US Stock Market Sectors

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Loading Data

Please wait, we are loading chart data

Interpretation

The charts above display the relative strength of each US stock market sector, a key concept in investing and technical analysis. This strength is assessed by comparing the sector's price performance against the S&P 500, a widely recognized benchmark index. A rising ratio in these charts signifies that a sector is outperforming the market, whereas a declining ratio indicates underperformance. There are 11 major sectors identified by the Global Industry Classification Standard (GICS), and each major public company falls into one of these categories. Understanding relative strength is particularly beneficial for executing sector rotation strategies. Such strategies involve identifying sectors with robust relative strength and strategically investing in them. By doing so, investors can take advantage of the positive momentum in these sectors, potentially enhancing their investment portfolio's performance.

Further Information


US Stock Market Sectors Overview

Loading Data

Please wait, we are loading chart data

Interpretation

The chart above gives a different view of the same data from the ratios above. Presented below are brief sector descriptions along with some example companies.


Correlation Heat Map for US Stock Market Sectors

All
1Y

Loading Data

Please wait, we are loading chart data

Interpretation

The chart above shows a heat map depicting the correlation coefficients among various US stock market sectors. Each sector is represented on both the x and y axes, with intersections revealing the strength and direction of their correlation. A correlation coefficient of +1 indicates a perfect positive correlation, implying that two sectors moved in the same direction during the specified time window. Conversely, a coefficient of -1 signifies that the sectors moved in opposite directions. Colors range from deep blue, indicating a negative correlation, to dark red, signifying a strong positive correlation. The chart shows that over a long time frame the Energy sector exhibits the weakest correlation with the other sectors.
The correlation coefficient is important to consider for diversification because it helps investors assess the potential benefits of including different assets in their investment portfolios. Diversification is the practice of spreading investments across different assets to reduce risk. In his book Principles, Ray Dalio called diversification the “Holy Grail of Investing”. He realized that with fifteen to twenty uncorrelated return streams, he could dramatically reduce the risks without reducing the expected returns.


Correlation Spanning Tree for US Stock Market Sectors

All
1Y

Loading Data

Please wait, we are loading chart data

Interpretation

The minimum spanning tree (MST) simplifies the data from the correlation matrix above by retaining only the strongest correlations between the sectors. If two sectors are connected, it means that they are positively correlated and that they tend to move in tandem. By analyzing the structure of the MST, one can identify clusters of assets that move together. This visual tool is especially beneficial when considering portfolio diversification. In fact, Marti, Gautier, et al. (2017) found that the optimal Markowitz portfolio is found at the outskirts of the tree and that the tree shrinks during a stock crisis.

Data Sources


View More Charts


Error

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.