Correlation Between Doosan Fuel and Doosan Bobcat
Can any of the company-specific risk be diversified away by investing in both Doosan Fuel and Doosan Bobcat 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 Doosan Fuel and Doosan Bobcat into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Doosan Fuel Cell and Doosan Bobcat, you can compare the effects of market volatilities on Doosan Fuel and Doosan Bobcat 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 Doosan Fuel with a short position of Doosan Bobcat. Check out your portfolio center. Please also check ongoing floating volatility patterns of Doosan Fuel and Doosan Bobcat.
Diversification Opportunities for Doosan Fuel and Doosan Bobcat
0.1 | Correlation Coefficient |
Average diversification
The 3 months correlation between Doosan and Doosan is 0.1. Overlapping area represents the amount of risk that can be diversified away by holding Doosan Fuel Cell and Doosan Bobcat in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Doosan Bobcat and Doosan Fuel 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 Doosan Fuel Cell are associated (or correlated) with Doosan Bobcat. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Doosan Bobcat has no effect on the direction of Doosan Fuel i.e., Doosan Fuel and Doosan Bobcat go up and down completely randomly.
Pair Corralation between Doosan Fuel and Doosan Bobcat
Assuming the 90 days trading horizon Doosan Fuel Cell is expected to under-perform the Doosan Bobcat. In addition to that, Doosan Fuel is 1.04 times more volatile than Doosan Bobcat. It trades about -0.04 of its total potential returns per unit of risk. Doosan Bobcat is currently generating about 0.03 per unit of volatility. If you would invest 3,376,186 in Doosan Bobcat on October 4, 2024 and sell it today you would earn a total of 813,814 from holding Doosan Bobcat or generate 24.1% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Doosan Fuel Cell vs. Doosan Bobcat
Performance |
Timeline |
Doosan Fuel Cell |
Doosan Bobcat |
Doosan Fuel and Doosan Bobcat Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Doosan Fuel and Doosan Bobcat
The main advantage of trading using opposite Doosan Fuel and Doosan Bobcat positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Doosan Fuel position performs unexpectedly, Doosan Bobcat 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 Doosan Bobcat will offset losses from the drop in Doosan Bobcat's long position.Doosan Fuel vs. ABOV Semiconductor Co | Doosan Fuel vs. LG Display Co | Doosan Fuel vs. Korean Drug Co | Doosan Fuel vs. Dongbang Transport Logistics |
Doosan Bobcat vs. Korea Electronic Certification | Doosan Bobcat vs. Grand Korea Leisure | Doosan Bobcat vs. Inzi Display CoLtd | Doosan Bobcat vs. LG Display Co |
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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