Correlation Between DB Financial and Doosan Fuel
Can any of the company-specific risk be diversified away by investing in both DB Financial and Doosan Fuel 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 DB Financial and Doosan Fuel into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DB Financial Investment and Doosan Fuel Cell, you can compare the effects of market volatilities on DB Financial and Doosan Fuel 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 DB Financial with a short position of Doosan Fuel. Check out your portfolio center. Please also check ongoing floating volatility patterns of DB Financial and Doosan Fuel.
Diversification Opportunities for DB Financial and Doosan Fuel
-0.21 | Correlation Coefficient |
Very good diversification
The 3 months correlation between 016610 and Doosan is -0.21. Overlapping area represents the amount of risk that can be diversified away by holding DB Financial Investment and Doosan Fuel Cell in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Doosan Fuel Cell and DB Financial 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 DB Financial Investment are associated (or correlated) with Doosan Fuel. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Doosan Fuel Cell has no effect on the direction of DB Financial i.e., DB Financial and Doosan Fuel go up and down completely randomly.
Pair Corralation between DB Financial and Doosan Fuel
Assuming the 90 days trading horizon DB Financial Investment is expected to generate 0.7 times more return on investment than Doosan Fuel. However, DB Financial Investment is 1.43 times less risky than Doosan Fuel. It trades about 0.14 of its potential returns per unit of risk. Doosan Fuel Cell is currently generating about -0.03 per unit of risk. If you would invest 523,000 in DB Financial Investment on December 1, 2024 and sell it today you would earn a total of 65,000 from holding DB Financial Investment or generate 12.43% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.31% |
Values | Daily Returns |
DB Financial Investment vs. Doosan Fuel Cell
Performance |
Timeline |
DB Financial Investment |
Doosan Fuel Cell |
DB Financial and Doosan Fuel Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with DB Financial and Doosan Fuel
The main advantage of trading using opposite DB Financial and Doosan Fuel positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DB Financial position performs unexpectedly, Doosan Fuel 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 Fuel will offset losses from the drop in Doosan Fuel's long position.DB Financial vs. Duksan Hi Metal | DB Financial vs. Kyeryong Construction Industrial | DB Financial vs. Songwon Industrial Co | DB Financial vs. Finebesteel |
Doosan Fuel vs. Eagon Industrial Co | Doosan Fuel vs. Chorokbaem Healthcare Co | Doosan Fuel vs. Songwon Industrial Co | Doosan Fuel vs. Hyunwoo Industrial 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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