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How closely the world's stock markets actually move together

Tokyo's daily correlation with the S&P 500 looks negligible and is not: the two markets never trade at the same time. Shift Tokyo one session forward and the number triples. That gap is the most useful thing on this page.

12 indices · data through 28 Aug 2026

Monthly return correlation over each pair's full overlapping history. Warmer is more correlated; blue is negative. Hover a cell for the number of months behind it.
 S&PNDQDJIARUTHSIN225KOSPISHCOMPDAXFTSESX5ESENSEX
S&P 5000.870.950.830.560.370.410.120.720.740.760.26
Nasdaq Composite0.870.760.850.530.470.420.110.660.620.680.27
Dow Jones Industrial Average0.950.760.790.560.510.420.130.720.740.750.27
Russell 20000.830.850.790.520.530.420.120.660.650.690.31
Hang Seng Index0.560.530.560.520.350.360.210.490.590.520.26
Nikkei 2250.370.470.510.530.350.490.110.490.460.530.16
KOSPI0.410.420.420.420.360.490.060.390.410.410.24
Shanghai Composite0.120.110.130.120.210.110.060.130.110.110.06
DAX0.720.660.720.660.490.490.390.130.720.930.29
FTSE 1000.740.620.740.650.590.460.410.110.720.770.23
EURO STOXX 500.760.680.750.690.520.530.410.110.930.770.31
BSE SENSEX0.260.270.270.310.260.160.240.060.290.230.31
The same measure over the last ten years only, from 2016-09. Read it against the matrix above: the pairs that moved are the ones whose relationship is recent, not structural.
 S&PNDQDJIARUTHSIN225KOSPISHCOMPDAXFTSESX5ESENSEX
S&P 5000.940.930.860.350.660.600.380.800.600.770.56
Nasdaq Composite0.940.790.800.330.650.620.380.680.460.650.49
Dow Jones Industrial Average0.930.790.850.300.630.530.340.820.670.800.56
Russell 20000.860.800.850.280.640.580.390.700.590.690.52
Hang Seng Index0.350.330.300.280.130.240.580.360.380.370.27
Nikkei 2250.660.650.630.640.130.760.330.620.510.640.43
KOSPI0.600.620.530.580.240.760.370.550.490.560.39
Shanghai Composite0.380.380.340.390.580.330.370.310.270.330.24
DAX0.800.680.820.700.360.620.550.310.780.950.54
FTSE 1000.600.460.670.590.380.510.490.270.780.790.49
EURO STOXX 500.770.650.800.690.370.640.560.330.950.790.52
BSE SENSEX0.560.490.560.520.270.430.390.240.540.490.52
The time-zone correction, market by market. 'Same day' pairs sessions by calendar date; 'lagged' compares each market's session with the S&P 500's previous one. Where the two differ, the same-day figure is the misleading one.
IndexDaily, same dayDaily, one session behind the S&PMonthly
Nikkei 2250.100.320.37
Hang Seng Index0.190.340.56
KOSPI0.110.240.41
Shanghai Composite0.030.070.12
BSE SENSEX0.130.140.26
FTSE 1000.480.250.74
EURO STOXX 500.520.240.76
DAX0.520.200.72
Nasdaq Composite0.860.030.87
Russell 20000.85-0.030.83
Dow Jones Industrial Average0.96-0.060.95
Daily correlation with the S&P 500 inside two crisis windows, against the same measure over the whole record.
IndexWhole recordGlobal financial crisisCovid crash
Nasdaq Composite0.860.980.98
Dow Jones Industrial Average0.960.990.99
Russell 20000.850.940.94
Hang Seng Index0.190.390.48
Nikkei 2250.100.140.37
KOSPI0.110.300.38
Shanghai Composite0.030.130.45
DAX0.520.680.73
FTSE 1000.480.580.77
EURO STOXX 500.520.620.75
BSE SENSEX0.130.500.48

Windows: Global financial crisis 1 Sep 2008 to 31 Mar 2009 · Covid crash 15 Feb 2020 to 30 Apr 2020. Monthly matrix built from 1,184 months of S&P 500 history and each pair's own overlap. Price returns from daily closing levels. Index price returns exclude dividends. Full provenance and a citation line are in Sources and method below.

