Correlation Between Lotte Data and Samsung Life
Can any of the company-specific risk be diversified away by investing in both Lotte Data and Samsung Life 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 Lotte Data and Samsung Life into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Lotte Data Communication and Samsung Life, you can compare the effects of market volatilities on Lotte Data and Samsung Life 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 Lotte Data with a short position of Samsung Life. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lotte Data and Samsung Life.
Diversification Opportunities for Lotte Data and Samsung Life
0.25 | Correlation Coefficient |
Modest diversification
The 3 months correlation between Lotte and Samsung is 0.25. Overlapping area represents the amount of risk that can be diversified away by holding Lotte Data Communication and Samsung Life in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Samsung Life and Lotte Data 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 Lotte Data Communication are associated (or correlated) with Samsung Life. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Samsung Life has no effect on the direction of Lotte Data i.e., Lotte Data and Samsung Life go up and down completely randomly.
Pair Corralation between Lotte Data and Samsung Life
Assuming the 90 days trading horizon Lotte Data Communication is expected to under-perform the Samsung Life. In addition to that, Lotte Data is 1.16 times more volatile than Samsung Life. It trades about -0.02 of its total potential returns per unit of risk. Samsung Life is currently generating about 0.03 per unit of volatility. If you would invest 8,400,000 in Samsung Life on November 3, 2024 and sell it today you would earn a total of 510,000 from holding Samsung Life or generate 6.07% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Lotte Data Communication vs. Samsung Life
Performance |
| Timeline |
| Lotte Data Communication |
| Samsung Life |
Lotte Data and Samsung Life Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Lotte Data and Samsung Life
The main advantage of trading using opposite Lotte Data and Samsung Life positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lotte Data position performs unexpectedly, Samsung Life 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 Samsung Life will offset losses from the drop in Samsung Life's long position.| Lotte Data vs. Daishin Information Communications | Lotte Data vs. Nable Communications | Lotte Data vs. Korea Information Communications | Lotte Data vs. Coloray International Investment |
| Samsung Life vs. Sempio Foods Co | Samsung Life vs. Atinum Investment Co | Samsung Life vs. Woori Technology Investment | Samsung Life vs. Samyang Foods 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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