Correlation Between McDonalds and Kandi Technologies
Can any of the company-specific risk be diversified away by investing in both McDonalds and Kandi Technologies 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 McDonalds and Kandi Technologies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between McDonalds and Kandi Technologies Group, you can compare the effects of market volatilities on McDonalds and Kandi Technologies 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 McDonalds with a short position of Kandi Technologies. Check out your portfolio center. Please also check ongoing floating volatility patterns of McDonalds and Kandi Technologies.
Diversification Opportunities for McDonalds and Kandi Technologies
0.62 | Correlation Coefficient |
Poor diversification
The 3 months correlation between McDonalds and Kandi is 0.62. Overlapping area represents the amount of risk that can be diversified away by holding McDonalds and Kandi Technologies Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Kandi Technologies and McDonalds 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 McDonalds are associated (or correlated) with Kandi Technologies. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Kandi Technologies has no effect on the direction of McDonalds i.e., McDonalds and Kandi Technologies go up and down completely randomly.
Pair Corralation between McDonalds and Kandi Technologies
Considering the 90-day investment horizon McDonalds is expected to under-perform the Kandi Technologies. But the stock apears to be less risky and, when comparing its historical volatility, McDonalds is 4.21 times less risky than Kandi Technologies. The stock trades about -0.13 of its potential returns per unit of risk. The Kandi Technologies Group is currently generating about 0.28 of returns per unit of risk over similar time horizon. If you would invest 218.00 in Kandi Technologies Group on January 31, 2024 and sell it today you would earn a total of 46.00 from holding Kandi Technologies Group or generate 21.1% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
McDonalds vs. Kandi Technologies Group
Performance |
Timeline |
McDonalds |
Kandi Technologies |
McDonalds and Kandi Technologies Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with McDonalds and Kandi Technologies
The main advantage of trading using opposite McDonalds and Kandi Technologies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if McDonalds position performs unexpectedly, Kandi Technologies 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 Kandi Technologies will offset losses from the drop in Kandi Technologies' long position.McDonalds vs. Chipotle Mexican Grill | McDonalds vs. Dutch Bros | McDonalds vs. Dominos Pizza | McDonalds vs. Yum Brands |
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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 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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