Correlation Between Yancoal Australia and PT Bayan
Can any of the company-specific risk be diversified away by investing in both Yancoal Australia and PT Bayan 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 Yancoal Australia and PT Bayan into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Yancoal Australia and PT Bayan Resources, you can compare the effects of market volatilities on Yancoal Australia and PT Bayan 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 Yancoal Australia with a short position of PT Bayan. Check out your portfolio center. Please also check ongoing floating volatility patterns of Yancoal Australia and PT Bayan.
Diversification Opportunities for Yancoal Australia and PT Bayan
0.38 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Yancoal and BNB is 0.38. Overlapping area represents the amount of risk that can be diversified away by holding Yancoal Australia and PT Bayan Resources in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PT Bayan Resources and Yancoal Australia 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 Yancoal Australia are associated (or correlated) with PT Bayan. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PT Bayan Resources has no effect on the direction of Yancoal Australia i.e., Yancoal Australia and PT Bayan go up and down completely randomly.
Pair Corralation between Yancoal Australia and PT Bayan
Assuming the 90 days horizon Yancoal Australia is expected to generate 1.52 times more return on investment than PT Bayan. However, Yancoal Australia is 1.52 times more volatile than PT Bayan Resources. It trades about 0.06 of its potential returns per unit of risk. PT Bayan Resources is currently generating about 0.01 per unit of risk. If you would invest 265.00 in Yancoal Australia on September 12, 2024 and sell it today you would earn a total of 107.00 from holding Yancoal Australia or generate 40.38% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 99.6% |
| Values | Daily Returns |
Yancoal Australia vs. PT Bayan Resources
Performance |
| Timeline |
| Yancoal Australia |
| PT Bayan Resources |
Yancoal Australia and PT Bayan Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Yancoal Australia and PT Bayan
The main advantage of trading using opposite Yancoal Australia and PT Bayan positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Yancoal Australia position performs unexpectedly, PT Bayan 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 PT Bayan will offset losses from the drop in PT Bayan's long position.| Yancoal Australia vs. DATAGROUP SE | Yancoal Australia vs. Jacquet Metal Service | Yancoal Australia vs. Automatic Data Processing | Yancoal Australia vs. United States Steel |
| PT Bayan vs. Verizon Communications | PT Bayan vs. American Airlines Group | PT Bayan vs. SINGAPORE AIRLINES | PT Bayan vs. Gol Intelligent Airlines |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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