Here is a new update of a popular market valuation method using the most recent Standard & Poor’s “as reported” earnings and earnings estimates and the index monthly averages of daily closes for the past month, which is 1,512.31. The ratios in parentheses use the monthly close of 1,514.68. For the earnings, see the table below created from Standard & Poor’s latest earnings spreadsheet.
● TTM P/E ratio = 17.0 (17.0)
● P/E10 ratio = 22.2 (22.3)
The Valuation Thesis
A standard way to investigate market valuation is to study the historic Price-to-Earnings (P/E) ratio using reported earnings for the trailing twelve months (TTM). Proponents of this approach ignore forward estimates because they are often based on wishful thinking, erroneous assumptions, and analyst bias.
TTM P/E Ratio
The “price” part of the P/E calculation is available in real time on TV and the Internet. The “earnings” part, however, is more difficult to find. The authoritative source is the Standard & Poor’s website, where the latest numbers are posted on the earnings page. (See the footnote below for instructions on accessing the file).
The table here shows the TTM earnings based on “as reported” earnings and a combination of “as reported” earnings and Standard & Poor’s estimates for “as reported” earnings for the next few quarters. The values for the months between are linear interpolations from the quarterly numbers.
The average P/E ratio since the 1870’s has been about 15. But the disconnect between price and TTM earnings during much of 2009 was so extreme that the P/E ratio was in triple digits — as high as the 120s — in the Spring of 2009. In 1999, a few months before the top of the Tech Bubble, the conventional P/E ratio hit 34. It peaked close to 47 two years after the market topped out.
As these examples illustrate, in times of critical importance, the conventional P/E ratio often lags the index to the point of being useless as a value indicator. “Why the lag?” you may wonder. “How can the P/E be at a record high after the price has fallen so far?” The explanation is simple. Earnings fell faster than price. In fact, the negative earnings of 2008 Q4 (-$23.25) is something that has never happened before in the history of the S&P 500.
Let’s look at a chart to illustrate the unsuitability of the TTM P/E as a consistent indicator of market valuation.
The P/E10 Ratio
Legendary economist and value investor Benjamin Graham noticed the same bizarre P/E behavior during the Roaring Twenties and subsequent market crash. Graham collaborated with David Dodd to devise a more accurate way to calculate the market’s value, which they discussed in their 1934 classic book, Security Analysis. They attributed the illogical P/E ratios to temporary and sometimes extreme fluctuations in the business cycle. Their solution was to divide the price by a multi-year average of earnings and suggested 5, 7 or 10-years. In recent years, Yale professor Robert Shiller, the author ofIrrational Exuberance, has reintroduced the concept to a wider audience of investors and has selected the 10-year average of “real” (inflation-adjusted) earnings as the denominator. Shiller refers to this ratio as the Cyclically Adjusted Price Earnings Ratio, abbreviated as CAPE, or the more precise P/E10, which is my preferred abbreviation.
The Correlation between the S&P Composite and its P/E10
As the chart below illustrates, the P/E10 closely tracks the real (inflation-adjusted) price of the S&P Composite. In fact, the correlation between the two since 1881, the year when the first decade of average earnings is available, is 0.77. (Note: A perfect positive correlation would be 1 and the absence of correlation would be 0).
The historic P/E10 average is 16.5. After dropping to 13.3 in March 2009, the ratio rebounded to an interim high of 23.5 in February of 2011 year and is now at 22.2. The ratio in the chart above is doubly smoothed (10-year average of earnings and monthly averages of daily closing prices for the index). Thus the fluctuations during the month aren’t especially relevant (e.g., the difference between the monthly average and monthly close P/E10).
Of course, the historic P/E10 has never flat-lined on the average. On the contrary, over the long haul it swings dramatically between the over- and under-valued ranges. If we look at the major peaks and troughs in the P/E10, we see that the high during the Tech Bubble was the all-time high above 44 in December 1999. The 1929 high of 32.6 comes in at a distant second. The secular bottoms in 1921, 1932, 1942 and 1982 saw P/E10 ratios in the single digits.
Where does the current valuation put us?
For a more precise view of how today’s P/E10 relates to the past, our chart includes horizontal bands to divide the monthly valuations into quintiles — five groups, each with 20% of the total. Ratios in the top 20% suggest a highly overvalued market, the bottom 20% a highly undervalued market. What can we learn from this analysis? The Financial Crisis of 2008 triggered an accelerated decline toward value territory, with the ratio dropping to the upper second quintile in March 2009. The price rebound since the 2009 low pushed the ratio back into the top quintile, and it has since hovered around that boundary. By this historic measure, the market is expensive, with the ratio approximately 35% above its average (arithmetic mean) of 16.5 (16.47 to two decimal places). Last month it was 33% above.
I’ve also included a regression trendline through the P/E10 ratio for the edification of anyone who believes the price-earnings ratio has naturally tended higher over time as markets evolve. The latest ratio is about 18% above trend, up from 17% last month (18.3% versus 16.5% at one decimal place). Critics of this more optimistic view would point to the unprecedented P/Es of the Tech Bubble as the explanation for this “unnatural” slope to the regression.
We can also use a percentile analysis to put today’s market valuation in the historical context. As the chart below illustrates, latest P/E10 ratio is approximately at the 86th percentile of the 1586 data points in this series.
A more cautionary observation is that when the P/E10 has fallen from the top to the second quintile, it has eventually declined to the first quintile and bottomed in single digits. Based on the latest 10-year earnings average, to reach a P/E10 in the high single digits would require an S&P 500 price decline below 550. Of course, a happier alternative would be for corporate earnings to continue their strong and prolonged surge. If the 2009 trough was not a P/E10 bottom, when might we see it occur? These secular declines have ranged in length from over 19 years to as few as three. The current decline is now approaching its 13th anniversary.
Or was March 2009 the beginning of a secular bull market? Perhaps, but the history of market valuations suggests a cautious perspective.
Note: Follow these steps to access the Standard & Poor’s earnings spreadsheet:
- Click the S&P 500 link in the second column of the Standard & Poor’s home page.
- Click the plus symbol to the left of the “Download Index Data” title.
- Click the Index Earnings link to download the Excel file. Once you’ve downloaded the spreadsheet, see the data in column D.
Note: For readers unfamiliar with the S&P Composite index, see this article for some background information.
Images: Flickr (licence attribution)
About The Author
My original dshort.com website was launched in February 2005 using a domain name based on my real name, Doug Short. I’m a formerly retired first wave boomer with a Ph.D. in English from Duke. Now my website has been acquired byAdvisor Perspectives, where I have been appointed the Vice President of Research.
My first career was a faculty position at North Carolina State University, where I achieved the rank of Full Professor in 1983. During the early ’80s I got hooked on academic uses of microcomputers for research and instruction. In 1983, I co-directed the Sixth International Conference on Computers and the Humanities. An IBM executive who attended the conference made me a job offer I couldn’t refuse.
Thus began my new career as a Higher Education Consultant for IBM — an ambassador for Information Technology to major universities around the country. After 12 years with Big Blue, I grew tired of the constant travel and left for a series of IT management positions in the Research Triangle area of North Carolina. I concluded my IT career managing the group responsible for email and research databases at GlaxoSmithKline until my retirement in 2006.
Contrary to what many visitors assume based on my last name, I’m not a bearish short seller. It’s true that some of my content has been a bit pessimistic in recent years. But I believe this is a result of economic realities and not a personal bias. For the record, my efforts to educate others about bear markets date from November 2007, as this Motley Fool