Correlation Between Chongqing Machinery and BANK HANDLOWY
Can any of the company-specific risk be diversified away by investing in both Chongqing Machinery and BANK HANDLOWY 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 Chongqing Machinery and BANK HANDLOWY into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Chongqing Machinery Electric and BANK HANDLOWY, you can compare the effects of market volatilities on Chongqing Machinery and BANK HANDLOWY 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 Chongqing Machinery with a short position of BANK HANDLOWY. Check out your portfolio center. Please also check ongoing floating volatility patterns of Chongqing Machinery and BANK HANDLOWY.
Diversification Opportunities for Chongqing Machinery and BANK HANDLOWY
-0.29 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Chongqing and BANK is -0.29. Overlapping area represents the amount of risk that can be diversified away by holding Chongqing Machinery Electric and BANK HANDLOWY in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BANK HANDLOWY and Chongqing Machinery 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 Chongqing Machinery Electric are associated (or correlated) with BANK HANDLOWY. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BANK HANDLOWY has no effect on the direction of Chongqing Machinery i.e., Chongqing Machinery and BANK HANDLOWY go up and down completely randomly.
Pair Corralation between Chongqing Machinery and BANK HANDLOWY
Assuming the 90 days horizon Chongqing Machinery Electric is expected to generate 4.04 times more return on investment than BANK HANDLOWY. However, Chongqing Machinery is 4.04 times more volatile than BANK HANDLOWY. It trades about 0.15 of its potential returns per unit of risk. BANK HANDLOWY is currently generating about -0.05 per unit of risk. If you would invest 6.00 in Chongqing Machinery Electric on September 22, 2024 and sell it today you would earn a total of 1.90 from holding Chongqing Machinery Electric or generate 31.67% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Chongqing Machinery Electric vs. BANK HANDLOWY
Performance |
Timeline |
Chongqing Machinery |
BANK HANDLOWY |
Chongqing Machinery and BANK HANDLOWY Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Chongqing Machinery and BANK HANDLOWY
The main advantage of trading using opposite Chongqing Machinery and BANK HANDLOWY positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Chongqing Machinery position performs unexpectedly, BANK HANDLOWY 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 BANK HANDLOWY will offset losses from the drop in BANK HANDLOWY's long position.Chongqing Machinery vs. Rayonier Advanced Materials | Chongqing Machinery vs. NEWELL RUBBERMAID | Chongqing Machinery vs. GOODYEAR T RUBBER | Chongqing Machinery vs. COSTCO WHOLESALE CDR |
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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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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