Correlation Between Palomar Holdings and Bausch Lomb
Can any of the company-specific risk be diversified away by investing in both Palomar Holdings and Bausch Lomb 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 Palomar Holdings and Bausch Lomb into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Palomar Holdings and Bausch Lomb Corp, you can compare the effects of market volatilities on Palomar Holdings and Bausch Lomb 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 Palomar Holdings with a short position of Bausch Lomb. Check out your portfolio center. Please also check ongoing floating volatility patterns of Palomar Holdings and Bausch Lomb.
Diversification Opportunities for Palomar Holdings and Bausch Lomb
0.12 | Correlation Coefficient |
Average diversification
The 3 months correlation between Palomar and Bausch is 0.12. Overlapping area represents the amount of risk that can be diversified away by holding Palomar Holdings and Bausch Lomb Corp in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Bausch Lomb Corp and Palomar Holdings 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 Palomar Holdings are associated (or correlated) with Bausch Lomb. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Bausch Lomb Corp has no effect on the direction of Palomar Holdings i.e., Palomar Holdings and Bausch Lomb go up and down completely randomly.
Pair Corralation between Palomar Holdings and Bausch Lomb
Given the investment horizon of 90 days Palomar Holdings is expected to generate 0.91 times more return on investment than Bausch Lomb. However, Palomar Holdings is 1.1 times less risky than Bausch Lomb. It trades about 0.11 of its potential returns per unit of risk. Bausch Lomb Corp is currently generating about 0.09 per unit of risk. If you would invest 8,306 in Palomar Holdings on September 3, 2024 and sell it today you would earn a total of 2,597 from holding Palomar Holdings or generate 31.27% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Palomar Holdings vs. Bausch Lomb Corp
Performance |
| Timeline |
| Palomar Holdings |
| Bausch Lomb Corp |
Palomar Holdings and Bausch Lomb Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Palomar Holdings and Bausch Lomb
The main advantage of trading using opposite Palomar Holdings and Bausch Lomb positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Palomar Holdings position performs unexpectedly, Bausch Lomb 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 Bausch Lomb will offset losses from the drop in Bausch Lomb's long position.| Palomar Holdings vs. Horace Mann Educators | Palomar Holdings vs. Kemper | Palomar Holdings vs. RLI Corp | Palomar Holdings vs. Global Indemnity PLC |
| Bausch Lomb vs. The Cooper Companies, | Bausch Lomb vs. ICU Medical | Bausch Lomb vs. Hologic | Bausch Lomb vs. Becton Dickinson and |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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