Correlation Between Hang Seng and First Community
Can any of the company-specific risk be diversified away by investing in both Hang Seng and First Community 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 Hang Seng and First Community into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hang Seng Bank and First Community, you can compare the effects of market volatilities on Hang Seng and First Community 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 Hang Seng with a short position of First Community. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hang Seng and First Community.
Diversification Opportunities for Hang Seng and First Community
-0.07 | Correlation Coefficient |
Good diversification
The 3 months correlation between Hang and First is -0.07. Overlapping area represents the amount of risk that can be diversified away by holding Hang Seng Bank and First Community in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on First Community and Hang Seng 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 Hang Seng Bank are associated (or correlated) with First Community. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of First Community has no effect on the direction of Hang Seng i.e., Hang Seng and First Community go up and down completely randomly.
Pair Corralation between Hang Seng and First Community
Assuming the 90 days horizon Hang Seng Bank is expected to under-perform the First Community. In addition to that, Hang Seng is 1.18 times more volatile than First Community. It trades about -0.05 of its total potential returns per unit of risk. First Community is currently generating about -0.04 per unit of volatility. If you would invest 895.00 in First Community on September 21, 2024 and sell it today you would lose (15.00) from holding First Community or give up 1.68% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 95.24% |
Values | Daily Returns |
Hang Seng Bank vs. First Community
Performance |
Timeline |
Hang Seng Bank |
First Community |
Hang Seng and First Community Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Hang Seng and First Community
The main advantage of trading using opposite Hang Seng and First Community positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hang Seng position performs unexpectedly, First Community 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 First Community will offset losses from the drop in First Community's long position.Hang Seng vs. Caixabank SA ADR | Hang Seng vs. Commercial International Bank | Hang Seng vs. PT Bank Rakyat | Hang Seng vs. Riverview Bancorp |
First Community vs. CCSB Financial Corp | First Community vs. Delhi Bank Corp | First Community vs. BEO Bancorp | First Community vs. First Community Financial |
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 My Watchlist Analysis module to analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like.
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