I loved Moneyball, both the book, by Michael Lewis, and movie starring Brad Pitt, because they bring together two things I love: baseball and numbers. At the risk of shortchanging the book, the central story in the book is a simple one. For most of baseball’s hundred plus years of existence, insiders (baseball managers, scouts and experts) have used stories and narratives to keep themselves above the riff raff (which is where you and I as fans belong). Thus, scouts claimed to have special skills (based on their long history of having done this before) to find potential superstars in high schools and the minor leagues, and managers justified their personnel decisions and game day choices with gut feeling and baseball instincts. Billy Beane, the general manager of the Oakland As, a storied but budget-constrained franchise, upended the game by shunting hoary tradition and putting his faith in the numbers.
I think that financial markets and baseball share a great deal in common. Equity research analysts are our baseball scouts, asking us to trust their story telling skills when picking stocks. Executives at companies are our baseball managers, flaunting their industry experience and asking us to trust their gut feeling and instincts, when it comes to big decisions. Like Billy Beane, I trust the numbers far more than either analyst stories or managerial instincts, and it is for that reason that I started gathering raw data on individual companies about two decades ago and computing industry averages for a few key inputs into investments: risk, return and growth. Initially, it was a limited exercise, where I looked at only US companies and a handful of statistics. I put those numbers online, not anticipating many downloads, but was pleasantly surprised at how many people seemed to find the data useful (I won’t flatter myself. The fact that it was free did help…)
Each year my coverage has expanded, driven partially by external demand and mostly by easier access to raw data. Starting in 2003, I went global and a year or two later started providing data on the individual companies as well. So, here is where the long windup is leading. I have just finished the January 2012 update to my data. You can get to it by going to the updated data page on my website:
http://www.stern.nyu.edu/~adamodar/New_Home_Page/data.html
My sample includes all (a) publicly traded firms, (b) listed on any global exchange and (c) have data on the data sources that I use (Value Line for US companies, Capital IQ and Bloomberg for non-US companies). In January 2012, there were 41,803 companies in my overall dataset.
I think that financial markets and baseball share a great deal in common. Equity research analysts are our baseball scouts, asking us to trust their story telling skills when picking stocks. Executives at companies are our baseball managers, flaunting their industry experience and asking us to trust their gut feeling and instincts, when it comes to big decisions. Like Billy Beane, I trust the numbers far more than either analyst stories or managerial instincts, and it is for that reason that I started gathering raw data on individual companies about two decades ago and computing industry averages for a few key inputs into investments: risk, return and growth. Initially, it was a limited exercise, where I looked at only US companies and a handful of statistics. I put those numbers online, not anticipating many downloads, but was pleasantly surprised at how many people seemed to find the data useful (I won’t flatter myself. The fact that it was free did help…)
Each year my coverage has expanded, driven partially by external demand and mostly by easier access to raw data. Starting in 2003, I went global and a year or two later started providing data on the individual companies as well. So, here is where the long windup is leading. I have just finished the January 2012 update to my data. You can get to it by going to the updated data page on my website:
http://www.stern.nyu.edu/~adamodar/New_Home_Page/data.html
My sample includes all (a) publicly traded firms, (b) listed on any global exchange and (c) have data on the data sources that I use (Value Line for US companies, Capital IQ and Bloomberg for non-US companies). In January 2012, there were 41,803 companies in my overall dataset.
I have computed industry averages for about 35 variables, covering a wide range of inputs:
a. Risk measures and hurdle rates: Betas and standard deviations, as well as costs of equity and capital, by sector.
b. Profitability measures: Profit margins (net and operating), tax rates and returns on equity and capital.
c. Growth measures/ estimates: Historical growth rates in revenues and earnings, as well as forecasted growth rates (where available)
d. Financial leverage (debt) measures: Book value and market value debt to equity and debt to capital ratios.
e. Dividend policy measures: Dividend yields and payout ratios, as well as cash statistics (cash as a percent of firm value).
f. Equity multiples: Price earnings ratios (current, trailing, forward), PEG ratios, Price to Book ratios and Price to Sales ratios.
g. Enterprise value multiples: Enterprise value to EBIT, EBITDA, revenues and invested capital.
I generally stay away from macro economic data but I do report equity risk premiums (historical and implied) over time and marginal tax rates across countries.
You are welcome to use whatever data you want from this site, but please keep in mind the following caveats:
1. Data yields estimates, not facts: In these days of easy data access and superb tools for analysis, it is easy to be lulled into believing that you are looking at facts, when you are really looking at estimates (and very noisy ones at that). Every number that is on my site, from the historical equity risk premium to the average PE ratio for chemical companies is an estimate (and adding more decimal points to my numbers will not make them more precise).
