Correlation Between Hua Hong and TotalEnergies
Can any of the company-specific risk be diversified away by investing in both Hua Hong and TotalEnergies 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 Hua Hong and TotalEnergies into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hua Hong Semiconductor and TotalEnergies SE, you can compare the effects of market volatilities on Hua Hong and TotalEnergies 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 Hua Hong with a short position of TotalEnergies. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hua Hong and TotalEnergies.
Diversification Opportunities for Hua Hong and TotalEnergies
0.46 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Hua and TotalEnergies is 0.46. Overlapping area represents the amount of risk that can be diversified away by holding Hua Hong Semiconductor and TotalEnergies SE in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on TotalEnergies SE and Hua Hong 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 Hua Hong Semiconductor are associated (or correlated) with TotalEnergies. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of TotalEnergies SE has no effect on the direction of Hua Hong i.e., Hua Hong and TotalEnergies go up and down completely randomly.
Pair Corralation between Hua Hong and TotalEnergies
Assuming the 90 days horizon Hua Hong Semiconductor is expected to generate 3.15 times more return on investment than TotalEnergies. However, Hua Hong is 3.15 times more volatile than TotalEnergies SE. It trades about 0.16 of its potential returns per unit of risk. TotalEnergies SE is currently generating about 0.46 per unit of risk. If you would invest 272.00 in Hua Hong Semiconductor on October 25, 2024 and sell it today you would earn a total of 24.00 from holding Hua Hong Semiconductor or generate 8.82% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Hua Hong Semiconductor vs. TotalEnergies SE
Performance |
Timeline |
Hua Hong Semiconductor |
TotalEnergies SE |
Hua Hong and TotalEnergies Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hua Hong and TotalEnergies
The main advantage of trading using opposite Hua Hong and TotalEnergies positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hua Hong position performs unexpectedly, TotalEnergies 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 TotalEnergies will offset losses from the drop in TotalEnergies' long position.Hua Hong vs. Discover Financial Services | Hua Hong vs. COPLAND ROAD CAPITAL | Hua Hong vs. BANK OF CHINA | Hua Hong vs. Chiba Bank |
TotalEnergies vs. Retail Estates NV | TotalEnergies vs. Australian Agricultural | TotalEnergies vs. Hanison Construction Holdings | TotalEnergies vs. FARM 51 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 Portfolio Suggestion module to get suggestions outside of your existing asset allocation including your own model portfolios.
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