Correlation Between ULTRA CLEAN and Carnegie Clean
Can any of the company-specific risk be diversified away by investing in both ULTRA CLEAN and Carnegie Clean 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 ULTRA CLEAN and Carnegie Clean into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between ULTRA CLEAN HLDGS and Carnegie Clean Energy, you can compare the effects of market volatilities on ULTRA CLEAN and Carnegie Clean 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 ULTRA CLEAN with a short position of Carnegie Clean. Check out your portfolio center. Please also check ongoing floating volatility patterns of ULTRA CLEAN and Carnegie Clean.
Diversification Opportunities for ULTRA CLEAN and Carnegie Clean
0.3 | Correlation Coefficient |
Weak diversification
The 3 months correlation between ULTRA and Carnegie is 0.3. Overlapping area represents the amount of risk that can be diversified away by holding ULTRA CLEAN HLDGS and Carnegie Clean Energy in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Carnegie Clean Energy and ULTRA CLEAN 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 ULTRA CLEAN HLDGS are associated (or correlated) with Carnegie Clean. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Carnegie Clean Energy has no effect on the direction of ULTRA CLEAN i.e., ULTRA CLEAN and Carnegie Clean go up and down completely randomly.
Pair Corralation between ULTRA CLEAN and Carnegie Clean
Assuming the 90 days trading horizon ULTRA CLEAN HLDGS is expected to under-perform the Carnegie Clean. But the stock apears to be less risky and, when comparing its historical volatility, ULTRA CLEAN HLDGS is 1.1 times less risky than Carnegie Clean. The stock trades about -0.17 of its potential returns per unit of risk. The Carnegie Clean Energy is currently generating about -0.02 of returns per unit of risk over similar time horizon. If you would invest 2.22 in Carnegie Clean Energy on December 5, 2024 and sell it today you would lose (0.22) from holding Carnegie Clean Energy or give up 9.91% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Very Weak |
Accuracy | 100.0% |
Values | Daily Returns |
ULTRA CLEAN HLDGS vs. Carnegie Clean Energy
Performance |
Timeline |
ULTRA CLEAN HLDGS |
Carnegie Clean Energy |
ULTRA CLEAN and Carnegie Clean Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with ULTRA CLEAN and Carnegie Clean
The main advantage of trading using opposite ULTRA CLEAN and Carnegie Clean positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if ULTRA CLEAN position performs unexpectedly, Carnegie Clean 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 Carnegie Clean will offset losses from the drop in Carnegie Clean's long position.ULTRA CLEAN vs. OPKO HEALTH | ULTRA CLEAN vs. Fevertree Drinks PLC | ULTRA CLEAN vs. BG Foods | ULTRA CLEAN vs. NIGHTINGALE HEALTH EO |
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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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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