Correlation Between Daewoo SBI and LG Household
Can any of the company-specific risk be diversified away by investing in both Daewoo SBI and LG Household 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 Daewoo SBI and LG Household into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Daewoo SBI SPAC and LG Household Healthcare, you can compare the effects of market volatilities on Daewoo SBI and LG Household 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 Daewoo SBI with a short position of LG Household. Check out your portfolio center. Please also check ongoing floating volatility patterns of Daewoo SBI and LG Household.
Diversification Opportunities for Daewoo SBI and LG Household
0.28 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Daewoo and 051900 is 0.28. Overlapping area represents the amount of risk that can be diversified away by holding Daewoo SBI SPAC and LG Household Healthcare in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on LG Household Healthcare and Daewoo SBI 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 Daewoo SBI SPAC are associated (or correlated) with LG Household. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of LG Household Healthcare has no effect on the direction of Daewoo SBI i.e., Daewoo SBI and LG Household go up and down completely randomly.
Pair Corralation between Daewoo SBI and LG Household
Assuming the 90 days trading horizon Daewoo SBI SPAC is expected to generate 0.81 times more return on investment than LG Household. However, Daewoo SBI SPAC is 1.24 times less risky than LG Household. It trades about -0.03 of its potential returns per unit of risk. LG Household Healthcare is currently generating about -0.06 per unit of risk. If you would invest 438,500 in Daewoo SBI SPAC on October 5, 2024 and sell it today you would lose (149,000) from holding Daewoo SBI SPAC or give up 33.98% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
Daewoo SBI SPAC vs. LG Household Healthcare
Performance |
Timeline |
Daewoo SBI SPAC |
LG Household Healthcare |
Daewoo SBI and LG Household Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with Daewoo SBI and LG Household
The main advantage of trading using opposite Daewoo SBI and LG Household positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Daewoo SBI position performs unexpectedly, LG Household 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 Household will offset losses from the drop in LG Household's long position.Daewoo SBI vs. Daou Data Corp | Daewoo SBI vs. Korean Reinsurance Co | Daewoo SBI vs. Hannong Chemicals | Daewoo SBI vs. Ssangyong Information Communication |
LG Household vs. KB Financial Group | LG Household vs. Shinhan Financial Group | LG Household vs. Hana Financial | LG Household vs. Woori Financial Group |
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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