Correlation Between SPDR Dow and IShares China
Can any of the company-specific risk be diversified away by investing in both SPDR Dow and IShares 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 SPDR Dow and IShares China into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SPDR Dow Jones and iShares China Large, you can compare the effects of market volatilities on SPDR Dow and IShares 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 SPDR Dow with a short position of IShares China. Check out your portfolio center. Please also check ongoing floating volatility patterns of SPDR Dow and IShares China.
Diversification Opportunities for SPDR Dow and IShares China
0.0 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between SPDR and IShares is 0.0. Overlapping area represents the amount of risk that can be diversified away by holding SPDR Dow Jones and iShares China Large in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on iShares China Large and SPDR Dow 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 SPDR Dow Jones are associated (or correlated) with IShares China. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of iShares China Large has no effect on the direction of SPDR Dow i.e., SPDR Dow and IShares China go up and down completely randomly.
Pair Corralation between SPDR Dow and IShares China
Assuming the 90 days trading horizon SPDR Dow is expected to generate 2.08 times less return on investment than IShares China. But when comparing it to its historical volatility, SPDR Dow Jones is 3.1 times less risky than IShares China. It trades about 0.17 of its potential returns per unit of risk. iShares China Large is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest 7,015 in iShares China Large on September 23, 2024 and sell it today you would earn a total of 1,325 from holding iShares China Large or generate 18.89% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Flat |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
SPDR Dow Jones vs. iShares China Large
Performance |
Timeline |
SPDR Dow Jones |
iShares China Large |
SPDR Dow and IShares China Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SPDR Dow and IShares China
The main advantage of trading using opposite SPDR Dow and IShares China positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SPDR Dow position performs unexpectedly, IShares 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 IShares China will offset losses from the drop in IShares China's long position.SPDR Dow vs. SPDR MSCI World | SPDR Dow vs. SPDR SP Dividend | SPDR Dow vs. SPDR SP 500 | SPDR Dow vs. SPDR BB SB |
IShares China vs. SPDR Dow Jones | IShares China vs. iShares Core MSCI | IShares China vs. Vanguard FTSE All World | IShares China vs. iShares China CNY |
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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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