Correlation Between Argha Karya and Anugerah Kagum
Can any of the company-specific risk be diversified away by investing in both Argha Karya and Anugerah Kagum 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 Argha Karya and Anugerah Kagum into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Argha Karya Prima and Anugerah Kagum Karya, you can compare the effects of market volatilities on Argha Karya and Anugerah Kagum 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 Argha Karya with a short position of Anugerah Kagum. Check out your portfolio center. Please also check ongoing floating volatility patterns of Argha Karya and Anugerah Kagum.
Diversification Opportunities for Argha Karya and Anugerah Kagum
0.25 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Argha and Anugerah is 0.25. Overlapping area represents the amount of risk that can be diversified away by holding Argha Karya Prima and Anugerah Kagum Karya in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Anugerah Kagum Karya and Argha Karya 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 Argha Karya Prima are associated (or correlated) with Anugerah Kagum. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Anugerah Kagum Karya has no effect on the direction of Argha Karya i.e., Argha Karya and Anugerah Kagum go up and down completely randomly.
Pair Corralation between Argha Karya and Anugerah Kagum
Assuming the 90 days trading horizon Argha Karya Prima is expected to generate 0.56 times more return on investment than Anugerah Kagum. However, Argha Karya Prima is 1.79 times less risky than Anugerah Kagum. It trades about -0.01 of its potential returns per unit of risk. Anugerah Kagum Karya is currently generating about -0.06 per unit of risk. If you would invest 73,000 in Argha Karya Prima on November 3, 2024 and sell it today you would lose (12,500) from holding Argha Karya Prima or give up 17.12% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Argha Karya Prima vs. Anugerah Kagum Karya
Performance |
| Timeline |
| Argha Karya Prima |
| Anugerah Kagum Karya |
Argha Karya and Anugerah Kagum Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Argha Karya and Anugerah Kagum
The main advantage of trading using opposite Argha Karya and Anugerah Kagum positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Argha Karya position performs unexpectedly, Anugerah Kagum 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 Anugerah Kagum will offset losses from the drop in Anugerah Kagum's long position.| Argha Karya vs. Asiaplast Industries Tbk | Argha Karya vs. Alumindo Light Metal | Argha Karya vs. Berlina Tbk | Argha Karya vs. Anugerah Kagum Karya |
| Anugerah Kagum vs. Asiaplast Industries Tbk | Anugerah Kagum vs. Argha Karya Prima | Anugerah Kagum vs. Alumindo Light Metal | Anugerah Kagum vs. Asahimas Flat Glass |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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