Correlation Between DB Insurance and Wonbang Tech
Can any of the company-specific risk be diversified away by investing in both DB Insurance and Wonbang Tech 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 Insurance and Wonbang Tech into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between DB Insurance Co and Wonbang Tech Co, you can compare the effects of market volatilities on DB Insurance and Wonbang Tech 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 Insurance with a short position of Wonbang Tech. Check out your portfolio center. Please also check ongoing floating volatility patterns of DB Insurance and Wonbang Tech.
Diversification Opportunities for DB Insurance and Wonbang Tech
0.75 | Correlation Coefficient |
Poor diversification
The 3 months correlation between 005830 and Wonbang is 0.75. Overlapping area represents the amount of risk that can be diversified away by holding DB Insurance Co and Wonbang Tech Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Wonbang Tech and DB Insurance 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 Insurance Co are associated (or correlated) with Wonbang Tech. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Wonbang Tech has no effect on the direction of DB Insurance i.e., DB Insurance and Wonbang Tech go up and down completely randomly.
Pair Corralation between DB Insurance and Wonbang Tech
Assuming the 90 days trading horizon DB Insurance Co is expected to generate 1.0 times more return on investment than Wonbang Tech. However, DB Insurance Co is 1.0 times less risky than Wonbang Tech. It trades about -0.01 of its potential returns per unit of risk. Wonbang Tech Co is currently generating about -0.21 per unit of risk. If you would invest 11,130,000 in DB Insurance Co on September 3, 2024 and sell it today you would lose (210,000) from holding DB Insurance Co or give up 1.89% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
DB Insurance Co vs. Wonbang Tech Co
Performance |
| Timeline |
| DB Insurance |
| Wonbang Tech |
DB Insurance and Wonbang Tech Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with DB Insurance and Wonbang Tech
The main advantage of trading using opposite DB Insurance and Wonbang Tech positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if DB Insurance position performs unexpectedly, Wonbang Tech 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 Wonbang Tech will offset losses from the drop in Wonbang Tech's long position.| DB Insurance vs. Playgram Co | DB Insurance vs. National Plastic Co | DB Insurance vs. Grand Korea Leisure | DB Insurance vs. EV Advanced Material |
| Wonbang Tech vs. Dongnam Chemical Co | Wonbang Tech vs. CKH Food Health | Wonbang Tech vs. Hyundai Green Food | Wonbang Tech vs. Youngbo Chemical 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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