The Global Buffett Indicator

The "Buffett Indicator" is a widely respected valuation metric famously endorsed by Warren Buffett, who once called it "probably the best single measure of where valuations stand at any given moment."

The metric is calculated by taking the Total Market Capitalization of a country's stock market and dividing it by the country's Gross Domestic Product (GDP). It essentially measures the size of the financial markets against the size of the underlying real economy. When the ratio is historically high, the stock market is considered overvalued. When it is low, the market is viewed as undervalued.

How We Plot It

At RatioPlotter.eu, our data engine seamlessly combines highly volatile daily stock market data from our data providors with smooth, annualized macroeconomic GDP data published by the International Monetary Fund (IMF).

To view the Buffett Indicator for various global economies, click on any of the country cards below. This will load our main Ratio Plotter with the IMF GDP tracker in the denominator and the corresponding country ETF in the numerator. We use these ETF's as a proxy for each nation's total equity market, while this is a common industry standard for real-time tracking, we believe in being fully transparent about our data sources and thus you should know this ETF is incomplete and can give a slightly twisted picture of the actual Buffett indicator. Above all, in our Buffett indicators, you cannot compare the y axis values across charts (eg. compare Denmark's y axis value against Australia's). On the standard Buffett indicators the y axis represents the total stock Market valuation divided by the GDP (e.g. market 20x GDP). In our Buffett indicators, the y axis represents the country ETF price divided by the GDP, not the total market equity value divided by the GDP. For some of our Buffett indicators we provide, below the chart, a link to a premium plot which estimates the Buffett indicator in the correct scale for That country (see Australia for instance), this link is accompanied by an explanation for how it was estimated. Concluding: our charts can tell if stocks are above or below their average relative price to GDP, but to really know if stocks from a certain country are cheap you can either configure the right coefficients in the equation plotter (see Australia example below the chart), or you can look at the world PE ratio website.

⚠️ Important Note for Advanced Users:
If you want to create a custom Buffett Indicator for a country not listed below, you must ensure that the ETF or Index you place in the numerator is priced in US Dollars (USD).

Our backend dynamically fetches the IMF Nominal GDP database (using the IMF-NGDPD-[Code] ticker syntax), which is strictly denominated in USD. If you divide an asset priced in a local currency (e.g., Euros or Yen) by a GDP metric priced in USD, the resulting chart will be mathematically invalid due to extreme exchange rate distortions.

Live Global Indicators

Click any country below to automatically generate its historical Buffett Indicator chart. (Using US-listed iShares MSCI ETFs as broad market proxies).

Select a country below to load the chart.

World 2 🌐 T-VT / IMF-NGDPD-WEOWORLD World 🌐 T-URTH / IMF-NGDPD-WEOWORLD Australia 🇦🇺 T-EWA / IMF-NGDPD-AUS Brazil 🇧🇷 T-EWZ / IMF-NGDPD-BRA China 🇨🇳 T-MCHI / IMF-NGDPD-CHN Canada 🇨🇦 T-EWC / IMF-NGDPD-CAN Denmark 🇩🇰 T-EDEN / IMF-NGDPD-DNK Germany 🇩🇪 T-EWG / IMF-NGDPD-DEU France 🇫🇷 T-EWQ / IMF-NGDPD-FRA Hong Kong 🇭🇰 T-EWH / IMF-NGDPD-HKG India 🇮🇳 T-INDA / IMF-NGDPD-IND Indonesia 🇮🇩 T-EIDO / IMF-NGDPD-IDN Ireland 🇮🇪 T-EIRL / IMF-NGDPD-IRL Israel 🇮🇱 T-EIS / IMF-NGDPD-ISR Japan 🇯🇵 T-EWJ / IMF-NGDPD-JPN Norway 🇳🇴 T-ENOR / IMF-NGDPD-NOR Saudi Arabia 🇸🇦 T-KSA / IMF-NGDPD-SAU Switzerland 🇨🇭 T-EWL / IMF-NGDPD-CHE Sweden 🇸🇪 T-EWD / IMF-NGDPD-SWE United Kingdom 🇬🇧 T-EWU / IMF-NGDPD-GBR United States 🇺🇸 T-SPY / IMF-NGDPD-USA United States 2 🇺🇸 T-ITOT / IMF-NGDPD-USA Vietnam 🇻🇳 T-VNAM / IMF-NGDPD-VNM

Cannot find a country?

