Correlation Between Sangsin Energy and BNK Financial
Can any of the company-specific risk be diversified away by investing in both Sangsin Energy and BNK Financial 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 Sangsin Energy and BNK Financial into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Sangsin Energy Display and BNK Financial Group, you can compare the effects of market volatilities on Sangsin Energy and BNK Financial 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 Sangsin Energy with a short position of BNK Financial. Check out your portfolio center. Please also check ongoing floating volatility patterns of Sangsin Energy and BNK Financial.
Diversification Opportunities for Sangsin Energy and BNK Financial
-0.75 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Sangsin and BNK is -0.75. Overlapping area represents the amount of risk that can be diversified away by holding Sangsin Energy Display and BNK Financial Group in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on BNK Financial Group and Sangsin Energy 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 Sangsin Energy Display are associated (or correlated) with BNK Financial. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of BNK Financial Group has no effect on the direction of Sangsin Energy i.e., Sangsin Energy and BNK Financial go up and down completely randomly.
Pair Corralation between Sangsin Energy and BNK Financial
Assuming the 90 days trading horizon Sangsin Energy Display is expected to generate 1.33 times more return on investment than BNK Financial. However, Sangsin Energy is 1.33 times more volatile than BNK Financial Group. It trades about 0.02 of its potential returns per unit of risk. BNK Financial Group is currently generating about -0.08 per unit of risk. If you would invest 740,952 in Sangsin Energy Display on October 3, 2024 and sell it today you would earn a total of 1,048 from holding Sangsin Energy Display or generate 0.14% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Sangsin Energy Display vs. BNK Financial Group
Performance |
Timeline |
Sangsin Energy Display |
BNK Financial Group |
Sangsin Energy and BNK Financial Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Sangsin Energy and BNK Financial
The main advantage of trading using opposite Sangsin Energy and BNK Financial positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Sangsin Energy position performs unexpectedly, BNK Financial 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 BNK Financial will offset losses from the drop in BNK Financial's long position.Sangsin Energy vs. AptaBio Therapeutics | Sangsin Energy vs. Woori Technology Investment | Sangsin Energy vs. Solution Advanced Technology | Sangsin Energy vs. Busan Industrial Co |
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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 Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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