Correlation Between Loomis Sayles and Diamond Hill
Can any of the company-specific risk be diversified away by investing in both Loomis Sayles and Diamond Hill 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 Diamond Hill into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Loomis Sayles Growth and Diamond Hill Large, you can compare the effects of market volatilities on Loomis Sayles and Diamond Hill 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 Diamond Hill. Check out your portfolio center. Please also check ongoing floating volatility patterns of Loomis Sayles and Diamond Hill.
Diversification Opportunities for Loomis Sayles and Diamond Hill
0.89 | Correlation Coefficient |
Very poor diversification
The 3 months correlation between Loomis and Diamond is 0.89. Overlapping area represents the amount of risk that can be diversified away by holding Loomis Sayles Growth and Diamond Hill Large in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Diamond Hill Large 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 Growth are associated (or correlated) with Diamond Hill. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Diamond Hill Large has no effect on the direction of Loomis Sayles i.e., Loomis Sayles and Diamond Hill go up and down completely randomly.
Pair Corralation between Loomis Sayles and Diamond Hill
Assuming the 90 days horizon Loomis Sayles Growth is expected to generate 1.5 times more return on investment than Diamond Hill. However, Loomis Sayles is 1.5 times more volatile than Diamond Hill Large. It trades about 0.25 of its potential returns per unit of risk. Diamond Hill Large is currently generating about 0.12 per unit of risk. If you would invest 2,690 in Loomis Sayles Growth on September 5, 2024 and sell it today you would earn a total of 450.00 from holding Loomis Sayles Growth or generate 16.73% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Strong |
Accuracy | 98.44% |
Values | Daily Returns |
Loomis Sayles Growth vs. Diamond Hill Large
Performance |
Timeline |
Loomis Sayles Growth |
Diamond Hill Large |
Loomis Sayles and Diamond Hill Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Loomis Sayles and Diamond Hill
The main advantage of trading using opposite Loomis Sayles and Diamond Hill positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Loomis Sayles position performs unexpectedly, Diamond Hill 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 Diamond Hill will offset losses from the drop in Diamond Hill's long position.Loomis Sayles vs. Loomis Sayles Growth | Loomis Sayles vs. Loomis Sayles Growth | Loomis Sayles vs. Diamond Hill Large | Loomis Sayles vs. Diamond Hill Large |
Diamond Hill vs. Diamond Hill Large | Diamond Hill vs. Diamond Hill Large | Diamond Hill vs. Loomis Sayles Growth | Diamond Hill vs. Loomis Sayles Growth |
Check out your portfolio center.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.
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