Correlation Between Bausch Lomb and Postal Realty
Can any of the company-specific risk be diversified away by investing in both Bausch Lomb and Postal Realty 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 Bausch Lomb and Postal Realty into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Bausch Lomb Corp and Postal Realty Trust, you can compare the effects of market volatilities on Bausch Lomb and Postal Realty 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 Bausch Lomb with a short position of Postal Realty. Check out your portfolio center. Please also check ongoing floating volatility patterns of Bausch Lomb and Postal Realty.
Diversification Opportunities for Bausch Lomb and Postal Realty
-0.76 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Bausch and Postal is -0.76. Overlapping area represents the amount of risk that can be diversified away by holding Bausch Lomb Corp and Postal Realty Trust in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Postal Realty Trust and Bausch Lomb 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 Bausch Lomb Corp are associated (or correlated) with Postal Realty. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Postal Realty Trust has no effect on the direction of Bausch Lomb i.e., Bausch Lomb and Postal Realty go up and down completely randomly.
Pair Corralation between Bausch Lomb and Postal Realty
Given the investment horizon of 90 days Bausch Lomb Corp is expected to under-perform the Postal Realty. But the stock apears to be less risky and, when comparing its historical volatility, Bausch Lomb Corp is 1.16 times less risky than Postal Realty. The stock trades about -0.15 of its potential returns per unit of risk. The Postal Realty Trust is currently generating about 0.09 of returns per unit of risk over similar time horizon. If you would invest 1,271 in Postal Realty Trust on December 28, 2024 and sell it today you would earn a total of 136.00 from holding Postal Realty Trust or generate 10.7% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Bausch Lomb Corp vs. Postal Realty Trust
Performance |
Timeline |
Bausch Lomb Corp |
Postal Realty Trust |
Bausch Lomb and Postal Realty Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Bausch Lomb and Postal Realty
The main advantage of trading using opposite Bausch Lomb and Postal Realty positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Bausch Lomb position performs unexpectedly, Postal Realty 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 Postal Realty will offset losses from the drop in Postal Realty's long position.Bausch Lomb vs. The Cooper Companies, | Bausch Lomb vs. ICU Medical | Bausch Lomb vs. Hologic | Bausch Lomb vs. Becton Dickinson and |
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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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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