Correlation Between SentinelOne and Procter Gamble
Can any of the company-specific risk be diversified away by investing in both SentinelOne and Procter Gamble 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 SentinelOne and Procter Gamble into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SentinelOne and Procter Gamble, you can compare the effects of market volatilities on SentinelOne and Procter Gamble 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 SentinelOne with a short position of Procter Gamble. Check out your portfolio center. Please also check ongoing floating volatility patterns of SentinelOne and Procter Gamble.
Diversification Opportunities for SentinelOne and Procter Gamble
-0.13 | Correlation Coefficient |
Good diversification
The 3 months correlation between SentinelOne and Procter is -0.13. Overlapping area represents the amount of risk that can be diversified away by holding SentinelOne and Procter Gamble in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Procter Gamble and SentinelOne 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 SentinelOne are associated (or correlated) with Procter Gamble. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Procter Gamble has no effect on the direction of SentinelOne i.e., SentinelOne and Procter Gamble go up and down completely randomly.
Pair Corralation between SentinelOne and Procter Gamble
Taking into account the 90-day investment horizon SentinelOne is expected to under-perform the Procter Gamble. In addition to that, SentinelOne is 2.59 times more volatile than Procter Gamble. It trades about -0.05 of its total potential returns per unit of risk. Procter Gamble is currently generating about 0.1 per unit of volatility. If you would invest 15,952 in Procter Gamble on January 26, 2024 and sell it today you would earn a total of 308.00 from holding Procter Gamble or generate 1.93% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
SentinelOne vs. Procter Gamble
Performance |
Timeline |
SentinelOne |
Procter Gamble |
SentinelOne and Procter Gamble Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SentinelOne and Procter Gamble
The main advantage of trading using opposite SentinelOne and Procter Gamble positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SentinelOne position performs unexpectedly, Procter Gamble 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 Procter Gamble will offset losses from the drop in Procter Gamble's long position.SentinelOne vs. Crowdstrike Holdings | SentinelOne vs. Block Inc | SentinelOne vs. Adobe Systems Incorporated | SentinelOne vs. Cloudflare |
Procter Gamble vs. Unilever PLC ADR | Procter Gamble vs. Kimberly Clark | Procter Gamble vs. Estee Lauder Companies | Procter Gamble vs. Reckitt Benckiser Group |
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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