Correlation Between Hua Nan and Central Reinsurance
Can any of the company-specific risk be diversified away by investing in both Hua Nan and Central Reinsurance 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 Hua Nan and Central Reinsurance into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hua Nan Financial and Central Reinsurance Corp, you can compare the effects of market volatilities on Hua Nan and Central Reinsurance 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 Hua Nan with a short position of Central Reinsurance. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hua Nan and Central Reinsurance.
Diversification Opportunities for Hua Nan and Central Reinsurance
0.51 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Hua and Central is 0.51. Overlapping area represents the amount of risk that can be diversified away by holding Hua Nan Financial and Central Reinsurance Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Central Reinsurance Corp and Hua Nan 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 Hua Nan Financial are associated (or correlated) with Central Reinsurance. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Central Reinsurance Corp has no effect on the direction of Hua Nan i.e., Hua Nan and Central Reinsurance go up and down completely randomly.
Pair Corralation between Hua Nan and Central Reinsurance
Assuming the 90 days trading horizon Hua Nan is expected to generate 1.21 times less return on investment than Central Reinsurance. But when comparing it to its historical volatility, Hua Nan Financial is 1.03 times less risky than Central Reinsurance. It trades about 0.13 of its potential returns per unit of risk. Central Reinsurance Corp is currently generating about 0.15 of returns per unit of risk over similar time horizon. If you would invest 2,585 in Central Reinsurance Corp on December 29, 2024 and sell it today you would earn a total of 185.00 from holding Central Reinsurance Corp or generate 7.16% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Hua Nan Financial vs. Central Reinsurance Corp
Performance |
Timeline |
Hua Nan Financial |
Central Reinsurance Corp |
Hua Nan and Central Reinsurance Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hua Nan and Central Reinsurance
The main advantage of trading using opposite Hua Nan and Central Reinsurance positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hua Nan position performs unexpectedly, Central Reinsurance 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 Central Reinsurance will offset losses from the drop in Central Reinsurance's long position.Hua Nan vs. First Financial Holding | Hua Nan vs. Mega Financial Holding | Hua Nan vs. CTBC Financial Holding | Hua Nan vs. ESUN Financial Holding |
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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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