Correlation Between Willis Towers and Brown Brown
Can any of the company-specific risk be diversified away by investing in both Willis Towers and Brown Brown 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 Willis Towers and Brown Brown into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Willis Towers Watson and Brown Brown, you can compare the effects of market volatilities on Willis Towers and Brown Brown 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 Willis Towers with a short position of Brown Brown. Check out your portfolio center. Please also check ongoing floating volatility patterns of Willis Towers and Brown Brown.
Diversification Opportunities for Willis Towers and Brown Brown
0.91 | Correlation Coefficient |
Almost no diversification
The 3 months correlation between Willis and Brown is 0.91. Overlapping area represents the amount of risk that can be diversified away by holding Willis Towers Watson and Brown Brown in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Brown Brown and Willis Towers 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 Willis Towers Watson are associated (or correlated) with Brown Brown. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Brown Brown has no effect on the direction of Willis Towers i.e., Willis Towers and Brown Brown go up and down completely randomly.
Pair Corralation between Willis Towers and Brown Brown
Considering the 90-day investment horizon Willis Towers is expected to generate 1.12 times less return on investment than Brown Brown. But when comparing it to its historical volatility, Willis Towers Watson is 1.01 times less risky than Brown Brown. It trades about 0.3 of its potential returns per unit of risk. Brown Brown is currently generating about 0.33 of returns per unit of risk over similar time horizon. If you would invest 10,321 in Brown Brown on August 27, 2024 and sell it today you would earn a total of 884.00 from holding Brown Brown or generate 8.57% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Strong |
| Accuracy | 100.0% |
| Values | Daily Returns |
Willis Towers Watson vs. Brown Brown
Performance |
| Timeline |
| Willis Towers Watson |
| Brown Brown |
Willis Towers and Brown Brown Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Willis Towers and Brown Brown
The main advantage of trading using opposite Willis Towers and Brown Brown positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Willis Towers position performs unexpectedly, Brown Brown 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 Brown Brown will offset losses from the drop in Brown Brown's long position.| Willis Towers vs. Marsh McLennan Companies | Willis Towers vs. Arthur J Gallagher | Willis Towers vs. Brown Brown | Willis Towers vs. Erie Indemnity |
| Brown Brown vs. Marsh McLennan Companies | Brown Brown vs. Aon PLC | Brown Brown vs. Willis Towers Watson | Brown Brown vs. Erie Indemnity |
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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