Correlation Between Koryo Credit and LG Energy
Can any of the company-specific risk be diversified away by investing in both Koryo Credit and LG Energy 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 LG Energy 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 LG Energy Solution, you can compare the effects of market volatilities on Koryo Credit and LG Energy 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 LG Energy. Check out your portfolio center. Please also check ongoing floating volatility patterns of Koryo Credit and LG Energy.
Diversification Opportunities for Koryo Credit and LG Energy
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between Koryo and 373220 is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding Koryo Credit Information and LG Energy Solution in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on LG Energy Solution 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 LG Energy. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of LG Energy Solution has no effect on the direction of Koryo Credit i.e., Koryo Credit and LG Energy go up and down completely randomly.
Pair Corralation between Koryo Credit and LG Energy
Assuming the 90 days trading horizon Koryo Credit Information is expected to generate 0.5 times more return on investment than LG Energy. However, Koryo Credit Information is 2.01 times less risky than LG Energy. It trades about 0.0 of its potential returns per unit of risk. LG Energy Solution is currently generating about -0.02 per unit of risk. If you would invest 989,298 in Koryo Credit Information on December 8, 2024 and sell it today you would lose (17,298) from holding Koryo Credit Information or give up 1.75% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Koryo Credit Information vs. LG Energy Solution
Performance |
| Timeline |
| Koryo Credit Information |
| LG Energy Solution |
Koryo Credit and LG Energy Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Koryo Credit and LG Energy
The main advantage of trading using opposite Koryo Credit and LG Energy positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Koryo Credit position performs unexpectedly, LG Energy 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 LG Energy will offset losses from the drop in LG Energy's long position.| Koryo Credit vs. CU Medical Systems | Koryo Credit vs. Seohee Construction Co | Koryo Credit vs. Dongwoo Farm To | Koryo Credit vs. Tuksu Engineering ConstructionLtd |
| LG Energy vs. LG Household Healthcare | LG Energy vs. Kukil Metal Co | LG Energy vs. DONGKUK TED METAL | LG Energy vs. PJ Metal Co |
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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