Sunday, January 13, 2013

Data Update 2013: The Dark Side of Numbers

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.

38 comments:

Student said...

Very generous of you, Professor. Thank you!

Anonymous said...

Prof, what is your thought on the next generation of investors. I just saw in one of the yahoo message boards..where there were few people urging not to vote for certain people and also disallow them with any benefit..
since more and more people, who invest in stocks..are going to message boards for whatever reason..

What do you think about kind of activity in the internet by people to encourage/discourage others to take certain action on a particular stock.

I am just thinking as 20 years ago, these things didn't happen..I wonder how the social structure of the investment world will change 5 years from now. For someone like me, it is extremely helpful. Thanks to the internet..that I'm following people like you.

I hope my question makes sense and apologize that it is off-topic to you original discussion.

Christoph said...

Dear Professor,

In your sheet

Earnings. Book Value and Sales Multiple Averages by Country

the aggregated (not average) values are all 0's. Is this an error or will they no longer be provided?

Loganthan said...

Dear Professor
Thanks for continuing to provide such useful data at no cost. It is invaluable to individual investors like me for whom access to (free) data is usually not available.

I was looking at your betaIndia and Indiacompfirm spreadsheets and find that the beta figures in Indiacompfirm spreadsheet in columns G and H are different from the beta numbers in the betaIndia spreadsheet columns C and F.

I would think the beta numbers (column H in Indiacomp and column C in betaindia) should be the same but I am probably reading the spreadsheets improperly and would appreciate your help / clarification.

Loganathan

fresno dan said...

very interesting and insightful. I am adding you to my FAVORITES! A high honor, that if you add 5>50$, gets you a small coffee at Starbucks...

Anonymous said...

Great information, thanks for providing! As for the cost of capital calculation (specifically referencing the US data compfirm.xls, columns CB-CE), can you explain the cost of debt calculation? E.g. the pretax cost of debt for Ford is 1.00% based on the spreadsheet, a lookup from the 3-yr standard deviation in stock price (column AK) to the table in G1:I9.

Aswath Damodaran said...

Anonymous,
Thank you for noticing the cost of debt. That was a screw up. It should be fixed now.

Aswath Damodaran said...

Loganathan,
A good catch. My mistake again. Should be fixed now.

Anonymous said...

Thank you for this historical summary.
When instead, you take all Yahoo downloads of publicly available historical data of stocks publicly listed on Nasdaq, the NYSE and the ASE, you end presently up with 5630 stocks, a little less than the number you quote for listed US companies. As it only takes a few minutes to collect this General Watch List, and not even an hour to collect all available historical exchange (non-fundamental) data back to 1962, it becomes an almost trivial exercise to back test any set of selection rules. You may add historical fundamental data and historical forward looking statements like analysts’ estimates and revisions thereof and even the entire collection of delisted stocks. One of the peculiar things with a back test on forward-looking statements is that if you would have known them a few days to a few weeks early, you really start to make money, and that does not appear to be the case with quarterly reports.

When you weekly randomly pick 50, 100, 500 or 1000 stocks from a collection of 5000 stocks over the past 50 years after eliminating stocks with an average daily trading liquidity of less than $1 Million, your CAGR of the equally weighted picks becomes somewhat larger than 17%/year independent of the number of picks. Hence, over the past 50 years, these random picks collected 100x more money than the S&P500 over the same period. On a semi-logarithmic scale, this performance approaches a straight line, even up to today.

The question that I have concerns the rigorous division into Sectors or Industries you use. Yahoo identifies 8 market sectors and 215 industry categories. My findings show that any limitation in market sectors and industries lowers the annual returns of the random picks. I understand that profitability is different in different industries, and I also understand various valuation models. But do you think that share prices really reflect such valuation models?

Petr said...

Thank you, Aswath. Very cool!

Marketing Report said...

The marketplaces are the same now as they were 5 or 10 decades ago because they keep changing-just like they did then.

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Heart said...

Thank you professor! Your data have been the most helpful to my investment operations. I can't thank you enough!

Anonymous said...

Thank you professor we are all better investor reading your thoughts.
Greetings from Bosnia.
Saša

UTJ Blogs said...

Thank you Professor for all the valuable support you provide to us.

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Anonymous said...

I love your blog and ideas - particularly when focused on business - please keep it up. However, the contamination of your ideas by MPT, "beta", and similar nonsense (primarily focused on short-term price movements) is regrettable.

Anonymous said...

Professor,

The names of some industries in the Total Beta By Industry Sector for CHINA are doubled - there's two each for Healthcare Equipment, Facilities and Services but there is no data on Heavy Construction, Homebuilding, etc.

Also, it seems that Debt Ratio Trade Off Variables by Industry for CHINA were not updated for 2013.

Thanks for your efforts and all the best!

Aswath Damodaran said...

I don't know why it happened, but I think it is fixed now.

Juju said...

Thanks Prof. Btw i just drop by to know what is the different of market risk premium calculated using implied risk premium and market risk premium from service provider by bloomberg ( they are using market cap * avarage ddm index). And like you said, many people still let go their responsibility to service provider for the data eventhough they dont know what the principle behind the formula and how service provider derive the number.

Anonymous said...

This is regarding beta information for India. Pl let me know if my comments are correct on them.

1) Bottom up Beta for sector - This is unlevered beta for the sector
2) Bottom up levered beta - This is levered beta for the sector
3) Beta (column AC) - This is levered beta for the firm (business)

Your comments will be useful in interpreting the information supplied.

Thanks



Anonymous said...

Professor,

When are the spring corporate finance and valuation classes starting?

Thanks

Aswath Damodaran said...

On the betas for Indian companies, you are almost right. The only difference is that the levered bottom up beta is the sector beta levered up at the company's debt to equity ratio.
And my spring classes start a week from Monday (February 4, 2013).

Loganthan said...

Professor
For calculating the cost of equity and cost of capital you have used the global betas (indiacompfirm file) and not the purely Indian betas so to say from the betainda file. Is there any particular reason for this? I ask this because there is a substantial difference in the two beta numbers as is to be expected and for my own valuation exercises I am not sure now which beta to use. Your clarification would greatly help.

Anonymous said...

thank you professor

Luiz Felipe de Araújo Pontes Girão said...

Thank you, Professor Damodaran!!

Equipment Valuations said...

Nice post!!your post is very informative and impressive.

Unknown said...

"For calculating the cost of equity and cost of capital you have used the global betas (indiacompfirm file) and not the purely Indian betas so to say from the betainda file. Is there any particular reason for this? I ask this because there is a substantial difference in the two beta numbers as is to be expected and for my own valuation exercises I am not sure now which beta to use. Your clarification would greatly help. "

Pls. do reply.

Its not a ritual bt a useful tool for anyone followin u.Keep it up sir.

Sunshine Coast Bookkeeper said...

The odds associated to numbers are not a taboo for me anymore. That’s what I thought, but I stand by corrected with this post. There are many other negatives on numbers I didn’t realize they are until I read it here.

Call Answering Services said...

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Valentin said...

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Anonymous said...

On Indian data update:
Cost of equity (column J) and Cost of capital (column N) are calculated on dollar terms.

However (ROE - Cost of Equity) and (ROIC - Cost of Capital), i.e columns O and P - are based on different currencies.

Due to this companies appear to have excess returns, but actually are not.

Am I missing something? Please advice. Thanks for help.

Sunny

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