Correlation Between CTP NV and GANGLONG CHINA
Can any of the company-specific risk be diversified away by investing in both CTP NV and GANGLONG CHINA 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 CTP NV and GANGLONG CHINA into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between CTP NV EO and GANGLONG CHINA PRGRLTD, you can compare the effects of market volatilities on CTP NV and GANGLONG CHINA 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 CTP NV with a short position of GANGLONG CHINA. Check out your portfolio center. Please also check ongoing floating volatility patterns of CTP NV and GANGLONG CHINA.
Diversification Opportunities for CTP NV and GANGLONG CHINA
0.31 | Correlation Coefficient |
Weak diversification
The 3 months correlation between CTP and GANGLONG is 0.31. Overlapping area represents the amount of risk that can be diversified away by holding CTP NV EO and GANGLONG CHINA PRGRLTD in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on GANGLONG CHINA PRGRLTD and CTP NV 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 CTP NV EO are associated (or correlated) with GANGLONG CHINA. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of GANGLONG CHINA PRGRLTD has no effect on the direction of CTP NV i.e., CTP NV and GANGLONG CHINA go up and down completely randomly.
Pair Corralation between CTP NV and GANGLONG CHINA
Assuming the 90 days horizon CTP NV EO is expected to under-perform the GANGLONG CHINA. But the stock apears to be less risky and, when comparing its historical volatility, CTP NV EO is 77.07 times less risky than GANGLONG CHINA. The stock trades about -0.03 of its potential returns per unit of risk. The GANGLONG CHINA PRGRLTD is currently generating about 0.09 of returns per unit of risk over similar time horizon. If you would invest 1.00 in GANGLONG CHINA PRGRLTD on September 26, 2024 and sell it today you would lose (0.10) from holding GANGLONG CHINA PRGRLTD or give up 10.0% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
CTP NV EO vs. GANGLONG CHINA PRGRLTD
Performance |
| Timeline |
| CTP NV EO |
| GANGLONG CHINA PRGRLTD |
CTP NV and GANGLONG CHINA Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with CTP NV and GANGLONG CHINA
The main advantage of trading using opposite CTP NV and GANGLONG CHINA positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if CTP NV position performs unexpectedly, GANGLONG CHINA 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 GANGLONG CHINA will offset losses from the drop in GANGLONG CHINA's long position.| CTP NV vs. China Resources Land | CTP NV vs. DEUTSCHE WOHNEN ADRS12 | CTP NV vs. SEAZEN GROUP LTD | CTP NV vs. Atrium Ljungberg AB |
| GANGLONG CHINA vs. China Resources Land | GANGLONG CHINA vs. DEUTSCHE WOHNEN ADRS12 | GANGLONG CHINA vs. CTP NV EO | GANGLONG CHINA vs. SEAZEN GROUP LTD |
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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