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Geoff Gannon December 29, 2006

In Defense of Extraordinary Claims

About two weeks ago in a post entitled “We Have Some Bearish Bloggers Out There“, Bill Rempel wrote, “Personally, I’m in the ‘extraordinary claims require extraordinary proof’ camp.” I’d like to think I am too, because Bill is right – extraordinary claims do require extraordinary proof.

So, before making any extraordinary claims about future long-term market returns (i.e., predicting future returns that differ substantially from historical returns), I’d like to spend this post laying out the case for why current circumstances are extraordinary. After all, extraordinary times call for extraordinary claims.

Essentially, this is a post about why the present is unlike the past and what that means for the future.

In a previous post, I wrote:

Stocks are not inherently attractive; they have often been attractive, because they have often been cheap.

Unless they internalize this fact, investors risk assuming that historical returns that existed under special circumstances can continue to serve as a useful frame of reference, even when these special circumstances no longer exist.

Later in this post, I will discuss the possibility of a “paradigm shift” (i.e., a change in basic assumptions within the theory of investment) that began in 1995. The only other period in the 20th century which saw similar upheaval in investment thinking was the 1920s.

Common Stocks as Long Term Investments

That theoretical crisis (and the higher valuations that followed it) has often been partly attributed to a thin volume published in 1924 by Edgar Lawrence Smith. The book was called “Common Stocks as Long Term Investments” and it was based on a study of 56 years of market data (1866 – 1922).

Smith found that stocks had consistently outperformed bonds over the long run. Neither the data in support of this conclusion nor the logical explanation for this outperformance (public companies retain earnings and these retained earnings lead to compound growth) was wrong.

However, a few years after Smith’s book was published, the special circumstances of the past disappeared as stocks (which had historically had higher yields than bonds) saw their prices surge and their yields plunge. Soon, stocks had lower yields than bonds – part of the reason for their past outperformance (the initial yield advantage) was gone and the margin of safety which a diversified group of stocks had offered over bonds narrowed considerably.

Simply put, circumstances changed. John Maynard Keynes saw this possibility when he reviewed Smith’s book in 1925:

“It is dangerous…to apply to the future inductive arguments based on past experience, unless one can distinguish the broad reasons why past experience was what it was.”

That has been the objective of this little study from the outset. In this post, I will focus on how the circumstances of the present differ from the circumstances of the past.

I will also endeavor to demonstrate that historical returns were the result of special circumstances, which (logically) need not apply now or in the future. The historical data suggests these circumstances may yet …

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Geoff Gannon December 27, 2006

On the Dow’s Normalized Earnings Yields for 1935-2006

Before tackling the subject of what kind of returns investors can expect from the stock market over their lifetime (and what a “fair” value for the Dow might be) we need to put today’s valuations in historical perspective.

To do this, I will first provide a graph of the Dow’s 15-year normalized P/E ratio for each year from 1935-2006. For information on how these “normalized” numbers were calculated, please see my previous post “On Calculating Normalized P/E Ratios.”

PE ratio.jpg

I consider this graph to be something of a conceptual crutch. Everyone cites P/E ratios – even I do, because it’s one of the best known measures in investing. Regardless of the audience you’re writing for, you can count on them understanding the P/E ratio.

However, presenting P/E ratios is a bit misleading, because I don’t really think in terms of P/E ratios – I think in terms of earnings yields. You should too.

The earnings yield is a much easier number to work with. It facilitates comparisons with other possible investments, simplifies the process of estimating the expected rate of return over various holding periods, and just generally makes life a whole lot easier.

The earnings yield is simply the inverse (i.e., reciprocal) of the P/E ratio. Simply put, it’s “e” over “p”. For example, a stock with a price-to-earnings ratio of 12.5 has an earnings yield of 8%.

Here is a graph of the Dow’s 15-year normalized earnings yield for each year from 1935-2006:

Earnings Yield.jpg

Finally, to give you an idea of the role interest rates played during this period, here is a graph showing both the Dow’s normalized earnings yield and AAA corporate bonds yields for each year from 1935-2006:

Earnings Yield AAA.jpg

Just look over these graphs for now. I’ll discuss the importance of normalized earnings yields in my next post. Without some historical perspective, you may have trouble following that discussion.

