Correlation Between JinkoSolar Holding and Bukit Jalil
Can any of the company-specific risk be diversified away by investing in both JinkoSolar Holding and Bukit Jalil 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 JinkoSolar Holding and Bukit Jalil into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between JinkoSolar Holding and Bukit Jalil Global, you can compare the effects of market volatilities on JinkoSolar Holding and Bukit Jalil 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 JinkoSolar Holding with a short position of Bukit Jalil. Check out your portfolio center. Please also check ongoing floating volatility patterns of JinkoSolar Holding and Bukit Jalil.
Diversification Opportunities for JinkoSolar Holding and Bukit Jalil
0.15 | Correlation Coefficient |
Average diversification
The 3 months correlation between JinkoSolar and Bukit is 0.15. Overlapping area represents the amount of risk that can be diversified away by holding JinkoSolar Holding and Bukit Jalil Global in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bukit Jalil Global and JinkoSolar Holding 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 JinkoSolar Holding are associated (or correlated) with Bukit Jalil. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bukit Jalil Global has no effect on the direction of JinkoSolar Holding i.e., JinkoSolar Holding and Bukit Jalil go up and down completely randomly.
Pair Corralation between JinkoSolar Holding and Bukit Jalil
Considering the 90-day investment horizon JinkoSolar Holding is expected to under-perform the Bukit Jalil. But the stock apears to be less risky and, when comparing its historical volatility, JinkoSolar Holding is 9.19 times less risky than Bukit Jalil. The stock trades about 0.0 of its potential returns per unit of risk. The Bukit Jalil Global is currently generating about 0.12 of returns per unit of risk over similar time horizon. If you would invest 5.53 in Bukit Jalil Global on October 5, 2024 and sell it today you would lose (1.93) from holding Bukit Jalil Global or give up 34.9% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 29.23% |
Values | Daily Returns |
JinkoSolar Holding vs. Bukit Jalil Global
Performance |
Timeline |
JinkoSolar Holding |
Bukit Jalil Global |
JinkoSolar Holding and Bukit Jalil Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with JinkoSolar Holding and Bukit Jalil
The main advantage of trading using opposite JinkoSolar Holding and Bukit Jalil positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if JinkoSolar Holding position performs unexpectedly, Bukit Jalil 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 Bukit Jalil will offset losses from the drop in Bukit Jalil's long position.JinkoSolar Holding vs. First Solar | JinkoSolar Holding vs. SolarEdge Technologies | JinkoSolar Holding vs. Sunrun Inc | JinkoSolar Holding vs. Sunnova Energy International |
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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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