Correlation Between Telkom Indonesia and NYSE Composite
Can any of the company-specific risk be diversified away by investing in both Telkom Indonesia and NYSE Composite 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 Telkom Indonesia and NYSE Composite into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Telkom Indonesia Tbk and NYSE Composite, you can compare the effects of market volatilities on Telkom Indonesia and NYSE Composite 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 Telkom Indonesia with a short position of NYSE Composite. Check out your portfolio center. Please also check ongoing floating volatility patterns of Telkom Indonesia and NYSE Composite.
Diversification Opportunities for Telkom Indonesia and NYSE Composite
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Telkom and NYSE is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Telkom Indonesia Tbk and NYSE Composite in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on NYSE Composite and Telkom Indonesia 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 Telkom Indonesia Tbk are associated (or correlated) with NYSE Composite. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of NYSE Composite has no effect on the direction of Telkom Indonesia i.e., Telkom Indonesia and NYSE Composite go up and down completely randomly.
Pair Corralation between Telkom Indonesia and NYSE Composite
Assuming the 90 days trading horizon Telkom Indonesia Tbk is expected to under-perform the NYSE Composite. In addition to that, Telkom Indonesia is 53.66 times more volatile than NYSE Composite. It trades about -0.05 of its total potential returns per unit of risk. NYSE Composite is currently generating about 0.08 per unit of volatility. If you would invest 1,385,572 in NYSE Composite on June 23, 2024 and sell it today you would earn a total of 551,802 from holding NYSE Composite or generate 39.82% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 97.83% |
Values | Daily Returns |
Telkom Indonesia Tbk vs. NYSE Composite
Performance |
Timeline |
Telkom Indonesia and NYSE Composite Volatility Contrast
Predicted Return Density |
Returns |
Telkom Indonesia Tbk
Pair trading matchups for Telkom Indonesia
NYSE Composite
Pair trading matchups for NYSE Composite
Pair Trading with Telkom Indonesia and NYSE Composite
The main advantage of trading using opposite Telkom Indonesia and NYSE Composite positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Telkom Indonesia position performs unexpectedly, NYSE Composite 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 NYSE Composite will offset losses from the drop in NYSE Composite's long position.Telkom Indonesia vs. Deutsche Telekom AG | Telkom Indonesia vs. Deutsche Telekom AG | Telkom Indonesia vs. Superior Plus Corp | Telkom Indonesia vs. NMI Holdings |
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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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