Related Reading

On 15-Year Normalized P/E Ratios for the Dow

On Normalized P/E Ratios and the Election Cycle

On Normalized P/E Ratios and the Election Cycle (Again)

On Normalized P/E Effects Over Time

On Calculating Normalized P/E Ratios

On the Difference Between Actual Earnings and Normalized Earnings

I’ll have many more posts on this project in the days ahead. If you have any questions (or suggestions) about this project, please feel free to comment to this post – or, simply send me an email.

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Geoff Gannon December 26, 2006

On the Difference Between Actual Earnings and Normalized Earnings

Yesterday, I concluded my post “On Calculating Normalized P/E Ratios” with this graph of the percentage difference between the Dow’s actual earnings and its 15-year normalized earnings for each year from 1935-2005:

Normalized earnings difference.jpg

I also wrote:

The difference between actual earnings and normalized (or “expected”) earnings is one of the most fascinating statistics in this little study.

To better understand this statistic we need to look at its distribution over the 71 years in this study.

First, let’s look at a graph of the difference between the Dow’s actual earnings and its 15-year normalized earnings for each of the last seventy-one years. But, this time, instead of presenting the data in chronological order, I will simply plot the data in ascending order by difference between actual and normalized earnings:

Purple Chart.jpg

Based on this graph, you would probably guess that the Dow’s actual earnings have been higher than its normalized earnings about half the time and lower than its normalized earnings about half the time.

That’s quite right. From 1935-2005, the difference between the Dow’s actual earnings and its normalized earnings was positive in 36 years and negative in 35 years.

Distribution of the Data

From 1935-2005, the percentage difference between the Dow’s actual earnings and its 15-year normalized earnings ranged from (62.12%) to 65.14%. The average (mean) difference between actual and normalized earnings was 0.44%. The median difference was 0.09%.

According to the “empirical rule”, in a normal (bell-shaped) distribution, about 68% of the values will be within one standard deviation of the mean, about 95% of the values will be within two standard deviations of the mean, and about 99.7% of the values will be within 3 standard deviations of the mean.

In our little study, 49 of 71 values (69.01%) are within one standard deviation of the mean, 67 of 71 values (94.37%) are within two standard deviations of the mean, and 71 of 71 values (100%) are within three standard deviations of the mean. No value is more than 2.5 standard deviations from the mean. In fact, the greatest distance between a value and the mean is 2.26 standard deviations.

Of the 49 values within one standard deviation, 24 are positive and 25 are negative. Of the 67 values within two standard deviations, 34 are positive and 33 are negative.

Frequency of Various Differences

I can quickly give you some sense of how common large and small differences between the Dow’s actual earnings and its 15-year normalized earnings were from 1935-2005.

Remember, negative numbers mean actual earnings fell below normalized earnings; positive numbers mean actual earnings exceeded normalized earnings.

(71.24%) – (56.90%): 2 of 71 years or 2.82% of the time

(56.90%) – (42.57%): 2 of 71 years or 2.82% of the time

(42.57%) – (28.23%): 6 of 71 years or 8.45% of the time

(28.23%) – (13.90%): 13 of 71 years or 18.31% of the time

(13.90%) – 0.44%: 13 of 71 years or 18.31% of the time

0.44% – 14.78%: 15 of 71 years or

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Geoff Gannon December 25, 2006

On Calculating Normalized P/E Ratios

So far, I’ve referenced normalized P/E ratios in four of my posts: “On 15-Year Normalized P/E Ratios for the Dow“, “On Normalized P/E Ratios and the Election Cycle“, “On Normalized P/E Ratios and the Election Cycle (Again)“, and “On Normalized P/E Effects Over Time“.

However, I’ve yet to explain how I calculated these normalized P/E ratios. Obviously, I took the Dow’s average price for the year and divided by a normalized earnings number. But, how did I come up with a normalized earnings number – in other words, what exactly is the normalization process?

The Normalization Process

The normalization process is actually quite simple and straightforward. First, you need to decide upon a reasonable long-term growth rate; otherwise, you won’t have a “trend” to use for comparisons between actual and “expected” earnings. Essentially, “normalized earnings” are just “expected earnings” based on a long-term trend rather than short-term considerations.

For the Dow, a reasonable long-term growth rate would be about 6%. Many different approaches (logical and empirical) will bring you to a similar conclusion. Of course, we could argue forever about what the “right” long-term growth rate assumption is.

