The Q Ratio and Market Valuation: December Update
Note: This update includes December close data.
The Q Ratio is a popular method of estimating the fair value of the stock market developed by Nobel Laureate James Tobin. It's a fairly simple concept, but laborious to calculate. The Q Ratio is the total price of the market divided by the replacement cost of all its companies. Fortunately, the government does the work of accumulating the data for the calculation. The numbers are supplied in the Federal Reserve Z.1 Financial Accounts of the United States, which is released quarterly.
Note on Revisions
We have received a number of inquiries lately regarding the recent drastic shift in valuations for the Q-Ratio and subsequently the Average of the Four. The Q Ratio relies heavily on the Federal Financial Accounts Z.1 data which is subject to revision. After inspection, we found that there were revisions made to the data that affect these calculations.
Here's a note from April 2021 regarding the 2020 Q4 data, which was not readily apparent:
April 01, 2021
2020:Q4 data correction
Due to a data error, the 2020:Q4 publication of the Financial Accounts of the United States has been reposted, including all affected data files and DDP. The 2020:Q4 value for series FL075035243 Interest rates and price indexes; owner-occupied real estate CoreLogic national (SA) was corrected. Levels and revaluations for input series FL165035023, FL115035023, FL105035023, which directly use the CoreLogic price index, have been corrected. FL010000076 has also been corrected. Corrections to the mentioned series resulted in corrections to numerous calculated series, including net worth of households and nonprofits (series FL152090005), which now stands at $130.37 trillion, $0.21 trillion higher than originally reported on March 11, 2021. $198 billion of this correction to net worth of household and nonprofits is from series FL115035023 Nonfinancial noncorporate business; residential real estate at market value, which is a component of LM152090205 Households and nonprofit organizations; proprietors' equity in noncorporate business -- an asset of the household and nonprofit organizations shown on table B.101 line 28...
These revisions - the change in Nonfinancial Corporate Business; Net Worth figure - resulted in a difference of 35% between the initial figures and the revisions.
Here's another, more general note on revisions to the Fed Accounts data:
"The data in the Financial Accounts come from a large variety of sources and are subject to limitations and uncertainty due to measurement errors, missing information, and incompatibilities among data sources. The size of this uncertainty cannot be quantified, but its existence is acknowledged by the inclusion of “statistical discrepancies” for various sectors and financial instruments."
The ratio subsequent to the latest Z.1 Fed data (through 2021 Q3) is based on a subjective process that factors in the monthly closes for the Vanguard Total Market ETF (VTI).
Unfortunately, the Q Ratio isn't a very timely metric. The Z.1 data is over two months old when it's released, and three additional months will pass before the next release. To address this problem, our monthly updates include an estimate for the more recent months based on changes in the VTI price (the Vanguard Total Market ETF) as a surrogate for Corporate Equities; Liability.
The first chart shows the Q Ratio from 1900 to the present.
Interpreting the Ratio
The data since 1945 is a simple calculation using data from the Federal Reserve Z.1 Statistical Release, section B.103, Balance Sheet and Reconciliation Tables for Nonfinancial Corporate Business. Specifically, it is the ratio of Market Value divided by Replacement Cost. It might seem logical that fair value would be a 1:1 ratio. But that has not historically been the case. The explanation, according to Smithers & Co. (more about them later) is that "the replacement cost of company assets is overstated. This is because the long-term real return on corporate equity, according to the published data, is only 4.8%, while the long-term real return to investors is around 6.0%. Over the long-term and in equilibrium, the two must be the same."
The average (arithmetic mean) Q Ratio is about 0.79. The chart below shows the Q Ratio relative to its arithmetic mean of 1 (i.e., divided the ratio data points by the average). This gives a more intuitive sense to the numbers. For example, the all-time Q Ratio high at the peak of the Tech Bubble was 1.67 — which suggests that the market price was 129% above the historic average of replacement cost. The all-time lows in 1921, 1932, and 1982 were around 0.29, which is approximately 60% below replacement cost. That's quite a range. The latest data point is 140% above the mean. Remember, this is extrapolated using the monthly VTI close and the most recent Q Ratio - which is 1.70 - as of Q3 2021.
Another Means to an End
Smithers & Co., an investment firm in London, incorporates the Q Ratio in their analysis. In fact, CEO Andrew Smithers and economist Stephen Wright of the University of London co-authored a book on the Q Ratio, Valuing Wall Street. They prefer the geometric mean for standardizing the ratio, which has the effect of weighting the numbers toward the mean. The chart below is adjusted to the geometric mean, which, based on the same data as the two charts above, is 163%.
The Message of Q: Overvaluation
Of course, periods of over-and under-valuation can last for many years at a time, so the Q Ratio is not a useful indicator for short-term investment timelines. This metric is more appropriate for formulating expectations for long-term market performance. As we can see in the next chart, peaks in the Q Ratio are correlated with secular market tops - the Tech Bubble peak and current market values being extreme outliers.
For a quick look at the two components of the Q Ratio calculation, here is an overlay of the two since the inception of quarterly Z.1 updates in 1952. There is an obvious similarity between the Fed's estimate of "Corporate Equities; Liabilities" and a broad market index, such as the S&P 500 or VTI. It is the more volatile of the two, but this component can be easily extrapolated for the months following the latest Fed data. The less volatile underlying Net Worth is not readily estimated from coincident indicators.
The regressions through the two data series to help to illustrate the secular trend toward higher valuations.