Correlation Between Shinkong Insurance and Cipherlab
Can any of the company-specific risk be diversified away by investing in both Shinkong Insurance and Cipherlab 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 Shinkong Insurance and Cipherlab into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Shinkong Insurance Co and Cipherlab Co, you can compare the effects of market volatilities on Shinkong Insurance and Cipherlab 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 Shinkong Insurance with a short position of Cipherlab. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shinkong Insurance and Cipherlab.
Diversification Opportunities for Shinkong Insurance and Cipherlab
-0.54 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Shinkong and Cipherlab is -0.54. Overlapping area represents the amount of risk that can be diversified away by holding Shinkong Insurance Co and Cipherlab Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Cipherlab and Shinkong 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 Shinkong Insurance Co are associated (or correlated) with Cipherlab. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Cipherlab has no effect on the direction of Shinkong Insurance i.e., Shinkong Insurance and Cipherlab go up and down completely randomly.
Pair Corralation between Shinkong Insurance and Cipherlab
Assuming the 90 days trading horizon Shinkong Insurance Co is expected to generate 0.48 times more return on investment than Cipherlab. However, Shinkong Insurance Co is 2.1 times less risky than Cipherlab. It trades about 0.12 of its potential returns per unit of risk. Cipherlab Co is currently generating about 0.04 per unit of risk. If you would invest 4,960 in Shinkong Insurance Co on September 26, 2024 and sell it today you would earn a total of 5,590 from holding Shinkong Insurance Co or generate 112.7% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Shinkong Insurance Co vs. Cipherlab Co
Performance |
| Timeline |
| Shinkong Insurance |
| Cipherlab |
Shinkong Insurance and Cipherlab Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Shinkong Insurance and Cipherlab
The main advantage of trading using opposite Shinkong Insurance and Cipherlab positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shinkong Insurance position performs unexpectedly, Cipherlab 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 Cipherlab will offset losses from the drop in Cipherlab's long position.| Shinkong Insurance vs. Taiwan Semiconductor Manufacturing | Shinkong Insurance vs. Hon Hai Precision | Shinkong Insurance vs. MediaTek | Shinkong Insurance vs. Chunghwa Telecom Co |
| Cipherlab vs. GeoVision | Cipherlab vs. Flytech Technology Co | Cipherlab vs. Quanta Storage | Cipherlab vs. Coretronic |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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