Correlation Between Koryo Credit and Korea Real
Can any of the company-specific risk be diversified away by investing in both Koryo Credit and Korea Real 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 Koryo Credit and Korea Real into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Koryo Credit Information and Korea Real Estate, you can compare the effects of market volatilities on Koryo Credit and Korea Real 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 Koryo Credit with a short position of Korea Real. Check out your portfolio center. Please also check ongoing floating volatility patterns of Koryo Credit and Korea Real.
Diversification Opportunities for Koryo Credit and Korea Real
0.07 | Correlation Coefficient |
Significant diversification
The 3 months correlation between Koryo and Korea is 0.07. Overlapping area represents the amount of risk that can be diversified away by holding Koryo Credit Information and Korea Real Estate in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Korea Real Estate and Koryo Credit 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 Koryo Credit Information are associated (or correlated) with Korea Real. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Korea Real Estate has no effect on the direction of Koryo Credit i.e., Koryo Credit and Korea Real go up and down completely randomly.
Pair Corralation between Koryo Credit and Korea Real
Assuming the 90 days trading horizon Koryo Credit Information is expected to generate 0.78 times more return on investment than Korea Real. However, Koryo Credit Information is 1.28 times less risky than Korea Real. It trades about 0.01 of its potential returns per unit of risk. Korea Real Estate is currently generating about -0.05 per unit of risk. If you would invest 1,003,327 in Koryo Credit Information on October 4, 2024 and sell it today you would earn a total of 673.00 from holding Koryo Credit Information or generate 0.07% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Insignificant |
Accuracy | 95.24% |
Values | Daily Returns |
Koryo Credit Information vs. Korea Real Estate
Performance |
Timeline |
Koryo Credit Information |
Korea Real Estate |
Koryo Credit and Korea Real Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Koryo Credit and Korea Real
The main advantage of trading using opposite Koryo Credit and Korea Real positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Koryo Credit position performs unexpectedly, Korea Real 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 Korea Real will offset losses from the drop in Korea Real's long position.Koryo Credit vs. AptaBio Therapeutics | Koryo Credit vs. Woori Technology Investment | Koryo Credit vs. Solution Advanced Technology | Koryo Credit vs. Busan Industrial Co |
Korea Real vs. Kukdo Chemical Co | Korea Real vs. Samhyun Steel Co | Korea Real vs. Han Kook Steel | Korea Real vs. INSUN Environmental New |
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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