Correlation Between Dow Jones and China Coal
Can any of the company-specific risk be diversified away by investing in both Dow Jones and China Coal 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 Dow Jones and China Coal into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Dow Jones Industrial and China Coal Energy, you can compare the effects of market volatilities on Dow Jones and China Coal 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 Dow Jones with a short position of China Coal. Check out your portfolio center. Please also check ongoing floating volatility patterns of Dow Jones and China Coal.
Diversification Opportunities for Dow Jones and China Coal
-0.03 | Correlation Coefficient |
Good diversification
The 3 months correlation between Dow and China is -0.03. Overlapping area represents the amount of risk that can be diversified away by holding Dow Jones Industrial and China Coal Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on China Coal Energy and Dow Jones 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 Dow Jones Industrial are associated (or correlated) with China Coal. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of China Coal Energy has no effect on the direction of Dow Jones i.e., Dow Jones and China Coal go up and down completely randomly.
Pair Corralation between Dow Jones and China Coal
Assuming the 90 days trading horizon Dow Jones Industrial is expected to generate 0.37 times more return on investment than China Coal. However, Dow Jones Industrial is 2.68 times less risky than China Coal. It trades about -0.04 of its potential returns per unit of risk. China Coal Energy is currently generating about -0.12 per unit of risk. If you would invest 4,257,373 in Dow Jones Industrial on December 29, 2024 and sell it today you would lose (98,983) from holding Dow Jones Industrial or give up 2.32% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
Dow Jones Industrial vs. China Coal Energy
Performance |
Timeline |
Dow Jones and China Coal Volatility Contrast
Predicted Return Density |
Returns |
Dow Jones Industrial
Pair trading matchups for Dow Jones
China Coal Energy
Pair trading matchups for China Coal
Pair Trading with Dow Jones and China Coal
The main advantage of trading using opposite Dow Jones and China Coal positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Dow Jones position performs unexpectedly, China Coal 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 China Coal will offset losses from the drop in China Coal's long position.Dow Jones vs. Perseus Mining Limited | Dow Jones vs. Falcon Metals Limited | Dow Jones vs. Broadstone Net Lease | Dow Jones vs. PennantPark Investment |
China Coal vs. Catalyst Pharmaceuticals | China Coal vs. China Southern Airlines | China Coal vs. Regeneron Pharmaceuticals | China Coal vs. Frontier Group Holdings |
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 Headlines Timeline module to stay connected to all market stories and filter out noise. Drill down to analyze hype elasticity.
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