The Buffett Indicator is the ratio of total US stock market valuation to GDP. As of November 25, 2020 we calculate the Buffett Indicator as:
By our calculation that is currently 70% higher than the historical average, suggesting that the market is Strongly Overvalued. It has not been around this level since the Internet Bubble of the early 2000's. The historical chart of this indicator is shown below - for much more analysis and information on our data sources and methodology, keep reading.
The Buffett Indicator is the ratio of total US stock market valuation to GDP. To calculate the ratio, we need to get data for those two metrics.
The most common measurement of the aggregate value of the US stock market is the Wilshire 5000. This is available directly from Wilshire (links to all data sources below), with monthly data starting in 1971, and daily measures beginning in 1980. (In fact, today it is updated every second, but we use daily closing value here). The Wilshire index was created such that a 1-point increase in the index corresponds to a $1 billion increase in US market cap. Since inception, that 1:1 ratio has drifted, and as of Dec 2013 a 1-point increase in the index corresponded to a $1.15 billion dollar increase. We adjust the data back to inception (and projected going forward) on a straight-line basis to compensate for this drift. For example, the Sep 2020 Wilshire Index of 35,807 corresponds to a total real market cap value of $42.27T USD.
For data prior to 1970 (where Wilshire data is not available) we use the Z.1 Financial Account - Nonfinancial corporate business; corporate equities; liability, Level, published by the Federal Reserve, which provides a quarterly estimate of total market value back to 1945. In order to integrate the datasets, we index the Z.1 data to match up to the 1970 Wilshire starting point.
Combined, these data make our Composite US Stock Market Value data series, shown below. Our estimate of current composite US stock market value is $44.8T.
The GDP represents the total production of the US economy. This is measured quarterly by the US Government's Bureau of Economic Analysis. The GDP is a static measurement of prior economic activity - it does not forecast the future or include any expectation or valuation of future economic activity. The GDP is calculated and published quarterly, but unfortunately it is done several months in arrears, such that by the time the data is published it is several months old. In order to provide updated data for the most recent quarter we use the most recent GDPNow estimate published by the Federal Reserve Bank of Atlanta. The GDP data is all nominal and not inflation adjusted. Our estimate of current (annualized) GDP is $21.7T. A historical chart of GDP is shown below.
Given that the stock market represents primarily expectations of future economic activity, and the GDP is a measure of most recent economic activity, the ratio of these two data series represents expected future growth relative to current performance. This is similar in nature to how we think about the PE ratio of a particular stock. It stands to reason that this ratio would remain relatively stable over time, and increase slowly over time as technology allows for the same labor and capital to be used ever more efficiently.
Now, let's look at how the Buffett Indicator has changed in the last ~75 years.
Searching for "Buffett Indicator" online can bring up a variety of different scores, which is a bit surprising for such a simple and straightforward metric. There are only two variables involved, so whats going on? We explain our methodology and data sources in detail on this page, and so are transparent about how we achieve our resulting score. That said, below are the main inconsistencies we see when comparing our data to other models that also claim to show the Buffett Indicator.
We've seen two main inconsistencies from other models here. First, some models don't use Wilshire 5000 and instead continue using the Fed's Z.1 equities measure for the full dataset. Second, for those using Wilshire, that dataset (per Wilshire's description) requires manual adjustments in order to correlate the reported Wilshire point value to corresponding USD values over time.
GDP is widely reported by the US Bureau of Economic Analysis and not controversial, however there are a few snags that can change a model result. First is that GDP changes over time; the BEA adjusts GDP numbers (including previously reported "actuals") as additional detail comes in, often several quarters after the fact. This is routine and the adjustments are often immaterial to a model as blunt as the Buffett Indicator, but these alterations could be missed.
Second, and more likely, is the consideration that GDP is announced well in arrears. Initial results of prior quarter GDP are not available until several months into the following quarter. If using only this GDP data from the BEA to find the Buffett Indicator, we would be comparing today's market value to a GDP number 3-6 months ago! This might be fine in normal environments where GDP growth is fairly consistent, but it is nonsensical during times of GDP volatility. As such, our model uses estimates of current GDP, to be compared to current market value.
Lastly, we'd note that some other Buffett Indicator models use Gross National Product (GNP) instead of GDP. The two differ slightly, and so will create a different outcome, which in our view may be meaningful but is not the Buffett Indicator.
Overall, we'd emphasize that there is nothing overwhelmingly 'right' or 'wrong' about these different measures for the Buffett Indicator - what matters most is the relative position of the measurement over time, compared to its own historical performance.
The historic ratio of total market value to GDP (aka the Buffett Indicator) can be seen below.
In the chart above, the exponential regression line shows the natural growth rate of the indicator. This shows the upward historical trend that expectations for future growth have risen faster over time than actual economic output. This makes sense, as technological progress drives exponential returns.
To make the context of our current position more clear, we can draw the regression line horizontally and remap the data as the percentage above or below that historical average. This is shown below, along with band lines showing +/- a standard deviation. Generally speaking, about 70% of the time the Buffett Indicator should be within +/- 1 standard deviation from the average, and 98% of the time it should be +/- 2 standard deviations from the average.
And finally, below is the same chart, but with only the last twenty years of data, so that recent market activity can be seen more clearly.
The current market-to-GDP ratio is 70% above the historical average, and considered Strongly Overvalued (see our ratings guide for more information).
The below table cites all data and sources used in constructing the charts, or otherwise referred to, on this page.
|Z.1 Financial Account||Board of Governors of the Federal Reserve System (US), Nonfinancial corporate business; corporate equities; liability, Level [NCBEILQ027S], retrieved from FRED, Federal Reserve Bank of St. Louis|
|Wilshire 5000||Wilshire 5000, with proprietary adjustments to index data.|
|GDP||U.S. Bureau of Economic Analysis, Gross Domestic Product [GDP], retrieved from FRED, Federal Reserve Bank of St. Louis;|
|GDP Now||GDPNow - Federal Reserve Bank of Atlanta, estimate of current quarter GDP.|