That’s because there is no right long-term growth rate. To the extent that future circumstances differ from past circumstances, there may be deviations from this trend. But, for the most part, it is not unreasonable to use an earnings growth rate of 6% per annum when normalizing the Dow’s earnings.

Once you’ve decided upon an appropriate earnings growth rate, you simply take one plus your assumed growth rate and raise it to a power equal to the distance between the current year and the year you are adjusting. This number is the adjustment factor.

If you were calculating a 15-year normalized P/E ratio, you would use the following fifteen “adjustment factors”: 1.06, 1.12, 1.19, 1.26, 1.34, 1.42, 1.50, 1.59, 1.69, 1.79, 1.90, 2.01, 2.13, 2.26, and 2.40.

You start by multiplying the first adjustment factor (1.06) by the most recent year’s earnings. Then, you multiply the second adjustment factor by the second most recent year’s earnings and so on.

Finally, you add up your adjusted earnings (i.e., the products of the operations you just performed) and you divide by the number of years used in your normalization process. When calculating a 15-year normalized P/E ratio, you would divide the sum of your adjusted earnings by 15. It’s really that simple.

For instance, if you were calculating normalized earnings for 1995, you would multiply 1994’s EPS by 1.06, 1993’s by 1.12, 1992’s by 1.19, 1991’s by 1.26 and so on.

Please note that I am not suggesting you ever use this normalization process on an individual stock. In fact, I think that would be a rather ridiculous approach that would generally prove inferior to a careful consideration of the known facts regarding that particular enterprise and its future prospects.

I am, however, suggesting that when applied to a diversified group of very large American businesses …

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Geoff Gannon December 23, 2006

Suggested Link: Joe Cit – Intelligent Investing

I just came across a blog I’d like to share with you. It’s called “Joe Cit – Intelligent Investing“.

The author is a student at Yale. He is obviously a Warren Buffett devotee – as a picture of him with Buffett adorns the site. Perhaps even more telling, his portfolio holdings (which he lists for all to see) reflect Buffett’s influence.

Of course, none of this is particularly remarkable. There are plenty of investors who seek to emulate Buffett and a few of them even write their own blogs.

What is remarkable is how similar many of the companies he writes about are to the ones I write about on this blog. That’s why I think you’ll enjoy visiting his blog, if you’re already reading mine.

Here are two stocks he’s mentioned that I’ve mentioned as well:

Avalon Holdings (AWX)

Joe Cit: Post on Avalon Holdings

Gannon On Investing: Podcast mentioning Avalon Holdings

PetMed Express (PETS)

Joe Cit: Post on PetMed Express

Gannon On Investing: Post mentioning PetMed Express

Visit Joe Cit – Intelligent Investing

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Geoff Gannon December 22, 2006

On Some of My Favorite Sites

It’s the end of the year. A lot of bloggers try to do something special at this time – make a prediction, summarize the past year, look back, look ahead, etc. I am planning something special for you this weekend.

But, for now, I don’t have anything of my own to share. Rather, I’d like to share the fruits of other people’s labor.

So, I now present some great sites I don’t mention enough (and you probably don’t read enough):

Bill Rempel, a.ka. No DooDahs

I haven’t done an official count, but I think it’s safe to say the blogger who has given me the most material to work with this year was Bill Rempel. His blog is quite different from my own, but his posts always leave me with something to think about. Most recently, I wrote a post “On Gold and Rome” after reading Bill’s post entitled “Not Bullish On Gold“. Several of my best posts were written after reading something Bill wrote. If you haven’t checked out his blog yet, I encourage you to do so now.

By the way, you can see Bill’s responses to the questions posed by Ticker Sense in their Financial Blogger Outlook. Click the image of a table to see Bill’s views on housing, gold, the dollar, bonds, etc.

Fat Pitch Financials

George of Fat Pitch Financials is a value investor and (in many ways) a disciple of Warren Buffett. He looks for “fat pitches” whether they are spin-offs, arbitrage opportunities, or simply great businesses. He also started Value Investing News, a great place to browse some of the best recent articles and posts in the world of value investing.

