Correlation Between ITM Semiconductor and Haesung DS
Can any of the company-specific risk be diversified away by investing in both ITM Semiconductor and Haesung DS 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 ITM Semiconductor and Haesung DS into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between ITM Semiconductor Co and Haesung DS Co, you can compare the effects of market volatilities on ITM Semiconductor and Haesung DS 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 ITM Semiconductor with a short position of Haesung DS. Check out your portfolio center. Please also check ongoing floating volatility patterns of ITM Semiconductor and Haesung DS.
Diversification Opportunities for ITM Semiconductor and Haesung DS
0.76 | Correlation Coefficient |
Poor diversification
The 3 months correlation between ITM and Haesung is 0.76. Overlapping area represents the amount of risk that can be diversified away by holding ITM Semiconductor Co and Haesung DS Co in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Haesung DS and ITM Semiconductor 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 ITM Semiconductor Co are associated (or correlated) with Haesung DS. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Haesung DS has no effect on the direction of ITM Semiconductor i.e., ITM Semiconductor and Haesung DS go up and down completely randomly.
Pair Corralation between ITM Semiconductor and Haesung DS
Assuming the 90 days trading horizon ITM Semiconductor Co is expected to generate 0.7 times more return on investment than Haesung DS. However, ITM Semiconductor Co is 1.42 times less risky than Haesung DS. It trades about -0.33 of its potential returns per unit of risk. Haesung DS Co is currently generating about -0.24 per unit of risk. If you would invest 2,015,000 in ITM Semiconductor Co on September 4, 2024 and sell it today you would lose (546,000) from holding ITM Semiconductor Co or give up 27.1% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
ITM Semiconductor Co vs. Haesung DS Co
Performance |
Timeline |
ITM Semiconductor |
Haesung DS |
ITM Semiconductor and Haesung DS Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with ITM Semiconductor and Haesung DS
The main advantage of trading using opposite ITM Semiconductor and Haesung DS positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ITM Semiconductor position performs unexpectedly, Haesung DS 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 Haesung DS will offset losses from the drop in Haesung DS's long position.ITM Semiconductor vs. SS TECH | ITM Semiconductor vs. LEENO Industrial | ITM Semiconductor vs. Wonik Ips Co | ITM Semiconductor vs. SFA Semicon Co |
Haesung DS vs. Youl Chon Chemical | Haesung DS vs. Taegu Broadcasting | Haesung DS vs. Hyosung Chemical Corp | Haesung DS vs. Kukdong Oil Chemicals |
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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