Use the ticker selector to plot the ratio of the country ETF (ticker selector category ETF), by the country GDP (ticker selector category GDP).

Ticker Data Usage and Sourcing

Ticker Data Sources

Our data is provided from a variety of sources: The Federal Reserve (FED-* tickers), The World Bank (WB-* tickers), The International Monetary Fund (IMF-* tickers), Tiingo (T-* tickers covering: stocks, ETFs, etc), Ourselves (MATH-* tickers). The ticker data sources vary according to ticker prefix as you can see. Our ticker selector allows browsing each ticker and their description and placing them on a basket for use.

SEA() Ticker function

For ratio plotting, the SEA(Ticker) function can only be used in the denominator argument and only for plotting TickerA over SEA(TickerA) like this: T-CRUD/SEA(T-CRUD). The SEA() ticker function works by calculating the median yearly seasonality as a new dataset (see the seasonality page for details and examples of this calculation). This one year seasonality dataset is then unfolded exactly the same for many years and directly inputted as a synthetic ticker into this plotting page arguments. Example: Ratio plot of Crude Oil by its seasonality: T-CRUD/SEA(T-CRUD). To write custom SEA() function synthetic tickers simply input your ticker of choice between the parentheses such as SEA(T-CRUD).

RAT() Ticker function

The RAT() ticker function works by calculating the ratio between two tickers as a new dataset. This dataset is then directly inputted as an synthetic ticker into the plotting page arguments. To write custom RAT() tickers simply input your two tickers of choice between the parentheses, separating them with a comma. Such as RAT(T-AAPL, T-MSFT). This can only be once, synthetic tickers like RAT(RAT(T-AAPL, T-MSFT), SEA(T-qtec)) are invalid for the time being. Example, how is Google outpacing Microsoft in terms of CAGR curve: RAT(T-GOOGL,T-MSFT)/MATH-CAGR_PCT-2

LAG() Ticker function

The LAG() ticker function is used to create a lagged version of an asset. This lags a dataset with an amount of days equal to the lag value in the ticker. To write custom LAG() tickers simply input your ticker of choice and lag value between the parenthesis, seperated by a comma. Such as LAG(T-AAPL, 10). Lag values can only be inputted into the denominator as positive whole numbers between 1 and 7000 days. Using the ratio plotter with LAG(), a ticker can be analyzed in its auto correlation, or correlation to a nother ticker with a gap of X days (e.g.ratio plot of TICKERA by LAG(TICKERB, 30)).

MATH- Tickers

The MATH- tickers are custom mathematical tickers made by us (e.g. MATH-CAGR_PCT-2 for 2% exponential growth, MATH-CONST_VEC-1 for a constant value of 1 each day). To find the list of valid MATH tickers go to the Ticker Selector Page and search through the MATH category list. Important to mention is that when using the MATH tickers the last observed date for this MATH ticker will be far in te future.

