@AswathDamodaran,
www.damodaran.com
(full post)
For the last two decades, I have dedicated the first two weeks of each
new year to a ritual. I obtain/collect/download data on all publicly
traded companies listed globally, using a variety of data sources, and
then analyze and present the data, aggregated at a number of different
levels: by country, by region (US, Europe, Emerging Markets, Japan,
Australia & Canada) and by industry. I report on measures of
operations (profit margins, turnover ratios, working capital), measures
of leverage (debt ratios), measures of risk (beta, standard deviation,
equity risk premiums, country risk premiums) and pricing measures
(earnings multiples, book value multiples, revenue multiples). I just
completed my 2013 update and you can find it by clicking here.
I start with a belief that all data should be accessible and available
to all investors at low or no cost, but my motives for providing my
reading of the data are far from altruistic. I draw on the numbers that I
estimate through the rest of the year for my teaching, analysis
(valuation or corporate finance) and writing (blogs, books). In other
words, I would have analyzed all of this data anyway and having
completed the work, I see little benefit in keeping it behind a pay wall
or passwords. Let me hasten to add that nothing that I do is
particularly original nor is it path breaking and my task is made easier
by the easy access that we have to raw data. I do hope, though, that
while I do make mistakes, that I have not let my personal biases or
views color the data, and that that nothing that I do is opaque.
Each year, I also try to add something new to the dataset to keep it
fresh and this year, I have added company-specific estimates of costs
of equity and capital (in US dollar terms) in the individual company
data sets (look to the top of the linked data page). In making these
estimates, though, I had to make very broad assumptions about country
risk. For instance, I used the risk premium of the country of
incorporation to the company, though it is preferable to use the risk
premium based on operations. So, take these cost of capital estimates
with a grain of salt, and if you prefer a more precise estimate for a
company, you should do in more detail.
When I finished my update a year ago, I posted on it here,
and talked about one of my favorite movies/books, Moneyball, in the
context of arguing that intuition & experience were vastly overrated
in business. Much of what we think we have learned or think we know
about investing and corporate finance is skewed by psychological flaws
that we all share: faulty framing, hindsight bias and selective memory,
and good data can play a cleansing role. That post represented the
“good” that I see in data/numbers, and I thought that this year’s post,
for balance, should offer the other side of the argument. 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. In
particular, here are three practices relating to data that I find
distasteful and suggestions on how you can counter them.
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
alway 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.
I hope that you find my data useful in your work or research. If you do,
that is more than sufficient return on any time that I have invested in
putting it together. If you can think of ways in which it can be more
useful or complete, please do let me know and I will try my best to
incorporate those suggestions into next year's update.
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The
2013 John D. McCarthy Award for Lifetime Achievement in the Scholarship
of Social Movements and Collective Behavior will be awarded to David Snow for
his extraordinary achievements in research and the role that he has
played in mentoring successive generations of scholars both within and
outside of his own department. This year's award ceremony and public
lecture will be on May 4, 2013 at the University of Notre Dame. 
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