Correlation Between Haesung DS and Daishin Balance
Can any of the company-specific risk be diversified away by investing in both Haesung DS and Daishin Balance 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 Haesung DS and Daishin Balance into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Haesung DS Co and Daishin Balance 1, you can compare the effects of market volatilities on Haesung DS and Daishin Balance 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 Haesung DS with a short position of Daishin Balance. Check out your portfolio center. Please also check ongoing floating volatility patterns of Haesung DS and Daishin Balance.
Diversification Opportunities for Haesung DS and Daishin Balance
0.53 | Correlation Coefficient |
Very weak diversification
The 3 months correlation between Haesung and Daishin is 0.53. Overlapping area represents the amount of risk that can be diversified away by holding Haesung DS Co and Daishin Balance 1 in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Daishin Balance 1 and Haesung DS 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 Haesung DS Co are associated (or correlated) with Daishin Balance. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Daishin Balance 1 has no effect on the direction of Haesung DS i.e., Haesung DS and Daishin Balance go up and down completely randomly.
Pair Corralation between Haesung DS and Daishin Balance
Assuming the 90 days trading horizon Haesung DS Co is expected to generate 0.96 times more return on investment than Daishin Balance. However, Haesung DS Co is 1.04 times less risky than Daishin Balance. It trades about -0.02 of its potential returns per unit of risk. Daishin Balance 1 is currently generating about -0.04 per unit of risk. If you would invest 3,813,338 in Haesung DS Co on September 1, 2024 and sell it today you would lose (1,648,338) from holding Haesung DS Co or give up 43.23% of portfolio value over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Together |
| Strength | Weak |
| Accuracy | 99.79% |
| Values | Daily Returns |
Haesung DS Co vs. Daishin Balance 1
Performance |
| Timeline |
| Haesung DS |
| Daishin Balance 1 |
Haesung DS and Daishin Balance Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Haesung DS and Daishin Balance
The main advantage of trading using opposite Haesung DS and Daishin Balance positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Haesung DS position performs unexpectedly, Daishin Balance 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 Daishin Balance will offset losses from the drop in Daishin Balance's long position.| Haesung DS vs. Dongsin Engineering Construction | Haesung DS vs. Doosan Fuel Cell | Haesung DS vs. Daishin Balance 1 | Haesung DS vs. Total Soft Bank |
| Daishin Balance vs. INNOX Advanced Materials | Daishin Balance vs. LAKE MATERIALS LTD | Daishin Balance vs. Iljin Materials Co | Daishin Balance vs. TOPMATERIAL LTD |
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 Backtesting module to avoid under-diversification and over-optimization by backtesting your portfolios.
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