Correlation Between LG Chemicals and KEPCO Engineering
Can any of the company-specific risk be diversified away by investing in both LG Chemicals and KEPCO Engineering 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 LG Chemicals and KEPCO Engineering into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between LG Chemicals and KEPCO Engineering Construction, you can compare the effects of market volatilities on LG Chemicals and KEPCO Engineering 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 LG Chemicals with a short position of KEPCO Engineering. Check out your portfolio center. Please also check ongoing floating volatility patterns of LG Chemicals and KEPCO Engineering.
Diversification Opportunities for LG Chemicals and KEPCO Engineering
-0.35 | Correlation Coefficient |
Very good diversification
The 3 months correlation between 051910 and KEPCO is -0.35. Overlapping area represents the amount of risk that can be diversified away by holding LG Chemicals and KEPCO Engineering Construction in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on KEPCO Engineering and LG Chemicals 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 LG Chemicals are associated (or correlated) with KEPCO Engineering. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of KEPCO Engineering has no effect on the direction of LG Chemicals i.e., LG Chemicals and KEPCO Engineering go up and down completely randomly.
Pair Corralation between LG Chemicals and KEPCO Engineering
Assuming the 90 days trading horizon LG Chemicals is expected to under-perform the KEPCO Engineering. In addition to that, LG Chemicals is 1.32 times more volatile than KEPCO Engineering Construction. It trades about -0.17 of its total potential returns per unit of risk. KEPCO Engineering Construction is currently generating about 0.01 per unit of volatility. If you would invest 7,160,000 in KEPCO Engineering Construction on August 28, 2024 and sell it today you would earn a total of 10,000 from holding KEPCO Engineering Construction or generate 0.14% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Insignificant |
Accuracy | 100.0% |
Values | Daily Returns |
LG Chemicals vs. KEPCO Engineering Construction
Performance |
Timeline |
LG Chemicals |
KEPCO Engineering |
LG Chemicals and KEPCO Engineering Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with LG Chemicals and KEPCO Engineering
The main advantage of trading using opposite LG Chemicals and KEPCO Engineering positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if LG Chemicals position performs unexpectedly, KEPCO Engineering 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 KEPCO Engineering will offset losses from the drop in KEPCO Engineering's long position.LG Chemicals vs. National Plastic Co | LG Chemicals vs. Korea Petro Chemical | LG Chemicals vs. Ssangyong Materials Corp | LG Chemicals vs. INNOX Advanced Materials |
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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