







The charts above show the relative strength of each factor index compared to the broader MSCI World Index. A rising line indicates the factor is outperforming the market, while a falling line indicates underperformance.

The chart above gives a different view of the same data from the ratios above. The list below provides descriptions for each MSCI World Factor Index, highlighting distinct investment strategies and market segments in global equity markets.

This chart compares the absolute performance of each factor index using MSCI's Gross Total Return (GRTR) data. Unlike a price index, a total return index assumes that all cash distributions, such as dividends, are reinvested. According to the MSCI methodology, the 'gross' index estimates the maximum potential reinvestment without any deductions for taxes, providing a standardized measure of an index's underlying performance.

This heatmap shows how different investment factors, such as Momentum, Value, and Quality, have performed in relation to one another. Red squares indicate a strong positive correlation (factors moving together), while blue squares show a negative correlation (factors moving apart). This is useful for seeing which strategies are complementary; for example, Momentum and Value often exhibit a negative correlation, making them effective diversifiers for each other.
Understanding these relationships helps in building a robust, multi-factor portfolio. By combining factors that have low or negative correlations, investors can smooth out returns and reduce dependency on any single investment style. This aligns with Ray Dalio's "Holy Grail of Investing"—using uncorrelated return streams to lower risk without sacrificing returns.
To create this chart, weekly returns are calculated for each factor index, and the Pearson correlation is computed for every pair. The heatmap is then organized using hierarchical clustering to group the most similar factors together, making strategic relationships easier to spot.

The Minimum Spanning Tree (MST) simplifies the correlation matrix by showing only the strongest connections between factors. If two factors 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 factors 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.
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