Seeking Alpha
Profile| Send Message|
( followers)  
Max Olson suggested I use Many Eyes to present some of the data from the normalized P/E series I started at the beginning of this year. A lot of people have asked me about the data – and this is as good a way to present it as any (and requires a lot less work on my part) – so, here's the first link using Many Eyes:

http://services.alphaworks.ibm.com/manyeyes/view/SxNb-HsOtha6TJUw_a96H2-

What you're seeing here is all the data for years where I have both a 15-year normalized P/E ratio for the Dow and a record of compound point growth in the Dow over the subsequent 15 years (the dots are for the years 1935-1991). Remember, although the value says "15yrRet" – we're looking at point growth, not total return. So, where the "15yrRet" value is equal to the "AAA" value, an investment in the Dow actually did quite a bit better.

The "x" axis shows the 15-year normalized P/E ratio (click here for an explanation of how that was calculated). The "y" axis shows point growth in the Dow over the subsequent 15 years. Once again, the actual total return over those fifteen years was higher in all cases. The size of the dot is related to the AAA yield for the year. Make sure you move your mouse around all over the graph so you can find all the tiny dots – otherwise, you'll miss all the years with low AAA bond yields – some of which were perfectly good years to buy stocks. The first number (as your mouse passes over a dot) is the percentage difference between actual and "expected" earnings – i.e., the difference between the Dow's actual earnings for that year and the 15-year normalized earnings for that year. Numbers in parentheses are negative. So, the large dot on the extreme left was a year in which Dow's actual earnings were slightly less than half of its 15-year normalized earnings. That year happens to be 1982.

Unfortunately, I don't have the years labeled for each of the dots. This is my first time using Many Eyes. I'll do better next time.

Please explore the next link, then see the discussion below.

http://services.alphaworks.ibm.com/manyeyes/view/SxNb-HsOtha6TJUw_a96H2-

One of the most frequently asked questions about the normalized P/E study is what I found regarding interest rates. In other words, aren't long-term interest rates and normalized P/E ratios closely related?

There's a strong logical basis for this assumption. As low normalized P/E ratios are also high normalized earnings yields (Low P/E = High E/P) – investors should compare the available bond yields with the available earnings yields and thus drive down normalized P/E ratios when bond yields are high.

Some of the data supports this – but only some of the data. There are several complicating factors here. One, any historical look at interest rates and stock prices is going to be dominated by the period of high interest rates and low stock prices that occurred at the outset of the 1982-1999 bull market. This was a specific time period, and like any specific time period, a lot of things occurred at once (few of which have been duplicated since – and some of which had never occurred before). As a result, it's hard to separate the factors involved.

This makes questions like: is it the absolute level of interest rates that matters or some relative level? – is it some relative level of interest rates that matters or is it the direction of movement in interest rates that matters? – how important is this interest rate effect? – how prolonged is this interest rate effect? – does it matter if current interest rates appear unsustainably high or low? – very, very hard to answer.

I didn't get into a long discussion of interest rates and normalized P/E ratios before, because I think it's a tricky subject. There is no doubt in my mind that interest rates both matter and matter far less than most people believe.

The biggest reason for this is that the effect of what I like to call "valuation undulation" is much, much bigger than any conceivable interest rate effect (I'm fibbing a bit here – with sustained rates very close to zero, the interest rate effect would be big). In other words, if you look at a year like 1982, you can't just look at the high interest rates and say they caused the low prices and that, as they fell, prices quite naturally rose. Did that happen? In a sense, yes. But, in a much bigger sense – no, that's not how it happened – or, at least that's not all (or even most) of what happened. How investors valued the long-term earnings power of the Dow components (which by the way, isn't very hard to estimate over long periods of time) changed. It changes all the time.

What caused the changes? I don't know. People who study markets (professionals, amateurs, and academics) like to think in terms of a stimulus and a response. You put "interest rates fall" into the black box and "stock prices rise" comes out. I'm not saying that the stimulus isn't important – what I am doing is reiterating a point I made a while back.

Data affects prices indirectly. The market is a lot like a fun house mirror. The resulting reflection is caused in part by the original data, but that does not mean the reflection is an accurate representation of the original data. To take this metaphor a step further, the Efficient Market Hypothesis is based on the idea that the original image acts on the mirror to create the reflection. It does not recognize the unpleasant truth that one can interpret the same process in a very different way. One could say it is the mirror that acts on the original image to create the reflection. In fact, that is often how we interpret the process. We say an object is reflected in a mirror. We rarely use the active “an object reflects in a mirror.”

The Efficient Market Hypothesis does not recognize the true importance of interpretation. Saying that data (publicly available information) acts on market prices omits the key step. After all, the same data is available to every blackjack player. Casinos just don’t like the way a card counter interprets that data.

What's going on inside that black box is critical. The stimulus causes the response in a very complex way. It adds another input that gets jumbled around with a handful of other important factors and a mountain of immaterial ones and then it comes out the other side as "buy," "sell," "hold," "wet yourself," etc.

Interest rates help form a certain interpretation. In that sense, they cause price changes. But, just because we can measure them and we can't measure a lot of other equally important things, we tend to overstate the importance of interest rates.

Once again, my favorite quote from Ben Graham:

The influence of what we call analytical factors over the market price is both partial and indirect – partial, because it frequently competes with purely speculative factors which influence the price in the opposite direction; and indirect, because it acts through the intermediary of people’s sentiments and decisions. In other words, the market is not a weighing machine, on which the value of each issue is recorded by an exact and impersonal mechanism, in accordance with its specific qualities. Rather should we say that the market is a voting machine, whereon countless individuals register choices which are the product partly of reason and partly of emotion.

Like all other data interest rates act through the intermediary of people's sentiments and decisions. You can't forget you're dealing with a lot of complex, thinking human beings. They don't react predictably to a single stimulus when there's a lot of cognition going on. Having said that, in certain similar situations, a specific stimulus may tend to lead (more often than not) to a certain response.

Regardless, when buying the entire market, simply looking for low normalized P/E ratios is a much more effective strategy than anything I can come up with involving interest rates. I expected this to be true in the long-term; the fact that it also works pretty well in the short-term surprised me.

Related Reading
A Look At 15-Yr. Normalized Dow P/E Ratios

A Look At Normalized P/E Ratios and the Election Cycle

A Second Look At Normalized P/E Ratios and the Election Cycle

On Normalized P/E Effects Over Time

Calculating Normalized P/E Ratios

The Difference Between Actual Earnings and Normalized Earnings

On Normalized Earnings Yields for the Dow, 1935-Present

On P/E Ratios in the 21st Century: Lower Your Expectations For Market Returns!

On the Utility of P/E Ratios Going Back 70+ Years

On High Normalized P/E Years

Source: On Normalized P/E Ratios, Interest Rates and Long-Term Returns