Correlation Between Stic Investments and Daehan Steel
Can any of the company-specific risk be diversified away by investing in both Stic Investments and Daehan Steel 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 Stic Investments and Daehan Steel into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between Stic Investments and Daehan Steel, you can compare the effects of market volatilities on Stic Investments and Daehan Steel 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 Stic Investments with a short position of Daehan Steel. Check out your portfolio center. Please also check ongoing floating volatility patterns of Stic Investments and Daehan Steel.
Diversification Opportunities for Stic Investments and Daehan Steel
-0.59 | Correlation Coefficient |
Excellent diversification
The 3 months correlation between Stic and Daehan is -0.59. Overlapping area represents the amount of risk that can be diversified away by holding Stic Investments and Daehan Steel in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Daehan Steel and Stic Investments 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 Stic Investments are associated (or correlated) with Daehan Steel. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Daehan Steel has no effect on the direction of Stic Investments i.e., Stic Investments and Daehan Steel go up and down completely randomly.
Pair Corralation between Stic Investments and Daehan Steel
Assuming the 90 days trading horizon Stic Investments is expected to under-perform the Daehan Steel. In addition to that, Stic Investments is 1.26 times more volatile than Daehan Steel. It trades about -0.03 of its total potential returns per unit of risk. Daehan Steel is currently generating about 0.2 per unit of volatility. If you would invest 1,256,000 in Daehan Steel on September 2, 2024 and sell it today you would earn a total of 295,000 from holding Daehan Steel or generate 23.49% return on investment over 90 days.
| Time Period | 3 Months [change] |
| Direction | Moves Against |
| Strength | Very Weak |
| Accuracy | 100.0% |
| Values | Daily Returns |
Stic Investments vs. Daehan Steel
Performance |
| Timeline |
| Stic Investments |
| Daehan Steel |
Stic Investments and Daehan Steel Volatility Contrast
Predicted Return Density |
| Returns |
Pair Trading with Stic Investments and Daehan Steel
The main advantage of trading using opposite Stic Investments and Daehan Steel positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if Stic Investments position performs unexpectedly, Daehan Steel 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 Daehan Steel will offset losses from the drop in Daehan Steel's long position.| Stic Investments vs. Dongsin Engineering Construction | Stic Investments vs. Doosan Fuel Cell | Stic Investments vs. Daishin Balance 1 | Stic Investments vs. Total Soft Bank |
| Daehan Steel vs. LG Chemicals | Daehan Steel vs. POSCO Holdings | Daehan Steel vs. Hanwha Solutions | Daehan Steel vs. Hyundai Steel |
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 Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.
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