The Buffett Indicator is the ratio of total United States stock market valuation to GDP. As of May 13, 2021 we calculate the Buffett Indicator as:
By our calculation that is currently 79% (or about 2.6 standard deviations) above the historical average, suggesting that the market is Strongly Overvalued. These are historical, all-time highs. However, with interest rates at historic lows, there is reason to suspect that "this time is different" may hold true.
The historical chart of the Buffett Indicator is shown below - for much more analysis and information on our data sources, methodology, and counterpoints, keep scrolling.
The Buffett Indicator is the ratio of total US stock market valuation to GDP. Named after Warren Buffett, who called the ratio "the best single measure of where valuations stand at any given moment". (Buffett later walked back those comments, hesitating to endorse any single measure as either comprehensive or consistent over time, but this ratio remains credited to his name). To calculate the ratio, we need to get data for both metrics: Total Market Value and GDP.
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. 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 per Wilshire, 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 $50.5T.
The Gross Domestic Product (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 or economic growth. The GDP is calculated and published quarterly, 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 $22.6T. 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 79% above the historical average, and considered Strongly Overvalued (see our ratings guide for more information).
What can the Buffett Indicator tell us about future stock market returns? This is probably the most relevant question investors have when considering valuation models. While it is not possible to predict the future, it is easy enough to look at historical data to see how the market has performed after periods of high and low valuation per the model.
The above chart shows monthly datapoints from 1950 to 2016, mapping the relative value of our Buffett Indicator model (x-axis) against the subsequent 5 year S&P500 returns (y-axis). The colored, dashed vertical lines indicate the same under/overvaluation bands as shown in previous models (i.e., values to the right of the dark red line indicate datapoints that were > 2 standard deviations above the trendline, indicating the market was 'Strongly Overvalued').
As an example, the rightmost point on the chart (labeled as Example 2 in the chart) corresponds with March 2000. At this point the US aggregate stock market value was $15.5T, vs a GDP of $10.0T, giving a raw BI ratio of 155%. At the time, this value was 67% (2.2 standard deviations) above the long term BI trend. In March 2000 the S&P500 was at $1,499. Five years later, in March 2005, the S&P00 had fallen to $1,181. This was a 21% nominal decline, and a 30% 'real' decline after adjusting for inflation during the 5 years.
Overall, Figure 5 above shows that there is a slight correlation between BI valuation and subsequent S&P00 returns. The highest stock market returns tend to come after periods of undervaluation (left side of the chart). Periods of overvaluation (right side of the chart), particularly at the extreme, tend to be followed by negative S&P00 returns.
A few final comments on this:
It is important to call out that no single metric is illustrative of the entire market, of course. The primary criticism of using the Buffett Indicator as a valuation metric (and particularly in 2021 using it as a metric to justify the overvaluation of the market), is that it does not address the state of non-equity asset markets. In truth, investors have many different asset classes to consider and evaluate when considering portfolio distribution - e.g., corporate bonds, real estate, and commodities.
The proverbial elephant in the room here is the bond market, expressed as interest rates. Very generally speaking, bonds represent a lower-risk asset as an alternative to equity (stock) markets, and the two have a highly interdependent relationship.
The 50,000ft overview on interest rates is as follows. When interest rates are high, bonds pay a high return to investors, which lowers demand (and prices) of the riskier equities. Additionally, higher interest rates means it's more expensive for businesses to borrow money, making it harder to borrow cash as a way to finance growth. Which is to say any business that takes on debt will face relatively higher interest payments, and therefore less profits. And again, less profits means lower stock prices. The corollary to all this is also true. Low interest rates means bonds pay less to investors, which lowers demand for them, which raises stock prices in relation to bonds. Low interest rates make it easy for corporations to borrow cash cheaply to finance growth. Corporate interest payments will be low, making profits high. This is all to say, if interest rates are high, stocks go down. If interest rates are low, stocks go up.
Interest rates today are lower than they've ever been. Below is a chart showing the interest rate of the 10Year US Treasury Bond. This is the most vanilla bond there is, and over the last 50 years the interest rate on it has averaged 6%. Back during the peak of the .com bubble (when the Buffett Indicator was very high), the 10Y Treasury rate was a bit higher than average, around 6.5%, showing that low interest rates weren't juicing the stock market. Today the Buffett Indicator is roughly the same distance above its historical average as it was during the .com bubble, but interest rates are at an all time low, near 1%. This can be interpreted to mean that during the .com bubble, equity investors had other good options for their money - but they still piled recklessly into stocks. Whereas today, investing in bonds returns so little that you may actually lose money to inflation. Today's investors need to seek a return from somewhere, and low interest rates are forcing them to seek that return from riskier assets, effectively pumping up the stock market. While this doesn't justify the high Buffett Indicator on any fundamental basis, it does suggest that the market today is less likely to quickly collapse like it did in 2000, and that it may have reason to stay abnormally high for as long as interest rates are abnormally low.
For additional detail on the effect interest rates have on stock prices, view our Interest Rate Model.
Below are some classics on fundamentals-based value investing. While these aren't specifically about the Buffett Indicator, they espouse similar ideas, and are strongly recommended resources.
An accessible, and intuitive, guide to stock valuation. Valuation is at the heart of any investment decision, whether that decision is to buy, sell, or hold. In The Little Book of Valuation, expert Aswath Damodaran explains the techniques in language that any investors can understand.
The greatest investment advisor of the twentieth century, Benjamin Graham, taught and inspired people worldwide. The father of value investing, this is the updated classic from 1949.
The definitive textbook on all topics related to investment valuation tools and techniques.
Whether you’re considering your first 401k contribution, contemplating retirement, or anywhere in between, A Random Walk Down Wall Street is the best investment guide money can buy.
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
Used to estimate aggregate market value prior to 1970.
|Wilshire 5000||Wilshire 5000
Used to estimate aggregate market value, 1970-present. Adjustments made in-line with Wilshire guidance that in 1985 a 1-point change in the index corresponded to a $1B change in aggregate market value, and by 2013 a 1-point index change corresponded to $1.15B in market value. That drift is extrapolated on a straight-line basis to present day.
|GDP||U.S. Bureau of Economic Analysis, Gross Domestic Product [GDP], retrieved from FRED, Federal Reserve Bank of St. Louis;
Used for all historical GDP data.
|GDP Now||GDPNow - Federal Reserve Bank of Atlanta
Used to estimate current quarter GDP.