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3 Ways Data Is Used For Evil
Every year, legendary NYU finance professor Aswath Damodaran compiles, analyzes, and then publishes a tremendous amount of global securities data on his website.
Data is always up for interpretation.
And sometimes data is intentionally misinterpreted and misrepresented for the wrong reasons.
“I know that data can be misused and manipulated, and that some of my own data has been used to back up specious arguments in multiple settings,” writes Prof. Damodaran.
For this reason, he identifies three practices he finds “distasteful” and he offers ways on countering them.
From his Damodaran’s blog (emphasis ours):
1. Data to intimidate: An article in the Wall Street Journal pointed to fact that people who are unfamiliar with numbers tend to give them too much weight to them and are particularly swayed by “mathematical” arguments, even if they are nonsensical. It is this weakness that is used by some number crunchers to intimidate those that may not have the same degree of facility with numbers. I have seen corporate financial analyses and valuations where analysts use table after table of numbers, to bludgeon others into submission, using acronyms, jargon and greek alphabets to further the rout.
The counter: The best weapons against number intimidation are common sense and a focus on the big picture. I hope that having access to my data will give you some ammunition in this endeavor but having a solid grounding in first principles of valuation and corporate finance always helps.
2. Data to mislead: If you have access to a great deal of data, you can parse the data and choose pieces to back up a preconception or argument that you want to advance. A couple of years ago, the effective tax rates that I publish on my site, for US companies, were used by some to advance the argument that US companies were not paying enough in taxes. Looking at the 2013 update on tax rates, that number is low (14.93%), but it is the average effective tax rate across all US companies, including those that are money losing (and thus paid no taxes). Looking only at money-making companies, the average effective tax rate is 28.37%, and the weighted average tax rate is even higher at 30.05%. So, if you have an agenda, you can take your pick to make the argument that US companies pay too little, just enough or even too much in taxes.
The counter: While there is little that you can do to stop people from using data selectively, you can counter their arguments by presenting them with the numbers that they are ignoring. In fact, it was in response to the tax rate debate that I started reporting the average tax rates for money-making companies and aggregated tax rates in my datasets.
3. Data to deflect and evade responsibility: Many analysts use data to avoid making tough judgments about businesses or dealing with uncertainty. Thus, assuming that a company will earn a profit margin typical of the industry is much easier to do than analyzing its competitive advantages and estimating a margin, based on your assessment. Similarly, using a historical or a service supplied equity risk premium in valuation is far simpler than estimating one, based upon the macroeconomic risks that we face in markets today. In fact, using an expert or a service estimate of these numbers (using an equity risk premium from a data service like Ibbotson or even my website) allows analysts to claim immunity from errors and to pass the buck, if the numbers turn out to be wrong in hindsight.
The counter: I have absolutely no concerns about you borrowing data and spreadsheets from my website but please make them your own by adapting and modifying them to not only fit your needs to but also to reflect your points of view.
Read more at Prof. Damodaran’s Musings On Markets.