Correlation Between Marsh McLennan and Willis Towers
Can any of the company-specific risk be diversified away by investing in both Marsh McLennan and Willis Towers 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 Marsh McLennan and Willis Towers into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Marsh McLennan Companies and Willis Towers Watson, you can compare the effects of market volatilities on Marsh McLennan and Willis Towers 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 Marsh McLennan with a short position of Willis Towers. Check out your portfolio center. Please also check ongoing floating volatility patterns of Marsh McLennan and Willis Towers.
Diversification Opportunities for Marsh McLennan and Willis Towers
0.7 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Marsh and Willis is 0.7. Overlapping area represents the amount of risk that can be diversified away by holding Marsh McLennan Companies and Willis Towers Watson in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Willis Towers Watson and Marsh McLennan 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 Marsh McLennan Companies are associated (or correlated) with Willis Towers. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Willis Towers Watson has no effect on the direction of Marsh McLennan i.e., Marsh McLennan and Willis Towers go up and down completely randomly.
Pair Corralation between Marsh McLennan and Willis Towers
Assuming the 90 days horizon Marsh McLennan is expected to generate 1.83 times less return on investment than Willis Towers. But when comparing it to its historical volatility, Marsh McLennan Companies is 1.23 times less risky than Willis Towers. It trades about 0.08 of its potential returns per unit of risk. Willis Towers Watson is currently generating about 0.11 of returns per unit of risk over similar time horizon. If you would invest 21,374 in Willis Towers Watson on September 27, 2024 and sell it today you would earn a total of 9,026 from holding Willis Towers Watson or generate 42.23% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
Marsh McLennan Companies vs. Willis Towers Watson
Performance |
Timeline |
Marsh McLennan Companies |
Willis Towers Watson |
Marsh McLennan and Willis Towers Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Marsh McLennan and Willis Towers
The main advantage of trading using opposite Marsh McLennan and Willis Towers positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Marsh McLennan position performs unexpectedly, Willis Towers 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 Willis Towers will offset losses from the drop in Willis Towers' long position.Marsh McLennan vs. Aon PLC | Marsh McLennan vs. Arthur J Gallagher | Marsh McLennan vs. Willis Towers Watson | Marsh McLennan vs. Steadfast Group Limited |
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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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