CURVE_FROM_VECTOR()

The CURVE_FROM_VECTOR function allows transforming your own data vectors into plottable data. It is perhaps one of the most powerfull functions. As the name says, it creates a vector from user entered data points. This function is to be used when you cannot find the data you need in the ticker selector, or if your data is proprietary such as your own private company that you will plot against perhaps competitor's sales or a competitor's stock price. Learn using CURVE_FROM_VECTOR() here: price to x demo with CURVE_FROM_VECTOR() or on the details below. The price to x has an expression builder to require less typing from the user. Example usage: CURVE_FROM_VECTOR([198.27, 211.92, 245.12, 281.72, 318.27], '2026-06-30', 'last', '1y', 'linear', 'forward_fill') The first argument is the time ordered list of data points, separated by commas. The second argument ('2026-06-30') is the one data anchor we need to plot these values over time. The third argument ('last') tells the system to apply the anchor date to the last value, another value for this parameter is 'first'. The '1y' tells the values fall on the exact same date on each year (or closest if leap year). The '1y' could also be '1q' or '1m' for quarter or month. The same considerations made for year apply. The 'linear' parameter tells the system how to interpolate the values for days in between the value data points given, another possible value here is 'forward_fill'. The 'forward_fill' last parameter is about the extrapolation, its possible values are: 'none', 'forward_fill', 'linear'.

Default Ticker Column Projections & Adjustments

Let's take T-GOOGL as an example. When the projection variable is left blank, the ratio plotter will use the default projection variable which is -AdjClose (close price adjusted for both dividends and splits). This means that when you input T-GOOGL into the ratio plotter it will actually plot the Tiingo split and dividend adjusted close price of GOOGL (T-GOOGL-AdjClose by T-MSFT-AdjClose). If you want to plot the split (but not dividend) adjusted close price of GOOGL you can input T-GOOGL-SplitAdjClose. All projection columns are provided by Tiingo, with exception of the -SplitAdjClose column which is calculated by our system.

Plotting Dividends Reinvested at Your Portfolio Return Rate

You can do this with the function: SIMUL_TICKER_DIV_REINVEST(T-MSFT,0.25,1,'2022-08-01')
This is a function available only for users with the advanced subscription. The first argument is the Ticker simulated, the second is your portfolio CAGR rate (in this case 0.25 = 25% yoy). We apply the daily equivalent rate though. The third argument is the initial investment in the currency of the stock ticker. The last argument is the start date. For a full example see our article on dividend reinvestment simulation or our article looking back at Warren Buffet's investment in Coca-Cola and the results of its juicy dividends.

Calculating slope, velocity, or derivating (these are all synonyms)

Let's say we to know how fast China is growing its GDP per day (GDP daily velocity). For this we can use the formula: ROLLING_SLOPE(IMF-NGDPD-CHN, 400, 1). The ticker is the IMF Chinese yearly GDP growth in billions of dollars, with revisions on a daily basis and with future forecasts also. The second argument 400 is the size of the lookback window in days to calculate the rolling, smoothed, slope. For GDP it has to be more than 365 days otherwise the GDP reviews cause the slope to not be smooth enough due to yearly GDP reviews, publishing. For other data such as stock prices you can have windows of 20, 30 days for instance. The number 1 shown as third argument implies we want to leave the slope at the granularity 1 which is growth per 1 day, or whether we want to perhaps to use an argument of 365 which will annualize the slope to the yearly growth velocity by multipling it by 365 (numbers smaller than 0 such as 0.001 are also allowed here). The value of this slope function for the IMF-NGDPD-CHN ticker is in billions of dollars GDP growth on per 1 day basis (if the last argument is 1). That always matches the ticker unit of measurement which in our case is in billions of dollars. Up to 2026, the best growth rates of China were 8 billion dollars per day, a staggering amount which seems to be stagnating lately. We can check these 9 billion dollar daily growth rates in this example of the ROLLING_SLOPE function: China GDP Velocity

Z scaling, bringing data to the normal curve Z score scale, normalizing

You can do this with the function: Z_SCALE. Examples: Z_SCALE(T-MSFT), Z_SCALE(RAT(T-GLD,T-SLV)), Z_SCALE(ROLLING_SLOPE(T-COM,90,1))
This is a function available only for users with the advanced subscription. The single argument can be a Ticker or another vector function. For a full example see our macro quadrant plot.