Correlation Between Vestas Wind and Franklin Electric
Can any of the company-specific risk be diversified away by investing in both Vestas Wind and Franklin Electric 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 Vestas Wind and Franklin Electric into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Vestas Wind Systems and Franklin Electric Co, you can compare the effects of market volatilities on Vestas Wind and Franklin Electric 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 Vestas Wind with a short position of Franklin Electric. Check out your portfolio center. Please also check ongoing floating volatility patterns of Vestas Wind and Franklin Electric.
Diversification Opportunities for Vestas Wind and Franklin Electric
-0.64 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Vestas and Franklin is -0.64. Overlapping area represents the amount of risk that can be diversified away by holding Vestas Wind Systems and Franklin Electric Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Franklin Electric and Vestas Wind 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 Vestas Wind Systems are associated (or correlated) with Franklin Electric. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Franklin Electric has no effect on the direction of Vestas Wind i.e., Vestas Wind and Franklin Electric go up and down completely randomly.
Pair Corralation between Vestas Wind and Franklin Electric
Assuming the 90 days horizon Vestas Wind Systems is expected to under-perform the Franklin Electric. In addition to that, Vestas Wind is 1.75 times more volatile than Franklin Electric Co. It trades about -0.2 of its total potential returns per unit of risk. Franklin Electric Co is currently generating about 0.08 per unit of volatility. If you would invest 9,948 in Franklin Electric Co on August 31, 2024 and sell it today you would earn a total of 882.00 from holding Franklin Electric Co or generate 8.87% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 98.44% |
Values | Daily Returns |
Vestas Wind Systems vs. Franklin Electric Co
Performance |
Timeline |
Vestas Wind Systems |
Franklin Electric |
Vestas Wind and Franklin Electric Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Vestas Wind and Franklin Electric
The main advantage of trading using opposite Vestas Wind and Franklin Electric positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Vestas Wind position performs unexpectedly, Franklin Electric 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 Franklin Electric will offset losses from the drop in Franklin Electric's long position.Vestas Wind vs. Kone Oyj ADR | Vestas Wind vs. Schneider Electric SE | Vestas Wind vs. Schneider Electric SA | Vestas Wind vs. Fanuc |
Franklin Electric vs. Graco Inc | Franklin Electric vs. Ametek Inc | Franklin Electric vs. Flowserve | Franklin Electric vs. Donaldson |
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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