companies based on criteria for succeeding with growth and improved profitability over time. In the figure above, this is illustrated with 200 likely candidates – straight out of Enin's database.
Monitor events
A great deal happens to those 200 candidates. Changes in management, ownership, mergers, demergers, restructurings and more, of course. We capture this through sources such as official announcements and ownership data. Everything then comes down to being early and acting on the information, which is why you have to keep track of the media – all of it. From websites to local newspapers that exist only in print. The information is turned into text that becomes the input for machine learning.
Which words are the important ones for the industry and for that one specific company? That is exactly the information I want delivered to me. As an alert, with a priority rating.
Keep it dynamic
What about company number 201, the one that just missed the cut? Things are happening to these companies all the time: some will drop out, and some do better than it looked when we first drew up our list. That is when number 201 should take another company's place. You also need to build dynamism into the companies that got far, the ones that almost made it to the final stage before an investment decision. Companies like that should carry a tag, an identifier telling us we have looked at them before, so it is easy to pull up the information we already had if the company reappears in our dynamic monitoring.
Automate steps in the sourcing
Not everything can be automated. Analysis and assessment also require conversations with key people at the company in question, but make a plan for everything that can be pulled from available data sources and for how it can be put to use directly.
Large parts of the competitor analysis can be automated. The same goes for the so-called "four-field analysis". All the information about the company, its owners, management, financial data and important news is in place before you start gathering the data that requires dialogue.
Minimise the manual work in Excel and PowerPoint
We know that part of the job is about presenting. And that means Excel and PowerPoint. There is no reason to resist it, but playing along is a success criterion.
From Enin's web portal, data and analyses are sent into Excel fully formatted, or, if you prefer, you can show the result by opening a presentation directly in PowerPoint. We take it as a given that processing and recording data should be possible directly in familiar applications, but on top of that we add formatting so you can present the analyses done in the system in the form you want.
Look for alternative selection criteria
It goes without saying that we should be able to pull out companies based on geography, revenue and profit figures, number of employees, other financial data and by using industry codes.
Companies are constantly evolving, though: they start up new business areas, wind down others and implement new business models. An industry code is therefore often out of date.
That is why Enin has developed alternatives, so you can select on the stated purpose of the business, or use news and look at which words a company is identified with.
Again, machine learning and algorithms that are trained and keep getting more accurate. On top of this, at Enin we build what we call the "Company Flag". We do this both to be able to single out companies where there have been major changes in employees, ownership, revenue and profit, and to catch anything else that is material, such as HSE or environmental requirements. It is precisely this that makes the selections you work with steadily more precise.
Ensure easy access to in-depth analysis
Once you are inside a company, it is important that you get an overview of the key elements first. Key financial figures, historical development, employees and ownership. From there you should be able to carry out in-depth analysis.
You can then again make use of the "company flag" that appears on a company if there has been notable news, official announcements or anything else associated with opportunity and/or risk.
From here you should be able to drill down into the information that stands out, which means you can come at it from several angles, including down to individual people, to build an overview of events, relationships, roles and so on.
If you need more, you can dive into the past few weeks of news. There you may find success factors you might not have spotted had they not been presented in the context of the very company you are analysing.
Contact us for access to our web portal or if you would like to talk about our experience with using data for investment purposes.
