Correlation Between Shinkong Insurance and Nan Yang
Can any of the company-specific risk be diversified away by investing in both Shinkong Insurance and Nan Yang 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 Nan Yang 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 Nan Yang Dyeing, you can compare the effects of market volatilities on Shinkong Insurance and Nan Yang 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 Nan Yang. Check out your portfolio center. Please also check ongoing floating volatility patterns of Shinkong Insurance and Nan Yang.
Diversification Opportunities for Shinkong Insurance and Nan Yang
-0.67 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Shinkong and Nan is -0.67. Overlapping area represents the amount of risk that can be diversified away by holding Shinkong Insurance Co and Nan Yang Dyeing in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Nan Yang Dyeing 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 Nan Yang. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Nan Yang Dyeing has no effect on the direction of Shinkong Insurance i.e., Shinkong Insurance and Nan Yang go up and down completely randomly.
Pair Corralation between Shinkong Insurance and Nan Yang
Assuming the 90 days trading horizon Shinkong Insurance Co is expected to generate 2.17 times more return on investment than Nan Yang. However, Shinkong Insurance is 2.17 times more volatile than Nan Yang Dyeing. It trades about 0.11 of its potential returns per unit of risk. Nan Yang Dyeing is currently generating about -0.02 per unit of risk. If you would invest 6,850 in Shinkong Insurance Co on October 3, 2024 and sell it today you would earn a total of 3,800 from holding Shinkong Insurance Co or generate 55.47% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 99.62% |
Values | Daily Returns |
Shinkong Insurance Co vs. Nan Yang Dyeing
Performance |
Timeline |
Shinkong Insurance |
Nan Yang Dyeing |
Shinkong Insurance and Nan Yang Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Shinkong Insurance and Nan Yang
The main advantage of trading using opposite Shinkong Insurance and Nan Yang positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Shinkong Insurance position performs unexpectedly, Nan Yang 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 Nan Yang will offset losses from the drop in Nan Yang's long position.Shinkong Insurance vs. EnTie Commercial Bank | Shinkong Insurance vs. Union Bank of | Shinkong Insurance vs. Bank of Kaohsiung | Shinkong Insurance vs. Ruentex Development Co |
Nan Yang vs. Ruentex Development Co | Nan Yang vs. Symtek Automation Asia | Nan Yang vs. WiseChip Semiconductor | Nan Yang vs. Novatek Microelectronics Corp |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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