Correlation Between Nusa Konstruksi and Sentul City
Can any of the company-specific risk be diversified away by investing in both Nusa Konstruksi and Sentul City 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 Nusa Konstruksi and Sentul City into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Nusa Konstruksi Enjiniring and Sentul City Tbk, you can compare the effects of market volatilities on Nusa Konstruksi and Sentul City 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 Nusa Konstruksi with a short position of Sentul City. Check out your portfolio center. Please also check ongoing floating volatility patterns of Nusa Konstruksi and Sentul City.
Diversification Opportunities for Nusa Konstruksi and Sentul City
-0.68 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Nusa and Sentul is -0.68. Overlapping area represents the amount of risk that can be diversified away by holding Nusa Konstruksi Enjiniring and Sentul City Tbk in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sentul City Tbk and Nusa Konstruksi 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 Nusa Konstruksi Enjiniring are associated (or correlated) with Sentul City. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sentul City Tbk has no effect on the direction of Nusa Konstruksi i.e., Nusa Konstruksi and Sentul City go up and down completely randomly.
Pair Corralation between Nusa Konstruksi and Sentul City
Assuming the 90 days trading horizon Nusa Konstruksi Enjiniring is expected to under-perform the Sentul City. But the stock apears to be less risky and, when comparing its historical volatility, Nusa Konstruksi Enjiniring is 6.66 times less risky than Sentul City. The stock trades about -0.28 of its potential returns per unit of risk. The Sentul City Tbk is currently generating about 0.54 of returns per unit of risk over similar time horizon. If you would invest 4,100 in Sentul City Tbk on September 15, 2024 and sell it today you would earn a total of 3,300 from holding Sentul City Tbk or generate 80.49% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Nusa Konstruksi Enjiniring vs. Sentul City Tbk
Performance |
Timeline |
Nusa Konstruksi Enji |
Sentul City Tbk |
Nusa Konstruksi and Sentul City Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Nusa Konstruksi and Sentul City
The main advantage of trading using opposite Nusa Konstruksi and Sentul City positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Nusa Konstruksi position performs unexpectedly, Sentul City 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 Sentul City will offset losses from the drop in Sentul City's long position.Nusa Konstruksi vs. Sentul City Tbk | Nusa Konstruksi vs. Gozco Plantations Tbk | Nusa Konstruksi vs. Bukit Darmo Property | Nusa Konstruksi vs. Total Bangun Persada |
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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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