Correlation Between Hansol Chemica and Lotte Non-Life
Can any of the company-specific risk be diversified away by investing in both Hansol Chemica and Lotte Non-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 Hansol Chemica and Lotte Non-Life into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Hansol Chemica and Lotte Non Life, you can compare the effects of market volatilities on Hansol Chemica and Lotte Non-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 Hansol Chemica with a short position of Lotte Non-Life. Check out your portfolio center. Please also check ongoing floating volatility patterns of Hansol Chemica and Lotte Non-Life.
Diversification Opportunities for Hansol Chemica and Lotte Non-Life
-0.3 | Correlation Coefficient |
Very good diversification
The 3 months correlation between Hansol and Lotte is -0.3. Overlapping area represents the amount of risk that can be diversified away by holding Hansol Chemica and Lotte Non Life in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Lotte Non Life and Hansol Chemica 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 Hansol Chemica are associated (or correlated) with Lotte Non-Life. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Lotte Non Life has no effect on the direction of Hansol Chemica i.e., Hansol Chemica and Lotte Non-Life go up and down completely randomly.
Pair Corralation between Hansol Chemica and Lotte Non-Life
Assuming the 90 days trading horizon Hansol Chemica is expected to generate 1.32 times more return on investment than Lotte Non-Life. However, Hansol Chemica is 1.32 times more volatile than Lotte Non Life. It trades about 0.49 of its potential returns per unit of risk. Lotte Non Life is currently generating about -0.14 per unit of risk. If you would invest 9,270,000 in Hansol Chemica on November 27, 2024 and sell it today you would earn a total of 2,340,000 from holding Hansol Chemica or generate 25.24% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Insignificant |
| Accuracy | 100.0% |
| Values | Daily Returns |
Hansol Chemica vs. Lotte Non Life
Performance |
| Timeline |
| Hansol Chemica |
| Lotte Non Life |
Hansol Chemica and Lotte Non-Life Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Hansol Chemica and Lotte Non-Life
The main advantage of trading using opposite Hansol Chemica and Lotte Non-Life positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Hansol Chemica position performs unexpectedly, Lotte Non-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 Lotte Non-Life will offset losses from the drop in Lotte Non-Life's long position.| Hansol Chemica vs. Visang Education | Hansol Chemica vs. SK Chemicals Co | Hansol Chemica vs. BNK Financial Group | Hansol Chemica vs. Settlebank |
| Lotte Non-Life vs. DC Media Co | Lotte Non-Life vs. Nasmedia Co | Lotte Non-Life vs. ChipsMedia | Lotte Non-Life vs. JYP Entertainment Corp |
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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