Value Blog Review

As you probably know, Steven Rosales contributes to this site (in fact, he writes most of our book reviews). He also has his own site where you can find plenty of material that doesn’t appear here. He reviews books, blogs, and online resources of interest to new investors. He’s also started discussing a few of his early trades. This is a great site for new investors to check out. You’ll feel like a fellow traveler setting off on a journey into the world of investing together.


This great resource tracks about thirty investing “gurus” – people like Warren Buffett, Bill Miller, Marty Whitman, Seth Klarman, and Bruce Berkowitz. I love how easy this site is to browse. There are other sites that track some of these gurus. But, this one is by far the best. I can’t recommend it highly enough. Don’t wait until the New Year, bookmark this site today.

24/7 Wall St.

This is a site you want to check once a day – everyday. Make it a kind of ritual. There’s often something interesting here and they keep the material coming constantly. You can’t live on posts like mine alone, you need a balanced diet and 24/7 Wall St. is just the kind of blogging buffet (for once

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Geoff Gannon December 21, 2006

On Normalized P/E Effects Over Time

In a previous post, I explained why I chose to present the performance of high and low 15-year normalized P/E years by measuring the compound point growth in the Dow over the subsequent fifteen years:

“I thought if long-term performance was as closely tied to “earnings power” as I thought it ought to be, then fifteen years of past earnings data and fifteen years of future share price growth should be enough to detect this relationship…I gave no thought to the possibility that any normalized P/E effect would be discernible over much shorter time periods.”

Well, I should have given that possibility some serious thought, because the normalized P/E effect is discernible over much shorter periods of time.

In fact, the normalized P/E effect is discernible in a way I did not expect to find – and may even have subconsciously preferred not to find.

I write about long-term investing on this blog, because I think about long-term investing and I want others to think about it too. As a result, I really don’t want to present findings from my little normalized P/E study that suggest there is a short-term P/E effect. More than anything, I really don’t want to present findings that suggest the normalized P/E effect is ever more pronounced over a shorter period of time than a longer period of time.

That last sentence requires some explanation. I will need to (reluctantly) employ a physics analogy. I say “reluctantly”, because I’ve (not so subtly) hinted that economists (and their science) suffer from a certain degree of physics envy, as well as an unhealthy attachment to precisely quantifiable figures. Nevertheless, I think this physics analogy will work, because everyone knows enough physics to know what I’m talking about.

The Weighing Force

Conceptually, I thought of the normalized P/E effect as evidence of a perpetual, pervasive, propensity that I’ll call “the weighing force” (for Ben Graham). Knowing what we know about markets, their participants, and the aims of those participants, we should expect markets to often appear efficient, simply because there is a natural tendency for price and value to converge.

I use the term “natural” quite loosely here, because we’re dealing with a complex, human phenomenon. Still, it seems appropriate to consider this tendency for price and value to converge to be a natural consequence of value-seeking market participants.

This is especially true, because some market participants are capable of extracting value outside the market by purchasing stock to gain influence or complete control of a business and then milking that business for cash, selling corporate assets, or integrating the entire business into their own operations.

For evidence of this “outside” influence on the stock market, consider Marty Whitman’s Third Avenue Value Fund, which makes purchases with the expectation that value will be extracted from many of the fund’s holdings without the fund actually selling shares in the open market.

Simply put, if you create a market for productive assets (like pieces of businesses) the weighing force is …

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Geoff Gannon December 20, 2006

On Normalized P/E Ratios and the Election Cycle (Again)

Today, I’ll be detailing how I calculated the normalized P/E numbers I referenced in two previous posts: “On 15-Year Normalized P/E Ratios for the Dow” and “On Normalized P/E Ratios and the Election Cycle“.

That explanation will come later in the day. First, I’d like to revisit two topics about which I’ve received quite a bit of email over the last twenty-four hours. The two topics readers seem most interested in are the election cycle and the relationship between 15-year normalized price-to-earnings ratios and one-year point growth in the Dow.

First, let’s tackle the election cycle. When writing about this (normalized P/E) project, I run a lot of numbers I never report to you. For the most part, I only share interesting or unexpected findings. However, I still routinely check to make sure I’m not missing something obvious. Despite these checks, I encourage (and ultimately depend on) your attempts to keep me honest by pointing out the possible holes in my logic.

So, let’s poke a bit at the findings from the last post and see if we can find a hole.

