Correlation Between Beijing SPC and Sinocat Environmental
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By analyzing existing cross correlation between Beijing SPC Environment and Sinocat Environmental Technology, you can compare the effects of market volatilities on Beijing SPC and Sinocat Environmental 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 Beijing SPC with a short position of Sinocat Environmental. Check out your portfolio center. Please also check ongoing floating volatility patterns of Beijing SPC and Sinocat Environmental.
Diversification Opportunities for Beijing SPC and Sinocat Environmental
0.93 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Beijing and Sinocat is 0.93. Overlapping area represents the amount of risk that can be diversified away by holding Beijing SPC Environment and Sinocat Environmental Technolo in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Sinocat Environmental and Beijing SPC 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 Beijing SPC Environment are associated (or correlated) with Sinocat Environmental. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Sinocat Environmental has no effect on the direction of Beijing SPC i.e., Beijing SPC and Sinocat Environmental go up and down completely randomly.
Pair Corralation between Beijing SPC and Sinocat Environmental
Assuming the 90 days trading horizon Beijing SPC Environment is expected to under-perform the Sinocat Environmental. But the stock apears to be less risky and, when comparing its historical volatility, Beijing SPC Environment is 1.95 times less risky than Sinocat Environmental. The stock trades about -0.08 of its potential returns per unit of risk. The Sinocat Environmental Technology is currently generating about 0.08 of returns per unit of risk over similar time horizon. If you would invest 1,644 in Sinocat Environmental Technology on August 29, 2024 and sell it today you would earn a total of 85.00 from holding Sinocat Environmental Technology or generate 5.17% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Beijing SPC Environment vs. Sinocat Environmental Technolo
Performance |
| Timeline |
| Beijing SPC Environment |
| Sinocat Environmental |
Beijing SPC and Sinocat Environmental Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Beijing SPC and Sinocat Environmental
The main advantage of trading using opposite Beijing SPC and Sinocat Environmental positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Beijing SPC position performs unexpectedly, Sinocat Environmental 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 Sinocat Environmental will offset losses from the drop in Sinocat Environmental's long position.| Beijing SPC vs. Central Plains Environment | Beijing SPC vs. Guangdong Liantai Environmental | Beijing SPC vs. GRINM Semiconductor Materials | Beijing SPC vs. Southchip Semiconductor Technology |
| Sinocat Environmental vs. Loongson Technology Corp | Sinocat Environmental vs. Chongqing Road Bridge | Sinocat Environmental vs. Shenzhen Fortune Trend | Sinocat Environmental vs. Wuhan Xianglong Power |
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 Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.
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