Correlation Between Chunghwa Telecom and Nankang Rubber
Can any of the company-specific risk be diversified away by investing in both Chunghwa Telecom and Nankang Rubber 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 Chunghwa Telecom and Nankang Rubber into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Chunghwa Telecom Co and Nankang Rubber Tire, you can compare the effects of market volatilities on Chunghwa Telecom and Nankang Rubber 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 Chunghwa Telecom with a short position of Nankang Rubber. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chunghwa Telecom and Nankang Rubber.
Diversification Opportunities for Chunghwa Telecom and Nankang Rubber
0.23 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Chunghwa and Nankang is 0.23. Overlapping area represents the amount of risk that can be diversified away by holding Chunghwa Telecom Co and Nankang Rubber Tire in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Nankang Rubber Tire and Chunghwa Telecom 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 Chunghwa Telecom Co are associated (or correlated) with Nankang Rubber. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Nankang Rubber Tire has no effect on the direction of Chunghwa Telecom i.e., Chunghwa Telecom and Nankang Rubber go up and down completely randomly.
Pair Corralation between Chunghwa Telecom and Nankang Rubber
Assuming the 90 days trading horizon Chunghwa Telecom is expected to generate 4.05 times less return on investment than Nankang Rubber. But when comparing it to its historical volatility, Chunghwa Telecom Co is 2.69 times less risky than Nankang Rubber. It trades about 0.03 of its potential returns per unit of risk. Nankang Rubber Tire is currently generating about 0.05 of returns per unit of risk over similar time horizon. If you would invest 3,450 in Nankang Rubber Tire on August 28, 2024 and sell it today you would earn a total of 1,420 from holding Nankang Rubber Tire or generate 41.16% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Chunghwa Telecom Co vs. Nankang Rubber Tire
Performance |
Timeline |
Chunghwa Telecom |
Nankang Rubber Tire |
Chunghwa Telecom and Nankang Rubber Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chunghwa Telecom and Nankang Rubber
The main advantage of trading using opposite Chunghwa Telecom and Nankang Rubber positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chunghwa Telecom position performs unexpectedly, Nankang Rubber 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 Nankang Rubber will offset losses from the drop in Nankang Rubber's long position.Chunghwa Telecom vs. CTBC Financial Holding | Chunghwa Telecom vs. Fubon Financial Holding | Chunghwa Telecom vs. President Chain Store |
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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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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