A comparative overview of major equity indices across the globe using historic and forward looking Price-to-Earnings (P/E) ratios and related metrics.
Explore global stock market valuations based on P/E and other valuation metrics with our interactive map. Markets are classified as Highly Overvalued, Overvalued, Fairly Valued, Undervalued, and Highly Undervalued.
Highly Overvalued
Overvalued
Fairly Valued
Undervalued
Highly Undervalued
Country | Index | Category | Valuation Score | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Philippines | PSEi Index | Undervalued | 84 | 10.2 | 12.0 | 0.8 | - | - | 18.2 | - |
Mexico | S&P/BMV IPC Index | Undervalued | 83 | 16.1 | 18.2 | 0.6 | - | - | 19.7 | - |
France | CAC 40 Index | Undervalued | 83 | 17.3 | 20.5 | 0.5 | 1.1 | 15.0 | 17.4 | 3.12% |
South Korea | KOSPI Index | Undervalued | 82 | 13.3 | 17.4 | 0.5 | 0.6 | 10.9 | 12.2 | 1.98% |
United Kingdom | FTSE 100 Index | Undervalued | 82 | 14.1 | 19.4 | 0.2 | 1.7 | 13.0 | 16.9 | 3.34% |
Poland | WIG Index | Undervalued | 82 | 12.2 | 15.7 | 0.3 | - | - | 13.1 | - |
South Africa | FTSE/JSE Africa All Shares Index | Undervalued | 80 | 14.5 | 20.3 | 0.5 | 0.5 | 11.0 | 15.2 | 2.94% |
Malaysia | FTSE Bursa Malaysia KLCI Index | Undervalued | 80 | 15.1 | 15.9 | 1.7 | - | - | 16.7 | 4.14% |
Italy | FTSE MIB Index | Fairly Valued | 79 | 12.5 | 20.6 | 0.2 | 2.2 | 11.9 | 13.8 | 4.03% |
Japan | NIKKEI 225 Index | Fairly Valued | 78 | 18.1 | 27.1 | 0.7 | 1.5 | 16.1 | 19.4 | 2.22% |
Saudi Arabia | Tadawul Index | Fairly Valued | 77 | 17.6 | 21.4 | 0.9 | - | - | 17.2 | - |
Brazil | Bovespa Index (Ibovespa) | Fairly Valued | 76 | 9.6 | 13.8 | 0.3 | 5.6 | 9.5 | 12.1 | - |
Canada | S&P/TSX Composite Index | Fairly Valued | 76 | 21.2 | 25.2 | 1.2 | 1.1 | 17.6 | 18.1 | 2.45% |
Germany | DAX 40 Index | Fairly Valued | 75 | 19.8 | 24.0 | 1.0 | 0.7 | 15.5 | 16.1 | 2.45% |
Netherlands | AEX Index | Fairly Valued | 75 | 17.8 | 22.7 | 0.3 | -7.2 | 18.3 | 17.3 | 2.88% |
Singapore | STI Index | Fairly Valued | 74 | 15.5 | 16.6 | 1.5 | - | - | 13.5 | 4.48% |
Taiwan | TWSE Index | Fairly Valued | 74 | 21.3 | 27.6 | 1.4 | 1.2 | 18.0 | 17.2 | 2.50% |
Australia | S&P/ASX 200 Index | Fairly Valued | 74 | 21.2 | 22.0 | 1.1 | 3.0 | 19.8 | 17.2 | 3.04% |
Indonesia | Jakarta Stock Exchange Composite Index | Fairly Valued | 73 | 16.8 | 21.8 | 1.6 | 0.4 | 11.7 | 17.0 | 4.85% |
China | SSE Composite Index | Fairly Valued | 73 | 18.0 | 17.3 | 15.5 | 1.0 | 15.3 | 16.4 | 2.25% |
Switzerland | SMI Index | Fairly Valued | 71 | 18.1 | 21.5 | 2.6 | - | - | 20.3 | - |
India | NIFTY 50 Index | Fairly Valued | 70 | 21.9 | 33.2 | 0.8 | 22.5 | 21.7 | 21.0 | 1.36% |
Turkey | BIST 100 Index | Overvalued | 69 | 14.0 | 8.0 | 0.3 | - | - | 9.8 | - |
United States | S&P 500 Index | Overvalued | 69 | 25.6 | 34.6 | 1.6 | 2.1 | 22.8 | 18.8 | 1.22% |
Thailand | SET Index | Overvalued | 68 | 16.2 | 14.1 | 2.5 | - | - | 15.5 | 4.21% |
Sweden | OMX Stockholm 30 Index | Overvalued | 66 | 18.1 | 19.0 | 2.3 | - | - | 15.9 | 2.87% |
Hong Kong | Hang Seng Index | Highly Overvalued | 63 | 13.1 | 11.3 | -31.9 | -1.6 | 14.3 | 11.6 | 3.22% |
