Correlation Between BIT Computer and DB Insurance
Can any of the company-specific risk be diversified away by investing in both BIT Computer and DB Insurance 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 BIT Computer and DB Insurance into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between BIT Computer Co and DB Insurance Co, you can compare the effects of market volatilities on BIT Computer and DB Insurance 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 BIT Computer with a short position of DB Insurance. Check out your portfolio center. Please also check ongoing floating volatility patterns of BIT Computer and DB Insurance.
Diversification Opportunities for BIT Computer and DB Insurance
0.71 | Correlation Coefficient |
Poor diversification
The 3 months correlation between BIT and 005830 is 0.71. Overlapping area represents the amount of risk that can be diversified away by holding BIT Computer Co and DB Insurance Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on DB Insurance and BIT Computer 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 BIT Computer Co are associated (or correlated) with DB Insurance. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of DB Insurance has no effect on the direction of BIT Computer i.e., BIT Computer and DB Insurance go up and down completely randomly.
Pair Corralation between BIT Computer and DB Insurance
Assuming the 90 days trading horizon BIT Computer is expected to generate 4.43 times less return on investment than DB Insurance. In addition to that, BIT Computer is 1.23 times more volatile than DB Insurance Co. It trades about 0.01 of its total potential returns per unit of risk. DB Insurance Co is currently generating about 0.05 per unit of volatility. If you would invest 8,432,970 in DB Insurance Co on September 4, 2024 and sell it today you would earn a total of 2,497,030 from holding DB Insurance Co or generate 29.61% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
BIT Computer Co vs. DB Insurance Co
Performance |
| Timeline |
| BIT Computer |
| DB Insurance |
BIT Computer and DB Insurance Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with BIT Computer and DB Insurance
The main advantage of trading using opposite BIT Computer and DB Insurance positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if BIT Computer position performs unexpectedly, DB Insurance 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 DB Insurance will offset losses from the drop in DB Insurance's long position.| BIT Computer vs. AptaBio Therapeutics | BIT Computer vs. KT Hitel | BIT Computer vs. SillaJen | BIT Computer vs. Cytogen |
| DB Insurance vs. Echomarketing CoLtd | DB Insurance vs. HB Technology TD | DB Insurance vs. Seoul Semiconductor Co | DB Insurance vs. Hwangkum Steel Technology |
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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