2. Data has to be measured: That is again stating the obvious, but implicit in this statement are two points. The first is that someone (an accountant, a data service, me) is doing the measurement and imposing his or her judgment on the measured value. The second is that there can be error in measurement. Thus, with my data, you can be assured that there are errors and mistakes in the final numbers. While I can blame some of these mistakes on the data services that I get my raw data from, many are mine. So, if you find a mistake or even something that looks like a mistake, please let me know and I promise you two things. First, I will not be defensive about it and will take a look at the issue you have raised. Second, if I do find myself in error, I will fix the error as soon as I can. (With a staff of one (me), this data service can get stretched sometimes… So, please have some patience).
3. Data for post-mortems versus data for predictions: As I see it, data can be used in two ways. The first is to generate post-mortems (about past performance) and the other is make forecasts for the future. Given my focus on corporate finance and valuation, I am more interested in the latter than the former. Thus, my data definitions are more attuned to forecasting than to after-the-fact analysis. Just to provide an example, the cost of capital that I am interested in computing for a company is the cost of capital that I can use for the next five years, not the one for the last three years.
a. Risk measures and hurdle rates: Betas and standard deviations, as well as costs of equity and capital, by sector.
b. Profitability measures: Profit margins (net and operating), tax rates and returns on equity and capital.
c. Growth measures/ estimates: Historical growth rates in revenues and earnings, as well as forecasted growth rates (where available)
d. Financial leverage (debt) measures: Book value and market value debt to equity and debt to capital ratios.
e. Dividend policy measures: Dividend yields and payout ratios, as well as cash statistics (cash as a percent of firm value).
f. Equity multiples: Price earnings ratios (current, trailing, forward), PEG ratios, Price to Book ratios and Price to Sales ratios.
g. Enterprise value multiples: Enterprise value to EBIT, EBITDA, revenues and invested capital.
I generally stay away from macro economic data but I do report equity risk premiums (historical and implied) over time and marginal tax rates across countries.
You are welcome to use whatever data you want from this site, but please keep in mind the following caveats:
1. Data yields estimates, not facts: In these days of easy data access and superb tools for analysis, it is easy to be lulled into believing that you are looking at facts, when you are really looking at estimates (and very noisy ones at that). Every number that is on my site, from the historical equity risk premium to the average PE ratio for chemical companies is an estimate (and adding more decimal points to my numbers will not make them more precise).
2. Data has to be measured: That is again stating the obvious, but implicit in this statement are two points. The first is that someone (an accountant, a data service, me) is doing the measurement and imposing his or her judgment on the measured value. The second is that there can be error in measurement. Thus, with my data, you can be assured that there are errors and mistakes in the final numbers. While I can blame some of these mistakes on the data services that I get my raw data from, many are mine. So, if you find a mistake or even something that looks like a mistake, please let me know and I promise you two things. First, I will not be defensive about it and will take a look at the issue you have raised. Second, if I do find myself in error, I will fix the error as soon as I can. (With a staff of one (me), this data service can get stretched sometimes… So, please have some patience).
3. Data for post-mortems versus data for predictions: As I see it, data can be used in two ways. The first is to generate post-mortems (about past performance) and the other is make forecasts for the future. Given my focus on corporate finance and valuation, I am more interested in the latter than the former. Thus, my data definitions are more attuned to forecasting than to after-the-fact analysis. Just to provide an example, the cost of capital that I am interested in computing for a company is the cost of capital that I can use for the next five years, not the one for the last three years.
4. Data anchoring: Whether we like it or not, our instinct when confronted with a number, and asked to decide whether it is high or low, is to compare it what we consider reasonable numbers (at least in our minds). Thus, if I came to you with a stock with a PE of 10, your determination of whether the stock is cheap or expensive will depend largely on what you think the average PE is across all stocks and what comprises a high or low PE and all too often, in the absence of updated and comprehensive data, these are guesses. It is for this reason that analysts and investors create rules of thumb: a EV/EBITDA of less than six is cheap, a PEG ratio less than one is cheap or a stock that trades at less than book value is cheap. But who comes up with these rules of thumb? And do they work? The only way to answer these questions is to look at the data across all companies and make your own judgments.
There is one final point generally about data that I have to make, and it relates back to Moneyball. Much as I agree with Billy Beane on the importance of data, I think that his mistake was focusing far too much on the data. The data should be the starting point for your assessments, but not the ending point. Stories do matter, if they can be backed up by the data, or to draw implications from it. The secret to great investing is a happy marriage between plausible investment stories and numbers, with the recognition that even the best sounding stories have to be abandoned at some point, if the numbers don’t back them up. So, explore the data and make it your own!!
There is one final point generally about data that I have to make, and it relates back to Moneyball. Much as I agree with Billy Beane on the importance of data, I think that his mistake was focusing far too much on the data. The data should be the starting point for your assessments, but not the ending point. Stories do matter, if they can be backed up by the data, or to draw implications from it. The secret to great investing is a happy marriage between plausible investment stories and numbers, with the recognition that even the best sounding stories have to be abandoned at some point, if the numbers don’t back them up. So, explore the data and make it your own!!
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