Correlation Between Loomis Sayles and Dow Jones

Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Dow Jones at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining Loomis Sayles and Dow Jones into the same portfolio, which is an essential part of the fundamental portfolio management process.

By analyzing existing cross correlation between Loomis Sayles High and Dow Jones Industrial, you can compare the effects of market volatilities on Loomis Sayles and Dow Jones and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in Loomis Sayles with a short position of Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Dow Jones.

Diversification Opportunities for Loomis Sayles and Dow Jones

0.9

Correlation Coefficient

Loomis Sayles  Dow Jones

Almost no diversification

The 3 months correlation between Loomis and Dow is 0.9. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles High and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones Industrial and Loomis Sayles is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on Loomis Sayles High are associated (or correlated) with Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Dow Jones go up and down completely randomly.

Pair Corralation between Loomis Sayles and Dow Jones

Assuming the 90 days horizon Loomis Sayles High is expected to under-perform the Dow Jones. But the mutual fund apears to be less risky and, when comparing its historical volatility, Loomis Sayles High is 3.75 times less risky than Dow Jones. The mutual fund trades about -0.03 of its potential returns per unit of risk. The Dow Jones Industrial is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest  4,231,300  in Dow Jones Industrial on September 27, 2024 and sell it today you would earn a total of  98,403  from holding Dow Jones Industrial or generate 2.33% return on investment over 90 days.

Time Period	3 Months [change]
Direction	Moves Together
Strength	Very Strong
Accuracy	100.0%
Values	Daily Returns

Loomis Sayles High  vs.  Dow Jones Industrial

Pair Trading with Loomis Sayles and Dow Jones

The main advantage of trading using opposite Loomis Sayles and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Dow Jones can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Dow Jones will offset losses from the drop in Dow Jones' long position.

The idea behind Loomis Sayles High and Dow Jones Industrial pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.

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Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.