Correlation Between SentinelOne and Matthews China
Can any of the company-specific risk be diversified away by investing in both SentinelOne and Matthews China 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 Matthews China into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SentinelOne and Matthews China Fund, you can compare the effects of market volatilities on SentinelOne and Matthews China 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 Matthews China. Check out your portfolio center. Please also check ongoing floating volatility patterns of SentinelOne and Matthews China.
Diversification Opportunities for SentinelOne and Matthews China
0.45 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between SentinelOne and Matthews is 0.45. Overlapping area represents the amount of risk that can be diversified away by holding SentinelOne and Matthews China Fund in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Matthews China 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 Matthews China. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Matthews China has no effect on the direction of SentinelOne i.e., SentinelOne and Matthews China go up and down completely randomly.
Pair Corralation between SentinelOne and Matthews China
Taking into account the 90-day investment horizon SentinelOne is expected to generate 1.63 times more return on investment than Matthews China. However, SentinelOne is 1.63 times more volatile than Matthews China Fund. It trades about 0.08 of its potential returns per unit of risk. Matthews China Fund is currently generating about 0.04 per unit of risk. If you would invest 1,548 in SentinelOne on September 1, 2024 and sell it today you would earn a total of 1,247 from holding SentinelOne or generate 80.56% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.63% |
| Values | Daily Returns |
SentinelOne vs. Matthews China Fund
Performance |
| Timeline |
| SentinelOne |
| Matthews China |
SentinelOne and Matthews China Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with SentinelOne and Matthews China
The main advantage of trading using opposite SentinelOne and Matthews China positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SentinelOne position performs unexpectedly, Matthews China 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 Matthews China will offset losses from the drop in Matthews China's long position.| SentinelOne vs. Palo Alto Networks | SentinelOne vs. Uipath Inc | SentinelOne vs. Block Inc | SentinelOne vs. Adobe Systems Incorporated |
| Matthews China vs. Matthews Pacific Tiger | Matthews China vs. Matthews India Fund | Matthews China vs. Matthews Asia Growth | Matthews China vs. Matthews China Dividend |
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 Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
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