In this version of Notes from MIAM Research Desk we turn our attention to data. The Merriam-Webster Dictionary defines data as “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.” Although Merriam-Webster doesn’t ask for our approval before defining words, we agree with their definition. We first ensure that our data is factual. Then we use the data for rigorous reasoning, discussions (lots of them), and calculations.
There are three primary ways that our research team interacts with data. First, we gather data. Second, we analyze data. And third, we act on data. Because each way we interact with data is complex, we will focus our current attention on the first step: how and why we gather data.
As a research team we encounter lots of types of data: “Big data,” simple data, colorful data, data in Excel, data in written stories, data from company management (see our notes from June 30 and July 20 for more detail on these interactions), new data, old data, un-translated data, and — worst of all — bad data. However, to say that we “encounter” data is an intentional misnomer because we seek data with intent rather than encounter it by happenstance.
Our primary goal when we gather data is to obtain it directly from the best source possible. Ideally, we would conduct our own surveys and observations, but there are practical time and geographic limitations that prevent us from doing that. Instead, we go to the source that is the closest to the subject matter that the data set measurers or describes. We do this so that we can draw our own conclusions on what the data tells us rather than have an intermediary color our analysis. While some media outlets and sources attempt to change little when they report data, even those well-intentioned sources can change the physical layout in which data is reported, add additional graphs, omit some data, or make scrivener’s errors that result in the wrong information being relayed. For these reasons, we go directly to the source to reduce the possibility of bad data to the lowest practicable level.
This philosophy of data gathering results in hyper diligence from each of our team members individually (we each subscribe to and read different sources of information about the world and portfolio companies) and as a team when it comes to “encountering” data. Individually, we all ensure that the apps, websites, broadcast media, and newspapers we read provide us with good data. We are agnostic when it comes to the analysis or opinions of the writers or editors that produce the media. In fact, we encourage different or contrarian viewpoints, especially where they challenge our own thinking, and the geographical differences of where our team members live and work avail them to different sources. However, we have little tolerance for manipulation or obfuscation of facts and will discard any sources that do so.
As a team, we review data only from sources that have proven over time to be accurate and reliable. Macroeconomic and industry data is typically drawn from reliable government and third-party sources like the Bureau of Labor Statistics (unemployment, job openings), the Energy Information Administration (crude reserves, energy trading), Fearnley (maritime shipping rates, vessel sales), or Baker Hughes (oil rig count, new oil field developments). Each week, three of our team members curate a wide-ranging selection of reports on housing, consumer spending, treasury activity, international trade, oil rig counts, and vehicle sales, among many other topics. Our team then meets for a review of the data and a discussion about the conclusions or observations that are drawn.
For data on companies, we almost exclusively review filings with regulatory bodies or press releases directly from the companies. Again, our emphasis is on reading data directly from the source to reduce the imposition of someone else’s analysis or a distortion of the data itself. There is an incredible amount of information contained in a company’s 10-K, 10-Q, 8-K, Form 4, 13D, other public filings, and press releases. While we are quick to question any irregularities or unclear issues on a company’s filing, the application of consistent and fair accounting rules, the oversight of regulatory bodies like the S.E.C., and the ability to use the courts as recourse for incorrectly published information all contribute to a repository of reliable data made up of company filings. Reading each company’s filings thoroughly allows us to exhaust that reliable resource before turning to other, less reliable sources for additional data. Rather than allow a data aggregator to use artificial intelligence to scrub information from the filings or get our information from another analyst who would necessarily impose their own view, each of our team members reads the filings themselves and forms their own analysis of what the data is telling them. We are then able to discuss the data and resulting analyses as a team without non-team members as an intermediary, which allows the purest application of the team’s analytical abilities to the data produced by the company.
With that end goal in mind, the data that we seek shapes our thinking and our entire research process so it must be as reliable as possible, or it risks a shaky foundation for our analysis and decision making. Consequently, our team spends the necessary time and uses the appropriate diligence to gather accurate, reliable data and ensures that investment decisions are made based upon the best possible data in each situation.
As always, please reach out to MIAM if you are interested in partnering or you would like to discuss a particular company or idea.
The MIAM Research Team
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