Correlation Between Xinjiang Goldwind and TECO 2030
Can any of the company-specific risk be diversified away by investing in both Xinjiang Goldwind and TECO 2030 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 Xinjiang Goldwind and TECO 2030 into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Xinjiang Goldwind Science and TECO 2030 ASA, you can compare the effects of market volatilities on Xinjiang Goldwind and TECO 2030 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 Xinjiang Goldwind with a short position of TECO 2030. Check out your portfolio center. Please also check ongoing floating volatility patterns of Xinjiang Goldwind and TECO 2030.
Diversification Opportunities for Xinjiang Goldwind and TECO 2030
-0.19 | Correlation Coefficient |
Good diversification
The 3 months correlation between Xinjiang and TECO is -0.19. Overlapping area represents the amount of risk that can be diversified away by holding Xinjiang Goldwind Science and TECO 2030 ASA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on TECO 2030 ASA and Xinjiang Goldwind 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 Xinjiang Goldwind Science are associated (or correlated) with TECO 2030. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of TECO 2030 ASA has no effect on the direction of Xinjiang Goldwind i.e., Xinjiang Goldwind and TECO 2030 go up and down completely randomly.
Pair Corralation between Xinjiang Goldwind and TECO 2030
Assuming the 90 days horizon Xinjiang Goldwind Science is expected to generate 0.58 times more return on investment than TECO 2030. However, Xinjiang Goldwind Science is 1.72 times less risky than TECO 2030. It trades about 0.16 of its potential returns per unit of risk. TECO 2030 ASA is currently generating about -0.05 per unit of risk. If you would invest 56.00 in Xinjiang Goldwind Science on September 5, 2024 and sell it today you would earn a total of 41.00 from holding Xinjiang Goldwind Science or generate 73.21% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 98.46% |
Values | Daily Returns |
Xinjiang Goldwind Science vs. TECO 2030 ASA
Performance |
Timeline |
Xinjiang Goldwind Science |
TECO 2030 ASA |
Xinjiang Goldwind and TECO 2030 Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Xinjiang Goldwind and TECO 2030
The main advantage of trading using opposite Xinjiang Goldwind and TECO 2030 positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Xinjiang Goldwind position performs unexpectedly, TECO 2030 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 TECO 2030 will offset losses from the drop in TECO 2030's long position.Xinjiang Goldwind vs. Dear Cashmere Holding | Xinjiang Goldwind vs. Goff Corp | Xinjiang Goldwind vs. Wialan Technologies | Xinjiang Goldwind vs. Cgrowth Capital |
TECO 2030 vs. Dear Cashmere Holding | TECO 2030 vs. Goff Corp | TECO 2030 vs. Wialan Technologies | TECO 2030 vs. Cgrowth Capital |
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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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