Correlation Between Kabelindo Murni and Chandra Asri
Can any of the company-specific risk be diversified away by investing in both Kabelindo Murni and Chandra Asri 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 Kabelindo Murni and Chandra Asri into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Kabelindo Murni Tbk and Chandra Asri Petrochemical, you can compare the effects of market volatilities on Kabelindo Murni and Chandra Asri 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 Kabelindo Murni with a short position of Chandra Asri. Check out your portfolio center. Please also check ongoing floating volatility patterns of Kabelindo Murni and Chandra Asri.
Diversification Opportunities for Kabelindo Murni and Chandra Asri
-0.06 | Correlation Coefficient |
Good diversification
The 3 months correlation between Kabelindo and Chandra is -0.06. Overlapping area represents the amount of risk that can be diversified away by holding Kabelindo Murni Tbk and Chandra Asri Petrochemical in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Chandra Asri Petroch and Kabelindo Murni 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 Kabelindo Murni Tbk are associated (or correlated) with Chandra Asri. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Chandra Asri Petroch has no effect on the direction of Kabelindo Murni i.e., Kabelindo Murni and Chandra Asri go up and down completely randomly.
Pair Corralation between Kabelindo Murni and Chandra Asri
Assuming the 90 days trading horizon Kabelindo Murni Tbk is expected to under-perform the Chandra Asri. But the stock apears to be less risky and, when comparing its historical volatility, Kabelindo Murni Tbk is 2.19 times less risky than Chandra Asri. The stock trades about -0.02 of its potential returns per unit of risk. The Chandra Asri Petrochemical is currently generating about 0.02 of returns per unit of risk over similar time horizon. If you would invest 757,500 in Chandra Asri Petrochemical on December 4, 2024 and sell it today you would lose (7,500) from holding Chandra Asri Petrochemical or give up 0.99% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Kabelindo Murni Tbk vs. Chandra Asri Petrochemical
Performance |
Timeline |
Kabelindo Murni Tbk |
Chandra Asri Petroch |
Kabelindo Murni and Chandra Asri Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Kabelindo Murni and Chandra Asri
The main advantage of trading using opposite Kabelindo Murni and Chandra Asri positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Kabelindo Murni position performs unexpectedly, Chandra Asri 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 Chandra Asri will offset losses from the drop in Chandra Asri's long position.Kabelindo Murni vs. Kmi Wire And | Kabelindo Murni vs. Jembo Cable | Kabelindo Murni vs. Sumi Indo Kabel | Kabelindo Murni vs. Voksel Electric Tbk |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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