Correlation Between GM and Agro Yasa
Can any of the company-specific risk be diversified away by investing in both GM and Agro Yasa 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 GM and Agro Yasa into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between General Motors and Agro Yasa Lestari, you can compare the effects of market volatilities on GM and Agro Yasa 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 GM with a short position of Agro Yasa. Check out your portfolio center. Please also check ongoing floating volatility patterns of GM and Agro Yasa.
Diversification Opportunities for GM and Agro Yasa
Modest diversification
The 3 months correlation between GM and Agro is 0.29. Overlapping area represents the amount of risk that can be diversified away by holding General Motors and Agro Yasa Lestari in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Agro Yasa Lestari and GM 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 General Motors are associated (or correlated) with Agro Yasa. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Agro Yasa Lestari has no effect on the direction of GM i.e., GM and Agro Yasa go up and down completely randomly.
Pair Corralation between GM and Agro Yasa
Allowing for the 90-day total investment horizon General Motors is expected to generate 0.48 times more return on investment than Agro Yasa. However, General Motors is 2.1 times less risky than Agro Yasa. It trades about 0.07 of its potential returns per unit of risk. Agro Yasa Lestari is currently generating about 0.01 per unit of risk. If you would invest 3,574 in General Motors on September 1, 2024 and sell it today you would earn a total of 1,985 from holding General Motors or generate 55.54% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 95.97% |
| Values | Daily Returns |
General Motors vs. Agro Yasa Lestari
Performance |
| Timeline |
| General Motors |
| Agro Yasa Lestari |
GM and Agro Yasa Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with GM and Agro Yasa
The main advantage of trading using opposite GM and Agro Yasa positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if GM position performs unexpectedly, Agro Yasa 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 Agro Yasa will offset losses from the drop in Agro Yasa's long position.The idea behind General Motors and Agro Yasa Lestari pairs trading is to make the combined position market-neutral, meaning the overall market's direction will not affect its win or loss (or potential downside or upside). This can be achieved by designing a pairs trade with two highly correlated stocks or equities that operate in a similar space or sector, making it possible to obtain profits through simple and relatively low-risk investment.| Agro Yasa vs. Sinergi Inti Plastindo | Agro Yasa vs. Bank Amar Indonesia | Agro Yasa vs. Andalan Sakti Primaindo | Agro Yasa vs. Era Mandiri Cemerlang |
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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