Each year, for the last 25 years, I have spent the first week playing Moneyball, with financial data. I gather accounting and market data on all publicly traded companies, listed globally, and then try to extract whatever lessons that I can from the data, to use in investing, corporate finance and valuation for the rest of the year. I report the data, classified by industry group and by country, on my website, in the hope that others might find it useful. While, like last year, I will be summarizing what I see in the data in a series of posts over the rest of January, I decided to use this one to both provide some perspective and cautionary notes not only on my data but on numbers, in general.

The Number Cruncher’s Delusions

In an earlier post on narrative and numbers, I confessed that I am more naturally a number cruncher than a story teller and that I have learned through experience that focusing entirely on the numbers can lead you astray in valuation and investing. In fact, as you read my posts on what the numbers look like at the start of 2017, it is also worth noting that I am, like all number crunchers, susceptible to three delusions about data:

  1. Numbers are precise: I say, only half jokingly, that when a number cruncher is in doubt, his or her reaction is to add more decimals, in the hope that making a number look more precise will make it so. The truth is that numbers are only as precise as the process that delivers them and in business, that makes them imprecise. Thus, when you peruse the returns on capital or costs of capital that I will be estimating and reporting for both companies and industry groups, please do recognize that the former is an accounting number, where discretionary choices on expensing and depreciation can translate into big changes in returns on capital, and the latter is market number, making it not only a moving target (as interest rates and risk premiums change) but also a function of my estimation choices as well as estimation error in estimating risk premiums and risk parameters.
  2. Numbers are objective: One of the resentments that number crunchers have about story tellers is that the latter indulge in flights of fancy and are unashamed about bringing their biases into their stories and through them into pricing and investing. The problem, though, is that numbers can be just as biased as stories, with the caveat that it is easier to hide biases with numbers. To give one example, one of the datasets that I will be updating has tax rates paid by US companies in 2016 and I provide three measures of effective tax rates, ranging from a simple average of effective tax rates across all companies in a sector, yielding the lowest values, to a weighted average effective tax rate that is computed only across money-making firms, which yields much higher values. If you are dead-set on making a case that US companies don’t pay their fair share in taxes, you will report only the first number and not mention the rest, whereas if you want to show that US companies pay their fair share and more in taxes, you will go with the latter. It is for this reason that I will not claim to be unbiased (since no one is) but I will try to provide multiple measures of widely used variables and leave it to you to decide which one best fits your preconceptions.
  3. Numbers put you in control: It is human nature to try to be in control and numbers serve us well, in that pursuit. As in other aspects of life, we seem to think that attaching a number to a volatile or uncontrollable variable brings it under control. So, at the risk of stating the obvious, let me say that measuring your return on invested capital is not going to turn bad projects into good ones, just as estimating your interest coverage ratio is not going to make it easier for you to make your interest payments.

Don’t get me wrong! I remain, at heart, a number cruncher but I have a more complicated, and healthier, relationship with data than I used to have. My faith in data has been tempered by my experiences with data, and especially so with the ease with which I have seen it bent to reflect the agenda of the user. I trust numbers, but only after I verify them, and I hope that you will do the same with the data that you find on my site.

A Big Data Skeptic

It is my experience with data that make me skeptical about two of the hottest concepts in business, big data and data analytics, at least as a basis for making money. It is true that companies are collecting more data than ever before on almost every aspect of our lives, with the intent of using that data to make more money of us. In a capitalist society, I remain doubtful that big data will be monetized, for three reasons.

  1. Data is not information: Not all data is created equal. Data that is based on what you do is worth a lot more than what you say will do; a tweet that you are bullish on Apple, Twitter or the entire market is less useful data than a record of you buying Apple, Twitter or the entire market. This is a point worth remembering as the rush is on to incorporate social media data (from Twitter and Facebook) with financial data to create super data bases. In addition, as we collect and store more data, it is worth noting that data is not information. In fact, if data analytics does its job, converting data to information will remain its focus, rather than generating neat look graphs and obscure statistics.
  2. If everyone has it (data), no one has it: For data to have value, you have to some degree of exclusivity in access to that data a proprietary edge on processing that data. It is one of the reasons that investors have been unable, for the most part, to convert increased access to financial data into investing profits.
  3. Not all data is actionable: , To convert that data to profits, you need to be able to find a way to monetize whatever data edge you have acquired. For companies that offer products and services, this will take the form of modifying existing products/services or coming up with new products/services to what you have learned from the data.

As you look at these three factors, it is easy to see why Netflix and Amazon have become illustrative examples for the benefits of big data. They get to observe us (as consumers) in action, Amazon watching what we buy and Netflix observing what we watch on our devices, and that information is not only proprietary but can be used to not only modify product offerings but to also nudge us to act in ways that will be beneficial to the companies. By the same token, you can also see why using big data as an investing advantage will, at best, provide a

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