![]() Of course, the big question is when exactly that is going to happen. However, historically XLE has been more correlated with the broader market and a reversion to the mean is almost certain when the outlook for the sector stabilizes. With commodities slumping across the board and oil price taking another 30%+ hit, the energy sector walked a path of its own, particularly in the second half of the year: It is well know that the utilities sector encompasses features of both equity and fixed income securities, thus its correlation with other stocks is usually lower.Īnother sector that was fairly independent in 2015 was the Energy (NYSEARCA: XLE). Nonetheless, the independence of the utilities stocks from the reset of the market should not come as a big surprise. XLU had a correlation coefficient no greater than 0.50 with 7 out of 8 peers and only demonstrated a slightly closer link with Consumer Staples (NYSEARCA: XLP ). Consumer Discretionary (NYSEARCA: XLY) could also be included in the same category as its co-movement is only immaterially less significant.Īnother thing that stands out from the numbers above is how little correlated to other sectors the Utilities (NYSEARCA: XLU ) are. XLI, XLF and XLK all have correlations of 0.93-0.94 with SPY, showcasing their close link to S&P 500 as a whole. Although there seems to be no apparent similarities between these sectors, the relatively close correlation between them can be explained by the broad market moves. As time spent running increases, body fat decreases. In other words, the variable running time and the variable body fat have a negative correlation. The highest reading of 0.87 was recorded in two cases: Industrials (NYSEARCA: XLI) & Financials (NYSEARCA: XLF) and Industrials & Technology (NYSEARCA: XLK). The more time an individual spends running, the lower their body fat tends to be. One interesting observation from the output matrix above is that no pair of individual sectors had a correlation coefficient of 0.90. ![]() To make it easier interpret the output, here are the sectors each of the tickers corresponds to: Utilizing daily data from 2015, the results table looks as follows: SPDR S&P 500 ETF (NYSEARCA: SPY) has been also included in the list as a proxy the broad U.S. Each member of the dataset gets plotted as a point whose x-y coordinates relates to its values for the two variables. To obtain the estimates of correlation coefficients, I have used the online portfolio analysis tool InvestSpy. A scatterplot is a type of data display that shows the relationship between two numerical variables. ![]() It is always a useful exercise to examine how the market is interlinked and hopefully this study will give readers some ideas how to diversify their portfolios more efficiently. This time I would like to complement the original piece with a correlation analysis of the same 9 Select Sector SPDR ETFs. In a separate article last week I briefly reviewed the performance of individual sectors in 2015.
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