MSCI Country Indices

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Interpretation

The charts above include all country indices (in USD) from Developed Markets, Emerging Markets, and Frontier Markets.
The MSCI country indices follow the MSCI Index Calculation Methodology, a standardized approach for calculating MSCI Equity Indexes. This methodology ensures a consistent and accurate representation of market performance across various countries, incorporating factors like market capitalization weighting, adjustments for corporate actions, and other market dynamics. The indices aim to cover approximately 85% of the free float-adjusted market capitalization in each country, focusing on large and mid-cap segments.

Further Information

Longermtrends: Developed Markets
Longermtrends: Emerging Markets
Longermtrends: Emerging vs. Developed Markets
Longermtrends: Frontier Markets

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Correlation Heat Map

Interpretation

This heatmap shows how the stock markets of different countries move in relation to one another. Red squares indicate a positive correlation (markets moving together), while blue shows a negative correlation. You can often see strong clusters based on geographic regions (like Europe or Asia) and economic ties, as national economies are often influenced by their neighbors.

For global investors, this chart is crucial for international diversification. Investing in countries with low or negative correlations can help protect a portfolio from regional downturns. This strategy is a practical application of Ray Dalio's "Holy Grail of Investing"—using uncorrelated assets to reduce risk.

To create this chart, weekly returns are calculated for each country's MSCI index, and the Pearson correlation is computed for every pair. The heatmap is then organized using hierarchical clustering to group countries with the most similar market performance, making global economic patterns easier to spot.

Correlation Spanning Tree

Interpretation

The Minimum Spanning Tree (MST) simplifies the correlation matrix by showing only the strongest connections between indices. If two indices are linked, they have a strong positive correlation and tend to move in tandem. This helps identify clusters of related assets and is useful for portfolio diversification.
The tree is constructed by converting the correlations into distances and then finding the set of connections that links all indices with the minimum total distance. As noted by Marti, Gautier, et al. (2017), the optimal Markowitz portfolio is often found at the tree's outskirts, and the tree tends to shrink during a financial crisis.

Data Sources

Northern Europe

Western Europe

Central Europe

Eastern Europe

Southern Europe

Balkans

Middle East

Gulf States

North Africa

Other African Markets

South and Central Asia

Southeast Asia

East Asia

Australasia

North America

Latin America

Caribbean

*The country indices marked with an asterisk are classified by MSCI as "Standalone Indices" and are not part of the MSCI Frontier Markets Index. They represent individual markets with significant barriers to foreign investment, such as stringent capital controls and regulatory constraints.

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An error appeared while loading the data. Maybe there is a technical problem with the data source. Please let me know if this happens regularly @silvan_frank.

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Interpretation

Further Information

Weekly Macro ReportMember Exclusive

Correlation Heat Map

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Interpretation

Correlation Spanning Tree

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Interpretation

Data Sources

Weekly Macro Report
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