Correlation Between Lotte Data and Dow Jones
Can any of the company-specific risk be diversified away by investing in both Lotte Data and Dow Jones 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 Dow Jones 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 Dow Jones Industrial, you can compare the effects of market volatilities on Lotte Data and Dow Jones 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 Dow Jones. Check out your portfolio center. Please also check ongoing floating volatility patterns of Lotte Data and Dow Jones.
Diversification Opportunities for Lotte Data and Dow Jones
-0.79 | Correlation Coefficient |
Pay attention - limited upside
The 3 months correlation between Lotte and Dow is -0.79. Overlapping area represents the amount of risk that can be diversified away by holding Lotte Data Communication and Dow Jones Industrial in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Dow Jones 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 Dow Jones. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Dow Jones Industrial has no effect on the direction of Lotte Data i.e., Lotte Data and Dow Jones go up and down completely randomly.
Pair Corralation between Lotte Data and Dow Jones
Assuming the 90 days trading horizon Lotte Data Communication is expected to under-perform the Dow Jones. In addition to that, Lotte Data is 2.64 times more volatile than Dow Jones Industrial. It trades about -0.19 of its total potential returns per unit of risk. Dow Jones Industrial is currently generating about 0.27 per unit of volatility. If you would invest 4,238,757 in Dow Jones Industrial on August 29, 2024 and sell it today you would earn a total of 247,274 from holding Dow Jones Industrial or generate 5.83% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Lotte Data Communication vs. Dow Jones Industrial
Performance |
| Timeline |
Lotte Data and Dow Jones Volatility Contrast
Predicted Return Density |
| Returns |
Lotte Data Communication
Pair trading matchups for Lotte Data
Dow Jones Industrial
Pair trading matchups for Dow Jones
Pair Trading with Lotte Data and Dow Jones
The main advantage of trading using opposite Lotte Data and Dow Jones positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Lotte Data position performs unexpectedly, Dow Jones 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 Dow Jones will offset losses from the drop in Dow Jones' long position.| Lotte Data vs. SK Holdings Co | Lotte Data vs. Daou Tech | Lotte Data vs. KT Corporation | Lotte Data vs. Samsung Heavy Industries |
| Dow Jones vs. CECO Environmental Corp | Dow Jones vs. Western Acquisition Ventures | Dow Jones vs. Tyson Foods | Dow Jones vs. Inflection Point Acquisition |
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 Insider Screener module to find insiders across different sectors to evaluate their impact on performance.
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