The Hypothesis

One obvious explanation for the election cycle effect is that mid-term years might tend be abnormally cheap years. Is this hypothesis supported by the data?

Technically, mid-term years do have below-average 15-year normalized P/E ratios. But, I wouldn’t say these years have abnormal 15-year normalized P/E ratios, because other randomly selected groups from within this same set of years (1935-2005) would also have normalized P/E ratios that fall a bit below the average for the entire set.

The Comparison

The “full set” (1935-2005) had an average (mean) 15-year normalized P/E of 14.08, a median of 13.59, and a range of 6.88 – 30.84. Just under 44% of the years in this set had a normalized P/E of less than 12.50.

The “election cycle set” (1938, 1942, 1946…) had an average 15-year normalized P/E of 13.46, a median of 13.00, and a range of 6.88 – 28.05. Just over 47% of the years in this set had a normalized P/E of less than 12.50.

The 12.50 Rule

The importance of this last check (percentage of years with normalized P/E of less than 12.50) is based purely on logic. Before beginning this study, I felt that when the Dow has a 15-year normalized earnings yield of 8% or more (i.e., a normalized P/E of 12.50 or less) there is a very good chance it is an attractive purchase for long-term investors, because other assets don’t tend to offer long-term returns superior to those expected from an asset priced at 12.5 times its “earnings power”, and sometimes present greater risks (including a loss of purchasing power) than a diversified group of large businesses like the Dow normally does.

Obviously, the fact that, since 1935, the Dow has been (what I would call) “undeniably cheap” nearly 44% of the time helps explain why it has done so much for long-term investors. Stocks are not inherently attractive; …

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Geoff Gannon December 18, 2006

On Gold and Rome

Over the weekend, Bill Rempel put up an interesting post entitled “Not Bullish on Gold”.

Bill makes two important points early in his post:

1) Gold is not “original money”
2) When a government controls money, it will manipulate the situation to its advantage

Inflation is not a modern phenomenon; it is a governmental phenomenon. Many otherwise intelligent people completely miss this point. For those investors with more knowledge of history than economics, Spain’s experience with New World gold probably stands out as a clear example of inflation. That’s good, because knowledge of two or more separate occurrences of the same phenomenon under seemingly different conditions is often the key to better understanding that phenomenon.

There’s another excellent example of inflation that is rarely studied. It happened roughly two thousand years ago in Europe, Africa, and the Middle East.

If you have any interest in inflation during Roman times, I’d recommend Kenneth Harl’s Coinage in the Roman Economy, 300 B.C. to A.D. 700 (Ancient Society and History). I should warn you this book was not written to address inflation specifically or even economics generally. It certainly sheds some light on those topics, but the subject of the book is exactly as the author describes it on page one:

“The objective of this book is an examination of how the Romans used coined money – its role in payrolls, tax collection, trade, and daily transactions – over the course of a millennium, 300 B.C. to A.D. 700. Although there are many books about Roman coins, they are, for the most part, numismatic works devoted to the study of coins as objects rather than as evidence for the economic and social life of the Roman world. This is an attempt to redress the imbalance by dealing with coins both as fiscal instruments of the Roman state and as the medium of exchange employed by the Roman public.”

I find this stuff interesting. If you do too, read the book. However, if your only interest in Roman monetary history is better understanding inflation (in modern societies), you’ll want to skip the book – but learn the history. I can’t cover Roman monetary history in a single blog post; however, I will try to touch on some highlights that relate to Bill’s post.

The first point worth mentioning is that Rome’s early monetary history clearly demonstrates that gold is not “original money”. Early Italian people used iron and bronze as money long before adopting gold. Before that, they may have reckoned prices in cattle.

In fact, Pliny the Elder wrote that the first forms of money (pecunia) were actually substitutes for cattle (pecus), and that’s where the Latin word pecunia came from. There may be some truth to this, as the English word “fee” is believed to be derived from a German word for cattle, which is itself quite possibly a cognate of pecus. Clearly, there was some connection between cattle and wealth in these societies; however, …

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Geoff Gannon December 13, 2006

Column: Warren Buffett and the Washington Post

Guest Columnist Max Olson’s latest article is entitled “Warren Buffett and the Washington Post”. In this article, Max attempts to “reverse engineer” Berkshire’s 1973 investment in the Washington Post Company.

Read “Warren Buffett and the Washington Post”

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