Correlation Between Korea Computer and FNC Entertainment
Can any of the company-specific risk be diversified away by investing in both Korea Computer and FNC Entertainment 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 Korea Computer and FNC Entertainment into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Korea Computer and FNC Entertainment Co, you can compare the effects of market volatilities on Korea Computer and FNC Entertainment 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 Korea Computer with a short position of FNC Entertainment. Check out your portfolio center. Please also check ongoing floating volatility patterns of Korea Computer and FNC Entertainment.
Diversification Opportunities for Korea Computer and FNC Entertainment
-0.44 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Korea and FNC is -0.44. Overlapping area represents the amount of risk that can be diversified away by holding Korea Computer and FNC Entertainment Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on FNC Entertainment and Korea Computer 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 Korea Computer are associated (or correlated) with FNC Entertainment. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of FNC Entertainment has no effect on the direction of Korea Computer i.e., Korea Computer and FNC Entertainment go up and down completely randomly.
Pair Corralation between Korea Computer and FNC Entertainment
Assuming the 90 days trading horizon Korea Computer is expected to generate 1.05 times more return on investment than FNC Entertainment. However, Korea Computer is 1.05 times more volatile than FNC Entertainment Co. It trades about -0.07 of its potential returns per unit of risk. FNC Entertainment Co is currently generating about -0.14 per unit of risk. If you would invest 532,000 in Korea Computer on August 31, 2024 and sell it today you would lose (31,000) from holding Korea Computer or give up 5.83% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Korea Computer vs. FNC Entertainment Co
Performance |
Timeline |
Korea Computer |
FNC Entertainment |
Korea Computer and FNC Entertainment Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Korea Computer and FNC Entertainment
The main advantage of trading using opposite Korea Computer and FNC Entertainment positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Korea Computer position performs unexpectedly, FNC Entertainment 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 FNC Entertainment will offset losses from the drop in FNC Entertainment's long position.Korea Computer vs. Dongsin Engineering Construction | Korea Computer vs. Doosan Fuel Cell | Korea Computer vs. Daishin Balance 1 | Korea Computer vs. Total Soft Bank |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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