In a recent post, link here, we discussed the importance of valuations when it comes to gauging the potential for returns and risks associated with those returns. Having an indicator to help guide your focus to either wealth creation or wealth preservation makes a ton of sense for stock market investors. We closed the post with a few questions which included the following – What indicator do we use to gauge valuations?
We use a ratio known as the Q Ratio. Is it perfect, no. Is it effective, let’s take a look.
Before we get going, if anybody would like to read up on the Q ratio, a great place to start is Andrew Smithers and Stephen Wright’s book, Valuing Wall Street – Protecting Wealth In Turbulent Markets. They are gurus of the Q and if interested, their book is well worth the read.
The Q ratio is the total stock market’s value (numerator) over the fundamental value of the companies that comprise the stock market (denominator). Think of fundamental value as the replacement costs of assets, or net worth. The data to calculate the Q ratio comes from the Fed’s Z.1 report on a quarterly basis. It’s really pretty simple, you are looking at non-financial corporate equities as a percentage of net worth.
Think of this in terms of your computer. The numerator is the price you are willing to pay for your computer (market price). The denominator is the cost of the stuff used to make your computer (replacement cost). Theoretically, if you could go out and buy the stuff to build your computer for $500 dollars and the market price of your computer is $1,000, you’d be inclined to just build a lot of computers and sell them on the market. The same thing holds true on the flip side. If for some reason replacement costs are higher than market price, you’d just buy the computer on the market. That’s a simple example of the Q ratio, but hopefully helpful in illustrating the ratio as it relates to the market as a whole and how the potential over or undervaluation can act as a magnet towards equilibrium.
Below is a chart of the Q ratio from 1901 through the first quarter of 2017. 103.8 is the current raw Q score and as you can see, it’s elevated. Take away the monster spike from the late 90’s and you have one of the highest readings of the last 100+ years. You will see in the chart, the typical Q reading is much lower than 1. The reasons for that are beyond the scope of this post but I wanted to point that out.
What exactly do we need it to do for us? Without getting too granular, let’s focus on what matters. The Q ratio should help us understand potential returns and risks of owning stocks as a whole which would guide our focus towards wealth creation or wealth preservation. Let me stress, this is how we measure the odds, not forecast certainty.
Common sense tell us that high Q readings would mean lower potential returns and higher potential risks while low Q readings would mean the reverse. The data seems to support that reasoning. Below is a chart of the excess 1 year nominal price returns of the S&P 500 vs the 1 year interest rate from 1901 to 2011. We have grouped the returns based on Q readings. We have created 4 groups related to the actual quartiles of the q. Quartiles are just a fancy way of breaking Q readings into buckets. The first bucket is the lowest 25% of readings, the second is the next lowest 25% and so on. The break points are on the horizontal axis of the chart below. It appears that stocks performed much better than the proxy for a risk-free asset when the prior years’s closing Q was below .45. It also appears that stocks performed much worse than our proxy for a risk free asset when the prior year’s closing Q was above .85. In addition, there doesn’t seem to be much of a difference when Q readings are in the middle two buckets. Maybe extreme readings should be something that catches our attention…more to come later on that thought. We are using S&P and interest rate data from from Robert Shiller .
What about risk? We decided to look at each negative annual return. Remember, we are looking at nominal price returns here. We wanted to review the preceding three Q readings prior to the negative return and take the max of the three. The average negative return year had an average max Q of .86 over the preceding 3 years. .86 is right on the border of the 3rd and 4th quartile.
There’s a million ways to skin this cat, but from our extremely simple glance at the Q Ratio’s effectiveness at guiding our focus – it appears High Q readings could increase the odds of lower returns and elevated risks. To emphasize, it’s the extreme Q readings that appear to be most powerful (again, more to come on that topic in a later post).
As we write this today, the current Q is well into our fourth quartile…where’s your focus?