Why the daily numbers are wrong and the monthly ones are not

The daily correlation between Nikkei 225 and the S&P 500 is 0.10, which reads as two markets that barely notice each other. It is an artefact. Tokyo closes before New York opens, so "the same calendar day" pairs a Japanese session with an American session that had not happened yet. Compare each Japanese session with the *previous* American one and the figure rises to 0.32. Nothing about the markets changed; only the alignment did.

That is why the headline matrix on this page is monthly. Over a calendar month the few hours of offset stop mattering, and what is left is the thing a reader wants: how much of one market's move is shared with another's. Every pair is computed over the months both indices actually existed for, and the observation count is in each cell's tooltip, because a 0.9 built from sixty months and one built from a thousand are not the same claim.

The lowest correlation in the table is Shanghai Composite at 0.12 against the S&P 500 — a genuinely separate market, driven by a domestic investor base and a different policy cycle, and one of the few remaining places where "diversification" is more than a word. The European indices sit at the other end, high enough that a portfolio split between New York and Frankfurt is closer to one position than to two.

Then the crisis columns. Every pair here is more correlated inside the 2008 and 2020 windows than over its own full history, and the increase is largest for the pairs that looked most diversifying in calm periods. This is the practical failure mode of an international allocation: the correlation you sized the position with is the average one, and the correlation you get in the month you need it is the crisis one.

Questions people ask about this

Why is the daily correlation with Asian markets so low?
Because a daily correlation compares two sessions that never overlap. Tokyo closes hours before New York opens, so 'the same day' in a daily table is not the same information set. Shifting the Asian markets one session forward — comparing today in Tokyo with yesterday in New York — roughly triples the figure, which is the version that describes the actual linkage.
Do correlations really rise in a crash?
Yes, and the crisis columns here measure by how much. Every pair on this page is more correlated inside the 2008 and 2020 windows than over the full record. That is the practical problem with international diversification: the correlation you are relying on is the calm-period one, and the one you get in the month you need it is higher.
Why do different pairs have different sample sizes?
Because each pair is computed over the months both indices actually existed for. The S&P 500 series reaches back to 1928 and the Shanghai Composite to 1991, so their overlap is the shorter of the two. The sample count for every pair is shown on the page rather than being hidden behind a single 'full history' label.

Sources and method

Data
  • Financial Modeling PrepDaily adjusted closing levels and quotes, retrieved through Plutux's own data service.
  • The index publishersS&P Dow Jones Indices, Nasdaq, FTSE Russell, Nikkei, Hang Seng Indexes, Deutsche Boerse, Korea Exchange, Shanghai Stock Exchange, BSE and STOXX each publish and maintain their own index.
How it was calculated
Pearson correlation of percentage returns. The headline matrix uses calendar-month returns, which is long enough that the time-zone offset between markets stops mattering; each pair is computed over the months both series cover, and the sample count is shown. The daily matrices use close-to-close returns on shared trading dates, once as they fall on the calendar and once with each market compared against the other's previous session. Crisis windows are 1 Sep 2008 to 31 Mar 2009 and 15 Feb 2020 to 30 Apr 2020. Local currency throughout, so no exchange-rate effect is included; price returns, dividends excluded.
How often it changes
Regenerated from the full daily history about once a year; the date it runs through is at the top of the page.
Citing this page

Free to quote — please link rather than copy the table.

Plutux. "How closely the world's stock markets actually move together." Data through 28 Aug 2026. https://plutux.ai/es/resources/tools/global-market-correlations

Historical figures for information only — not investment advice, and not a forecast.

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Global Stock Market Correlation Matrix: 12 Indices Compared | Plutux