Correlation Between Salesforce and Booking Holdings
Can any of the company-specific risk be diversified away by investing in both Salesforce and Booking Holdings 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 Salesforce and Booking Holdings into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Salesforce and Booking Holdings, you can compare the effects of market volatilities on Salesforce and Booking Holdings 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 Salesforce with a short position of Booking Holdings. Check out your portfolio center. Please also check ongoing floating volatility patterns of Salesforce and Booking Holdings.
Diversification Opportunities for Salesforce and Booking Holdings
0.46 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Salesforce and Booking is 0.46. Overlapping area represents the amount of risk that can be diversified away by holding Salesforce and Booking Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Booking Holdings and Salesforce 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 Salesforce are associated (or correlated) with Booking Holdings. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Booking Holdings has no effect on the direction of Salesforce i.e., Salesforce and Booking Holdings go up and down completely randomly.
Pair Corralation between Salesforce and Booking Holdings
Assuming the 90 days trading horizon Salesforce is expected to generate 1.09 times more return on investment than Booking Holdings. However, Salesforce is 1.09 times more volatile than Booking Holdings. It trades about 0.06 of its potential returns per unit of risk. Booking Holdings is currently generating about -0.1 per unit of risk. If you would invest 32,130 in Salesforce on November 6, 2024 and sell it today you would earn a total of 655.00 from holding Salesforce or generate 2.04% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 95.24% |
| Values | Daily Returns |
Salesforce vs. Booking Holdings
Performance |
| Timeline |
| Salesforce |
| Booking Holdings |
Salesforce and Booking Holdings Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Salesforce and Booking Holdings
The main advantage of trading using opposite Salesforce and Booking Holdings positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Salesforce position performs unexpectedly, Booking Holdings 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 Booking Holdings will offset losses from the drop in Booking Holdings' long position.| Salesforce vs. SAP SE | Salesforce vs. Uber Technologies | Salesforce vs. PagerDuty | Salesforce vs. Rocket Internet SE |
| Booking Holdings vs. SENECA FOODS A | Booking Holdings vs. Tower Semiconductor | Booking Holdings vs. Taiwan Semiconductor Manufacturing | Booking Holdings vs. Cal Maine Foods |
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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