Correlation Between SentinelOne and Tenaga Nasional
Can any of the company-specific risk be diversified away by investing in both SentinelOne and Tenaga Nasional 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 Tenaga Nasional into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SentinelOne and Tenaga Nasional Bhd, you can compare the effects of market volatilities on SentinelOne and Tenaga Nasional 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 Tenaga Nasional. Check out your portfolio center. Please also check ongoing floating volatility patterns of SentinelOne and Tenaga Nasional.
Diversification Opportunities for SentinelOne and Tenaga Nasional
-0.3 | Correlation Coefficient |
Very good diversification
The 3 months correlation between SentinelOne and Tenaga is -0.3. Overlapping area represents the amount of risk that can be diversified away by holding SentinelOne and Tenaga Nasional Bhd in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Tenaga Nasional Bhd 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 Tenaga Nasional. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Tenaga Nasional Bhd has no effect on the direction of SentinelOne i.e., SentinelOne and Tenaga Nasional go up and down completely randomly.
Pair Corralation between SentinelOne and Tenaga Nasional
Taking into account the 90-day investment horizon SentinelOne is expected to generate 3.19 times more return on investment than Tenaga Nasional. However, SentinelOne is 3.19 times more volatile than Tenaga Nasional Bhd. It trades about 0.07 of its potential returns per unit of risk. Tenaga Nasional Bhd is currently generating about 0.13 per unit of risk. If you would invest 1,492 in SentinelOne on August 31, 2024 and sell it today you would earn a total of 1,303 from holding SentinelOne or generate 87.33% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 98.13% |
| Values | Daily Returns |
SentinelOne vs. Tenaga Nasional Bhd
Performance |
| Timeline |
| SentinelOne |
| Tenaga Nasional Bhd |
SentinelOne and Tenaga Nasional Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with SentinelOne and Tenaga Nasional
The main advantage of trading using opposite SentinelOne and Tenaga Nasional positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SentinelOne position performs unexpectedly, Tenaga Nasional 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 Tenaga Nasional will offset losses from the drop in Tenaga Nasional's long position.| SentinelOne vs. Crowdstrike Holdings | SentinelOne vs. Okta Inc | SentinelOne vs. Cloudflare | SentinelOne vs. MongoDB |
| Tenaga Nasional vs. Sports Toto Berhad | Tenaga Nasional vs. Lotte Chemical Titan | Tenaga Nasional vs. Mercury Industries Bhd | Tenaga Nasional vs. Media Prima Bhd |
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 Price Transformation module to use Price Transformation models to analyze the depth of different equity instruments across global markets.
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