Correlation Between Palo Alto and Katapult Holdings
Can any of the company-specific risk be diversified away by investing in both Palo Alto and Katapult 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 Palo Alto and Katapult Holdings into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Palo Alto Networks and Katapult Holdings, you can compare the effects of market volatilities on Palo Alto and Katapult 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 Palo Alto with a short position of Katapult Holdings. Check out your portfolio center. Please also check ongoing floating volatility patterns of Palo Alto and Katapult Holdings.
Diversification Opportunities for Palo Alto and Katapult Holdings
-0.76 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Palo and Katapult is -0.76. Overlapping area represents the amount of risk that can be diversified away by holding Palo Alto Networks and Katapult Holdings in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Katapult Holdings and Palo Alto 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 Palo Alto Networks are associated (or correlated) with Katapult Holdings. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Katapult Holdings has no effect on the direction of Palo Alto i.e., Palo Alto and Katapult Holdings go up and down completely randomly.
Pair Corralation between Palo Alto and Katapult Holdings
Given the investment horizon of 90 days Palo Alto Networks is expected to generate 0.32 times more return on investment than Katapult Holdings. However, Palo Alto Networks is 3.1 times less risky than Katapult Holdings. It trades about 0.18 of its potential returns per unit of risk. Katapult Holdings is currently generating about -0.18 per unit of risk. If you would invest 36,235 in Palo Alto Networks on August 28, 2024 and sell it today you would earn a total of 2,403 from holding Palo Alto Networks or generate 6.63% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Palo Alto Networks vs. Katapult Holdings
Performance |
| Timeline |
| Palo Alto Networks |
| Katapult Holdings |
Palo Alto and Katapult Holdings Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Palo Alto and Katapult Holdings
The main advantage of trading using opposite Palo Alto and Katapult Holdings positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Palo Alto position performs unexpectedly, Katapult 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 Katapult Holdings will offset losses from the drop in Katapult Holdings' long position.| Palo Alto vs. Zscaler | Palo Alto vs. Cloudflare | Palo Alto vs. Okta Inc | Palo Alto vs. Adobe Systems Incorporated |
| Katapult Holdings vs. GigaCloud Technology Class | Katapult Holdings vs. Arqit Quantum | Katapult Holdings vs. Telos Corp | Katapult Holdings vs. Cemtrex |
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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