Correlation Between JE Cleantech and Xinjiang Goldwind
Can any of the company-specific risk be diversified away by investing in both JE Cleantech and Xinjiang Goldwind 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 JE Cleantech and Xinjiang Goldwind into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between JE Cleantech Holdings and Xinjiang Goldwind Science, you can compare the effects of market volatilities on JE Cleantech and Xinjiang Goldwind 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 JE Cleantech with a short position of Xinjiang Goldwind. Check out your portfolio center. Please also check ongoing floating volatility patterns of JE Cleantech and Xinjiang Goldwind.
Diversification Opportunities for JE Cleantech and Xinjiang Goldwind
0.36 | Correlation Coefficient |
Weak diversification
The 3 months correlation between JCSE and Xinjiang is 0.36. Overlapping area represents the amount of risk that can be diversified away by holding JE Cleantech Holdings and Xinjiang Goldwind Science in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Xinjiang Goldwind Science and JE Cleantech 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 JE Cleantech Holdings are associated (or correlated) with Xinjiang Goldwind. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Xinjiang Goldwind Science has no effect on the direction of JE Cleantech i.e., JE Cleantech and Xinjiang Goldwind go up and down completely randomly.
Pair Corralation between JE Cleantech and Xinjiang Goldwind
Given the investment horizon of 90 days JE Cleantech Holdings is expected to generate 1.88 times more return on investment than Xinjiang Goldwind. However, JE Cleantech is 1.88 times more volatile than Xinjiang Goldwind Science. It trades about 0.06 of its potential returns per unit of risk. Xinjiang Goldwind Science is currently generating about 0.1 per unit of risk. If you would invest 128.00 in JE Cleantech Holdings on September 26, 2024 and sell it today you would earn a total of 13.00 from holding JE Cleantech Holdings or generate 10.16% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
JE Cleantech Holdings vs. Xinjiang Goldwind Science
Performance |
Timeline |
JE Cleantech Holdings |
Xinjiang Goldwind Science |
JE Cleantech and Xinjiang Goldwind Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with JE Cleantech and Xinjiang Goldwind
The main advantage of trading using opposite JE Cleantech and Xinjiang Goldwind positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if JE Cleantech position performs unexpectedly, Xinjiang Goldwind 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 Xinjiang Goldwind will offset losses from the drop in Xinjiang Goldwind's long position.JE Cleantech vs. Nuburu Inc | JE Cleantech vs. Laser Photonics | JE Cleantech vs. Reelcause | JE Cleantech vs. Quality Industrial Corp |
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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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