Correlation Between Monster Beverage and Fast Retailing
Can any of the company-specific risk be diversified away by investing in both Monster Beverage and Fast Retailing 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 Monster Beverage and Fast Retailing into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Monster Beverage Corp and Fast Retailing Co, you can compare the effects of market volatilities on Monster Beverage and Fast Retailing 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 Monster Beverage with a short position of Fast Retailing. Check out your portfolio center. Please also check ongoing floating volatility patterns of Monster Beverage and Fast Retailing.
Diversification Opportunities for Monster Beverage and Fast Retailing
0.43 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Monster and Fast is 0.43. Overlapping area represents the amount of risk that can be diversified away by holding Monster Beverage Corp and Fast Retailing Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Fast Retailing and Monster Beverage 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 Monster Beverage Corp are associated (or correlated) with Fast Retailing. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Fast Retailing has no effect on the direction of Monster Beverage i.e., Monster Beverage and Fast Retailing go up and down completely randomly.
Pair Corralation between Monster Beverage and Fast Retailing
Assuming the 90 days trading horizon Monster Beverage is expected to generate 2.91 times less return on investment than Fast Retailing. But when comparing it to its historical volatility, Monster Beverage Corp is 1.12 times less risky than Fast Retailing. It trades about 0.01 of its potential returns per unit of risk. Fast Retailing Co is currently generating about 0.04 of returns per unit of risk over similar time horizon. If you would invest 21,786 in Fast Retailing Co on April 7, 2025 and sell it today you would earn a total of 5,724 from holding Fast Retailing Co or generate 26.27% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Monster Beverage Corp vs. Fast Retailing Co
Performance |
Timeline |
Monster Beverage Corp |
Fast Retailing |
Monster Beverage and Fast Retailing Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Monster Beverage and Fast Retailing
The main advantage of trading using opposite Monster Beverage and Fast Retailing positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Monster Beverage position performs unexpectedly, Fast Retailing 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 Fast Retailing will offset losses from the drop in Fast Retailing's long position.Monster Beverage vs. Direct Line Insurance | Monster Beverage vs. MCEWEN MINING INC | Monster Beverage vs. INDUSTRIAL MINERALS LTD | Monster Beverage vs. Regions Financial |
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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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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