Correlation Between Lotte Data and Myoung Shin
Can any of the company-specific risk be diversified away by investing in both Lotte Data and Myoung Shin 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 Myoung Shin 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 Myoung Shin Industrial, you can compare the effects of market volatilities on Lotte Data and Myoung Shin 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 Myoung Shin. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lotte Data and Myoung Shin.
Diversification Opportunities for Lotte Data and Myoung Shin
0.74 | Correlation Coefficient |
Poor diversification
The 3 months correlation between Lotte and Myoung is 0.74. Overlapping area represents the amount of risk that can be diversified away by holding Lotte Data Communication and Myoung Shin Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Myoung Shin Industrial 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 Myoung Shin. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Myoung Shin Industrial has no effect on the direction of Lotte Data i.e., Lotte Data and Myoung Shin go up and down completely randomly.
Pair Corralation between Lotte Data and Myoung Shin
Assuming the 90 days trading horizon Lotte Data Communication is expected to under-perform the Myoung Shin. In addition to that, Lotte Data is 1.03 times more volatile than Myoung Shin Industrial. It trades about -0.09 of its total potential returns per unit of risk. Myoung Shin Industrial is currently generating about -0.04 per unit of volatility. If you would invest 1,538,096 in Myoung Shin Industrial on November 1, 2024 and sell it today you would lose (426,096) from holding Myoung Shin Industrial or give up 27.7% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Significant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Lotte Data Communication vs. Myoung Shin Industrial
Performance |
| Timeline |
| Lotte Data Communication |
| Myoung Shin Industrial |
Lotte Data and Myoung Shin Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Lotte Data and Myoung Shin
The main advantage of trading using opposite Lotte Data and Myoung Shin positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lotte Data position performs unexpectedly, Myoung Shin 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 Myoung Shin will offset losses from the drop in Myoung Shin's long position.| Lotte Data vs. Korea Steel Co | Lotte Data vs. Wonil Special Steel | Lotte Data vs. Korean Reinsurance Co | Lotte Data vs. SeAH Besteel Corp |
| Myoung Shin vs. Nh Investment And | Myoung Shin vs. Lotte Data Communication | Myoung Shin vs. TS Investment Corp | Myoung Shin vs. NH Investment Securities |
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 Portfolio Volatility module to check portfolio volatility and analyze historical return density to properly model market risk.
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