January 2016 Data Update 1: The US Equity Markets

Like most of you, I start every new year with optimism and hopeful resolutions, but the first week each year for the last two decades has been what I term my “Moneyball” week. During the week, I download the raw data on every publicly traded company that is listed globally, and work at converting that data into the industry averages that you see on my website. I do the analyses to keep myself grounded, since it is so easy to form preconceptions about market and corporate behavior that have no basis in reality. As I update the data, I will be doing a series of posts on how the numbers have or have not changed during the course of the last year. In this, the first of the series, I will start by looking at US equity markets, as we start the new year.

Looking Back

It was not a good year for US equity markets, but given circumstances, it could have been a lot worse. The S&P 500 was almost unchanged during the course of the year, though indices of some subsets of the market did worse:

Source: Standard and Poor's

During 2015, large cap stocks did better than small cap stocks, growth outperformed value and momentum investing provided a positive payoff. 

If there is a word that I would use to describe US equity markets in 2015, it is “resilient”, since they had to weather two significant crises. During the summer, China, the engine for global economic growth for much of the last decade, had a market meltdown, triggered by an economic slowdown. (Take a look at the post that I did at the height of the crisis in late August). The commodity markets, which collapsed in 2014, continued their decline in 2015, albeit at a slower pace. Notwithstanding these developments, the S&P 500 ended 2015, with a return of 1.36%, if you count in dividends, higher than the returns you would have generated on T.Bills (0.21%) or T.Bonds (1.28%) for the year. Incorporating the returns from 2015 into the historical data, the compounded annual returns on stocks, T.Bonds and T.Bills are shown below for periods going back as far as 1928:

Source: Damodaran Online

The historical premium earned by stocks, relative to treasury bonds, inched down to 4.54% for the 1928-2015 time period, from 4.60% for the 1928-2014 time period. 

Looking forward

The equity risk premium is the extra return that investors demand for investing in stocks as opposed to putting their money in a riskless asset, and it is the composite statistic that best captures how stocks are priced in the aggregate. It is also a number that I have posted on extensively, that I update at the start of every month on my website, and write an extended update about, each year. My preferred approach for estimating the premium is to start with current stock prices, estimate expected cash flows from owning stocks and to solve for the discount rate (expected return or internal rate of return). At the start of 2016, using the S&P 500 as the market index, the collective cash flow from dividends and buybacks as the cash flow from stocks and a top down estimate of growth in earnings for the index as the growth rate in the cash flows, I obtain an equity risk premium (ERP) of 6.12%:

Spreadsheet

This equity risk premium serves two purposes. First, it is a key input in valuing individual companies, becoming a part of the costs of equity and capital of all companies; a higher ERP translates into higher discount rates and lower values. Second, by comparing the current ERP to what you believe a fair ERP should be (perhaps by looking at historical averages), you can make a judgment on whether you think the market is under or over valued. If the current premium is higher (lower) than what you think is fair, stocks are under (over) valued. 

Source: Damodaran Online

On that comparison, the high ERP on US stocks (or at least those in the S&P 500) is reason for optimism. 

Cautionary Notes

The optimism emanating from the high current ERP should be tempered, though, since the drivers of that premium are much softer than they were a year ago, when the ERP was 5.78%. Largely as a consequence of the commodity price decline, base year earnings for the S&P 500 companies dropped almost 10% in 2015. In fact, the ERP at the start of 2016 is being elevated by two forces, the first is that the T.Bond rate continues to be low (by historic standards) and the second is that cash returned  (about 60% in buybacks) by US companies rose last year by 4% during the year. The net result of the declining earnings and increasing buybacks is that the cash returned last year was 101.54% of earnings, unsustainable over time. 

To correct for this, I reestimated the ERP, adjusting the cash return down to a more sustainable number over the long term. In effect, I lower the cash return ratio each year over the next five years to reach about 84% of earnings in year 5:

The resulting ERP is 5.16%, still higher than the 75th percentile (4.93%) for the 1960-2015 period, but the margin for error is much smaller than it was last year. If the continued drop in commodity prices in the last quarter of 2015 affects earnings for the index in 2016 and/or T.Bond rates rise sharply, the ERP will decline. This year, I plan to monitor (and report) this buyback-adjusted ERP , in addition to my unadjusted estimates, more closely than in prior years.

Bottom Line

I am more wary about equities going into 2016 than I was entering 2015, but my feelings about the market have never been a reliable predictor of what stocks actually do during the year. Thus, I will do what I always do at the start of every year, invest on the presumption that I am not  market timer and that I am better off investing in individual companies that I think are under valued. My faith in intrinsic value will be tested by price movements in the wrong direction and I hope that I will not be found wanting.

Data

1. Historical Returns on stocks, bonds and bills: United States
2. Historical Implied ERP for S&P 500

Spreadsheets

1. Implied ERP - January 2016

Data Update Posts

1. January 2016 Data Update 1: The US Equity Market 
2. January 2016 Data Update 2: Interest Rates and Exchange Rates - Currencies 
3. January 2016 Data Update 3: Country Risk and Pricing
4. January 2016 Data Update 4: Costs of Equity and Capital
5. January 2016 Data Update 5: Investment Returns and Profitability
6. January 2016 Data Update 6: Capital Structure
7. January 2016 Data Update 7: Dividend Policy
8. January 2016  Data Update 8: Pricing, with an end of month update

Moneyball and Investing: Data, Information and my 2012 Update

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 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. 

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!!