Spain | IBEX 35 Index | Highly Overvalued | 60 | 12.8 | 21.7 | -0.1 | 3.9 | 12.4 | 14.2 | 3.71% |
Europe | STOXX Europe 600 Index | Undervalued | 82 | 18.8 | 21.3 | 0.6 | 0.8 | 15.3 | 17.6 | - |
Emerging Markets | MSCI Emerging Markets Index | Fairly Valued | 73 | 16.4 | 15.7 | 2.3 | 0.9 | 14.0 | 13.9 | - |
Developed World | MSCI World Index | Fairly Valued | 72 | 24.5 | 29.9 | 2.0 | 1.2 | 20.4 | 18.8 | - |
Global Equity Markets | MSCI All Country World (ACWI) Index | Fairly Valued | 71 | 23.1 | 27.3 | 2.0 | 1.2 | 19.4 | 17.9 | - |
Last Updated | 30-Sep-25 | 30-Sep-25 | 30-Sep-25 | 30-Sep-25 | 30-Sep-25 | 30-Sep-25 | 30-Sep-25 | 31-Aug-25 |
Disclaimer: The data is for informational purposes only and should not be considered investment advice. While we strive for accuracy, we do not guarantee the completeness or reliability of the information.
The country/index valuation score is based on the assessment of the index based on five major parameters:
While the PE Ratio captures the current market position, the CAPE ratio and the 5-year historical PEG Ratio capture the index performance from a historical or medium-term lens. Similarly, Forward PE and Forward PEG ratios are based on future expectations about the index earnings per share.
Next, let’s break down and analyse the scoring methodology for each of the constituents of the index:
The PE Ratio score is calculated using the percentile rank, which allows users to evaluate how expensive or cheap an index is relative to its own PE ratio over the last 25 years.
CAPE ratio compares the price of an index to its 10-year average inflation-adjusted earnings, smoothing cyclical fluctuations. The scores are assigned based on deviation from PE ratios, as it provides a referential context.
Forward PE is also scored relative to the current PE levels. It is a reflection of overall future optimism about the earnings from the index.
Historical PEG Ratio scores are calculated based on 5-year annualised growth in earnings per share for an index. The scores are calculated to reward growth at reasonable price levels. Ratios around 1-1.5 are categorised as Fairly Valued, receiving a neutral score, while values below 1 indicate that the index is undervalued and receives higher scores (A ratio between 0.2 and 0.5 gets the highest score). Extremely high or negative PEGs (due to declining earnings) are penalised. Scores are calibrated using a banded scale to avoid over-rewarding unsustainable growth or deeply cyclical downturns.
Forward PEG uses 1-year earnings growth forecasts. Scores favour ratios between 0.2–0.5, indicating healthy earnings at modest valuations. Forward PEGs above 1.5 and negative PEGs are penalised due to implied risk or volatility. It offers a dynamic, sentiment-sensitive valuation-growth composite signal.
The consolidated overall score is calculated as the weighted average of each of the five components. The weights are subjective and reflect the balance between historical parameters and future forward-looking estimates.
As we take weighted averages, outlier values are addressed by applying upper and lower bounds to ensure stability in the dataset.