Correlation Between Argha Karya and Champion Pacific
Can any of the company-specific risk be diversified away by investing in both Argha Karya and Champion Pacific 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 Champion Pacific 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 Champion Pacific Indonesia, you can compare the effects of market volatilities on Argha Karya and Champion Pacific 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 Champion Pacific. Check out your portfolio center. Please also check ongoing floating volatility patterns of Argha Karya and Champion Pacific.
Diversification Opportunities for Argha Karya and Champion Pacific
-0.45 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Argha and Champion is -0.45. Overlapping area represents the amount of risk that can be diversified away by holding Argha Karya Prima and Champion Pacific Indonesia in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Champion Pacific Ind 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 Champion Pacific. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Champion Pacific Ind has no effect on the direction of Argha Karya i.e., Argha Karya and Champion Pacific go up and down completely randomly.
Pair Corralation between Argha Karya and Champion Pacific
Assuming the 90 days trading horizon Argha Karya Prima is expected to under-perform the Champion Pacific. But the stock apears to be less risky and, when comparing its historical volatility, Argha Karya Prima is 1.01 times less risky than Champion Pacific. The stock trades about -0.16 of its potential returns per unit of risk. The Champion Pacific Indonesia is currently generating about 0.1 of returns per unit of risk over similar time horizon. If you would invest 49,800 in Champion Pacific Indonesia on September 12, 2024 and sell it today you would earn a total of 2,200 from holding Champion Pacific Indonesia or generate 4.42% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Argha Karya Prima vs. Champion Pacific Indonesia
Performance |
Timeline |
Argha Karya Prima |
Champion Pacific Ind |
Argha Karya and Champion Pacific Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Argha Karya and Champion Pacific
The main advantage of trading using opposite Argha Karya and Champion Pacific positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Argha Karya position performs unexpectedly, Champion Pacific 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 Champion Pacific will offset losses from the drop in Champion Pacific'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 |
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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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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